Education
- Doctor of Philosophy in Industrial and Systems Engineering, National University of Singapore, 2013
- Master of Science in Industrial Engineering, De La Salle University, 2009
- Bachelor of Science in Industrial Engineering, De La Salle University, 2003
Field of Specialization
Dynamic systems
Robustness
Mathematical modelling
Systems dynamics
System modelling
Modeling and simulation
System dynamics modelling
Stability analysis
Researches
Article title: A Multi-period Optimization Model for the Design of an Off-Grid Micro Hydro Power Plant with Profitability and Degradation Considerations
Authors: Juan Carlo Hernandez, Carlos Jan Peñas, Adrianne Ressa Tiu, Charlle Sy
Publication title: Process Integration and Optimization for Sustainability 5(10):1-13, June 2021
Abstract
Micro hydroelectric power is a clean and efficient source of energy that has been used for the electrification of rural off-grid communities. However, numerous micro hydro installations have failed as caused by factors such as poor site selection and uneconomical design of materials, among others. A multi-period mixed integer linear programming model for the design of an off-grid micro hydro power plant is then developed. The proposed model is able to provide technical specifications such as the penstock dimensions, turbine choice, weir height, and site choice in order to fulfill a community’s demand while simultaneously maximizing the net present value of the investment. The model may choose among different productive end uses, with each being subject to a respective investment cost as well as a set-up time and degradation rate. Computational experiments demonstrate the different capabilities of the model to address real-life scenarios such as population growth and streamflow variability. An increase in energy consumption due to population growth leads to the requirement of a more powerful turbine. Capacity limitations likewise prevent the community to invest in productive end usage. Meanwhile, streamflow variability potentially reduces the capability of the power plant to produce electricity. In these instances, batteries had to simultaneously be used in order to augment the increase in energy requirement.
Full text link https://tinyurl.com/xr24nt8t
Article title: AI Methods for Modeling the Vacuum Drying Characteristics of Chlorococcum infusionum for Algal Biofuel Production
Authors: Phoebe Mae L. Ching, Andres Philip Mayol, Jayne Lois G. San Juan, Andre Marvin Calapatia, et al.
Publication title: Process Integration and Optimization for Sustainability 5(46):1-10
Abstract
Algal-based biofuels offer distinct advantages over other types of biofuels currently available within the fuel industry. However, one important disadvantage is that over their entire life cycle, they consume significant amounts of energy through cultivation, pretreatment, and production. Under pretreatment, drying is an energy-intensive yet highly critical process in standardizing the production of algal biofuel products. The current study proposes the use of artificial intelligence (AI) methods in optimizing the efficiency of a vacuum drying process. Previously, vacuum drying was modeled using least-squares regression methods, which captured the general linear or non-linear trend of the samples, but secured poor accuracy for individual sample points. In addition, these methods are unsuitable for online parameter optimization. Three AI-based models were developed to model the vacuum drying process, specifically an artificial neural network (ANN), a support vector machine (SVM), and an extreme gradient boosting machine (XGB). Based on error values, the ANN (RMSE = 0.0437) and XGB (RMSE = 0.0308) outperformed polynomial regression, and all models obtained meaningful lower values than multivariate linear regression (MLR). There is a notable difference in the ability of XGB to approximate individual sample points, particularly at high and low tail-ends of the dataset. Overall, the AI methods exhibited higher accuracy in estimating the drying characteristics for the chosen strain of algae. The current study may be extended to optimization by relating the control parameters to energy consumption, and automation based on the mathematical model. Graphical abstract
Full text link https://tinyurl.com/svrnpyvm
Article title: An integral linear programming formulation for post-departure air traffic flow management
Authors: Miriam Bongo and Charlle Sy
Publication title: ASEAN Engineering Journal 11(2): 101-117, March 2021
Abstract
The air transportation domain faces issues in air traffic congestion which leads to delays affecting a network of flights. As stakeholders strive to address such issue by applying air traffic flow management (ATFM) actions, there exists an apparent divide in the solution objective and perspectives. In the extant literature, solution approaches involving ATFM actions are often framed from the perspective of only one stakeholder to another. Such a scheme does not comprehensively cover the overall goal of the stakeholders, thus, provides inadequate, even conflicting, solutions. Therefore, this paper proposes an integer linear programming model for a rerouting problem that satisfactorily incorporates the individual interests of stakeholders (i.e., airport management, airline sector, air traffic management) in the commercial aviation industry and the common goal of ensuring safety in flight operations. The proposed model is designed to tactically select an alternate route when the primary route is constrained due to uncertainties such as inclement weather in a post-departure scenario. A hypothetical case study involving multiple destinations and alternate routes is carried out to illustrate the validity of the model. A Demo version of Lingo software is used to run the proposed model. Notable computational results show significant differences of selected routes as individual system interests are taken into isolation compared to when the general, collaborative model is implemented. In other words, the proposed model is able to show that preferences in alternate routes do vary with the individual interests of stakeholders, more so with the integration of the collaborative decision among stakeholders. Therefore, this research work provides a groundwork to a more comprehensive take of managing air traffic scenario involving all phases of flights. This is realized by providing a proof that significant shifts of decision solutions occur when the overall goal of stakeholders is considered rather than taking their individual interests into isolation.
Full text available upon request to the author
Article title: Systems Dynamics Modeling of Pandemic Influenza for Strategic Policy Development: a Simulation-Based Analysis of the COVID-19 Case
Authors: Charlle Sy, Phoebe Mae Ching, Jayne Lois San Juan, Ezekiel Bernardo, et al.
Publication title: Process Integration and Optimization for Sustainability, January 2021
Abstract
The novel coronavirus disease 2019 (COVID-19) is a truly wicked problem which has remained a stubborn issue plaguing multiple countries worldwide. The continuously increasing number of infections and deaths has driven several countries to implement control and response strategies including community lockdowns, physical distancing, and travel bans with different levels of success. However, a disease outbreak and the corresponding policies can cause disastrous economic consequences due to business closures and risk minimization behaviors. This paper develops a system dynamics framework of a disease outbreak system covering various policies to evaluate their effectiveness in mitigating transmission and the resulting economic burden. The system dynamics modeling approach captures the relationships, feedbacks, and delays in such a system, revealing meaningful insights on the dynamics of several response strategies.
Full text link https://tinyurl.com/4adby7nd
Article title: Fuzzy optimization of carbon management networks based on direct and indirect biomass co-firing
Authors: K.B.Aviso, C.L.Sy, R.R.Tan, A.T.Ubando
Publication title: Renewable and Sustainable Energy Reviews 132(3):110035, October 2020
Abstract
A drastic reduction in greenhouse gas emissions from electricity generation will be needed to mitigate climate change to a safe level. Residual biomass from agriculture is an underutilized energy source that can contribute to the needed emissions cut, but its geographic dispersion presents logistical problems. Direct and indirect co-firing of biomass in existing power plants presents a flexible means of utilizing this resource. Indirect co-firing of biomass with biochar co-production can even give greater reduction in greenhouse gas emissions if the biochar is applied to soil as a form of carbon sequestration. In this paper, a fuzzy linear programming model is developed for optimizing a carbon management network based on direct and indirect biomass co-firing, coupled with biochar application to soil for the latter case. The model can match biomass sources to power plants; the power plants that use indirect co-firing are also matched to biochar application sites. The model is illustrated using a case study representative of a developing country with an agriculture-intensive economy. Results show that not all powerplants need to implement co-firing to reach a balance between reducing GHG emissions and the risk of introducing contaminants in soil. The model provides effective decision support for decarbonizing power generation, particularly in developing countries that still make use of coal-fired power plants and which have abundant biomass resources in the form of agricultural waste.
Full text available upon request to the author
Article title: Multi-Objective Target-Oriented Robust Optimization of Biomass Co-Firing Networks Under Quality Uncertainty
Authors: Jayne San Juan and Charlle Sy
Publication title: Journal of Sustainable Development of Energy Water and Environment Systems , October 2020
Abstract
Reductions in coal use and greenhouse gas emissions may be achieved through implementing biomass co-firing in existing coal-fired power plants with minor retrofits. Furthermore, the biomass may be sourced sustainably from agricultural wastes. Under direct co-firing, biomass is directly used as secondary fuel, while indirect co-firing separately processes the biomass reducing risks for equipment damage from unconventional feedstock. Despite the increased costs, this approach generates a biochar by-product that may be applied directly to soil for permanent carbon sequestration. However, these systems face uncertainties in biomass quality that may increase costs and environmental impacts during actual operations. This work develops a multi-objective target-oriented robust optimization model to design biomass co-firing networks integrating uncertainty in biomass properties with investment and operations planning. A case study is solved to demonstrate model capabilities. Monte Carlo simulation shows that the robust optimal network is relatively insusceptible to uncertainties compared to the deterministic solution.
Full text link https://tinyurl.com/7hutjje4
Article title: Multi-Objective Optimization of an Integrated Algal and Sludge-Based Bioenergy Park and Wastewater Treatment System
Authors: Jayne Lois San Juan, Carlo James Caligan, Maria Mikayla Garcia, Jericho Mitra, et al.
Publication title: Sustainability 12(18):7793, September 2020
Abstract
Given increasing energy demand and global warming potential, the advancements in bioenergy production have become a key factor in combating these issues. Biorefineries have been effective in converting biomass into energy and valuable products with the added benefits of treating wastewater used as a cultivation medium. Recent developments enable relationships between sewage sludge and microalgae that could lead to higher biomass and energy yields. This study proposes a multi-objective optimization model that would assist stakeholders in designing an integrated system consisting of wastewater treatment systems, an algal-based bioenergy park, and a sludge-based bioenergy park that would decide which processes to use in treating wastewater and sludge while minimizing cost and carbon emissions. The baseline run of the model showed that the three plants were utilized in treating both sludge and water for the optimal answer. Running the model with no storage prioritizes water disposal, while having storage can help produce more energy. Sensitivity analysis was performed on storage costs and demand. Results show that decreasing the demand is directly proportional to the total costs while increasing it can help reduce expected costs through storage and utilizing process capacities. Costs of storage do not cause a huge overall difference in costs and directly follow the change.
Full text link https://tinyurl.com/f57umjvk
Article title: Process integration foremerging challenges: optimal allocation ofantivirals underresource constraints
Authors: C.L.Sy, K.B.Aviso, C.D.Cayamanda, A.S.F.Chiu, et al.
Publication title: Clean Technologies and Environmental Policy 22(6), August 2020
Abstract
The global scientific community has intensified efforts to develop, test, and commercialize pharmaceutical products to deal with the COVID-19 pandemic. Trials for both antivirals and vaccines are in progress; candidates include existing repurposed drugs that were originally developed for other ailments. Once these are shown to be effective, their production will need to be ramped up rapidly to keep pace with the growing demand as the pandemic progresses. It is highly likely that the drugs will be in short supply in the interim, which leaves policymakers and medical personnel with the difficult task of determining how to allocate them. Under such conditions, mathematical models can provide valuable decision support. In particular, useful models can be derived from process integration techniques that deal with tight resource constraints. In this paper, a linear programming model is developed to determine the optimal allocation of COVID-19 drugs that minimizes patient fatalities, taking into account additional hospital capacity constraints. Two hypothetical case studies are solved to illustrate the computational capability of the model, which can generate an allocation plan with outcomes that are superior to simple ad hoc allocation.
Full text link https://tinyurl.com/jkxzkcv4
Article title: Policy Development for Pandemic Response Using System Dynamics: a Case Study on COVID-19
Authors: Charlle Sy, Ezekiel Bernardo, Angelimarie Miguel, Jayne Lois San Juan, et al.
Publication title: Process Integration and Optimization for Sustainability 4(4), July 2020
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has burdened several countries. Its high transmissibility and mortality rate have caused devastating impacts on human lives. This has led countries to implement control strategies, such as social distancing, travel bans, and community lockdowns, with varying levels of success. However, a disease outbreak can cause significant economic disruption from business closures and risk avoidance behaviors. This paper raises policy recommendations through a system dynamics modeling approach. The developed model captures relationships, feedbacks, and delays present in a disease transmission system. The dynamics of several policies are analyzed and assessed based on effectiveness in mitigating infection and the resulting economic strain.
Full text link https://tinyurl.com/2h57x9z2
Article title: A Multi-Objective Optimization Model for the Design of Biomass Co-Firing Networks Integrating Feedstock Quality Considerations
Authors: Jayne Lois G. San Juan 1, Kathleen B. Aviso 2, Raymond R. Tan and Charlle L. Sy
Publication title: Energies 12(12):2252, June 2019
Abstract
The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversion yield and equipment efficiency. These considerations are shown to be important drivers of network decisions, emphasizing the importance of managing biomass and coal blend ratios to ensure that acceptable fuel properties are obtained.
Full text link https://tinyurl.com/byedzck
Article title: Multi-Objective Optimization of an Integrated Algal and Sludge-Based Bioenergy Park and Wastewater Treatment System
Authors: Jayne Lois San Juan, Carlo James Caligan , Maria Mikayla Garcia, Jericho Mitra, et al.
Publication title: Sustainability 12(18):7793, September 2020
Abstract
Given increasing energy demand and global warming potential, the advancements in bioenergy production have become a key factor in combating these issues. Biorefineries have been effective in converting biomass into energy and valuable products with the added benefits of treating wastewater used as a cultivation medium. Recent developments enable relationships between sewage sludge and microalgae that could lead to higher biomass and energy yields. This study proposes a multi-objective optimization model that would assist stakeholders in designing an integrated system consisting of wastewater treatment systems, an algal-based bioenergy park, and a sludge-based bioenergy park that would decide which processes to use in treating wastewater and sludge while minimizing cost and carbon emissions. The baseline run of the model showed that the three plants were utilized in treating both sludge and water for the optimal answer. Running the model with no storage prioritizes water disposal, while having storage can help produce more energy. Sensitivity analysis was performed on storage costs and demand. Results show that decreasing the demand is directly proportional to the total costs while increasing it can help reduce expected costs through storage and utilizing process capacities. Costs of storage do not cause a huge overall difference in costs and directly follow the change.
Full text l;ink https://tinyurl.com/f57umjvk
Article title: Process integration for emerging challenges: optimal allocation of antivirals under resource constraints
Authors: C.L.Sy, K.B.Aviso, C.D.Cayamanda, A.S.F.Chiu, et al.
Publication title: Clean Technologies and Environmental Policy 22(6), August 2020
Abstract
The global scientific community has intensified efforts to develop, test, and commercialize pharmaceutical products to deal with the COVID-19 pandemic. Trials for both antivirals and vaccines are in progress; candidates include existing repurposed drugs that were originally developed for other ailments. Once these are shown to be effective, their production will need to be ramped up rapidly to keep pace with the growing demand as the pandemic progresses. It is highly likely that the drugs will be in short supply in the interim, which leaves policymakers and medical personnel with the difficult task of determining how to allocate them. Under such conditions, mathematical models can provide valuable decision support. In particular, useful models can be derived from process integration techniques that deal with tight resource constraints. In this paper, a linear programming model is developed to determine the optimal allocation of COVID-19 drugs that minimizes patient fatalities, taking into account additional hospital capacity constraints. Two hypothetical case studies are solved to illustrate the computational capability of the model, which can generate an allocation plan with outcomes that are superior to simple ad hoc allocation.
Full text link https://tinyurl.com/jkxzkcv4
Article title: Policy Development for Pandemic Response Using System Dynamics: a Case Study on COVID-19
Authors: Charlle Sy, Ezekiel Bernardo, Angelimarie Miguel, Jayne Lois San Juan, et al.
Publication title: Process Integration and Optimization for Sustainability 4(4), July 2020
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has burdened several countries. Its high transmissibility and mortality rate have caused devastating impacts on human lives. This has led countries to implement control strategies, such as social distancing, travel bans, and community lockdowns, with varying levels of success. However, a disease outbreak can cause significant economic disruption from business closures and risk avoidance behaviors. This paper raises policy recommendations through a system dynamics modeling approach. The developed model captures relationships, feedbacks, and delays present in a disease transmission system. The dynamics of several policies are analyzed and assessed based on effectiveness in mitigating infection and the resulting economic strain.
Full text link https://tinyurl.com/2h57x9z2
Article title: A Multi-Objective Optimization Model for the Design of Biomass Co-Firing Networks Integrating Feedstock Quality Considerations
Authors: Jayne Lois G. San Juan 1, Kathleen B. Aviso, Raymond R. Tan, and Charlle L. Sy
Publication title: Energies 12(12):2252, June 2019
Abstract
The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversion yield and equipment efficiency. These considerations are shown to be important drivers of network decisions, emphasizing the importance of managing biomass and coal blend ratios to ensure that acceptable fuel properties are obtained.
Full text link https://tinyurl.com/byedzck
Article title: A multi-period and multi-criterion optimization model integrating multiple input configurations, reuse, and disposal options for a wastewater treatment facility
Authors: Michael S.Ang, JensenDuyag, Kimberly C.Tee, Charlle L.Sy
Publication title: Journal of Cleaner Production 231(1), May 2019
Abstract
Rapid global industrial development has led to a significant increase in waste generation, including wastewater. Improper disposal of wastewater leads to the degradation of water bodies, endangering marine life and posing health hazards to the nearby communities. This study addresses the current lack of integration in the design of wastewater treatment plants and the challenge presented by the conflicting criteria of economic impact and environmental cost. A multi-period and multi-criterion non-linear programming model for a wastewater treatment plant that simultaneously considers economic and environmental tradeoffs, alternative plant configurations, disposal and reuse options is then developed. The study considers the variability of inputs in the form of water quality and quantity in order to demonstrate the natural variations presented by wastewater sources across a planning horizon. The proposed model is applied to a case study of an actual water utility company in the Philippines. It was seen that the integration of disposal and reuse options had facilitated the realization of improved economic and environmental benefits as it was able to match the effluent water quality to the best suited option. The model enabled the improvement of the treatment process of wastewater inputs by considering alternative methods of entry such as series and parallel configurations instead of just having a mixed input configuration. This significantly improved relevant metrics such as processing time and operational costs.
Full text available upon request to the author
Article title: Target-oriented robust optimization of emissions reduction measures with uncertain cost and performance
Authors: Kathleen B. Aviso, Janne Pauline S. Ngo, Charlle L. Sy, RaymondR.Tan
Publication title: Clean Technologies and Environmental Policy 21(553), January 2019
Abstract
Emissions can be reduced through the implementation of various combinations of control or prevention measures, or combinations thereof. However, the total cost and performance of such emissions reduction measures can often be difficult to predict precisely. Such uncertainties result in techno-economic risks that firms will have to deal with when implementing a project aimed at cutting emissions. In this work, an integer linear programming model is extended using the target-oriented robust optimization (TORO) framework for determining the best mix of emissions reduction measures. This framework allows optimization to be carried out with uncertain model parameters given in the form of intervals. A range of potential solutions can then be generated and subjected to Monte Carlo simulation to gauge their robustness. The decision maker can select a solution to implement based on the information regarding the expected performance, cost and robustness of the mix of emissions reduction measures. Case studies on reduction of hydrogen fluoride emissions from brick manufacturing and CO2 emissions from maritime vessels are solved to illustrate the methodology. The examples demonstrate the capability of the TORO model to identify good solutions that are able to perform well despite variations in techno-economic conditions. Graphical abstract Open image in new window
Full text link https://tinyurl.com/yu85kf52
Article title: Target-oriented robust optimization of a microgrid system investment model
Authors: Lanz Uy, Patric Uy, Jhoenson Siy, Anthony Shun Fung Chiu, Charlle Sy
Publication title: Frontiers in Energy 12(1), June 2018
Abstract
An emerging alternative solution to address energy shortage is the construction of a microgrid system. This paper develops a mixed-integer linear programming microgrid investment model considering multi-period and multi-objective investment setups. It further investigates the effects of uncertain demand by using a target-oriented robust optimization (TORO) approach. The model was validated and analyzed by subjecting it in different scenarios. As a result, it is seen that there are four factors that affect the decision of the model: cost, budget, carbon emissions, and useful life. Since the objective of the model is to maximize the net present value (NPV) of the system, the model would choose to prioritize the least cost among the different distribution energy resources (DER). The effects of load uncertainty was observed through the use of Monte Carlo simulation. As a result, the deterministic model shows a solution that might be too optimistic and might not be achievable in real life situations. Through the application of TORO, a profile of solutions is generated to serve as a guide to the investors in their decisions considering uncertain demand. The results show that pessimistic investors would have lower NPV targets since they would invest more in storage facilities, incurring more electricity stock out costs. On the contrary, an optimistic investor would tend to be aggressive in buying electricity generating equipment to meet most of the demand, however risking more storage stock out costs.
Full text available upon request to the author
Article title: Linear optimization of soil mixes in the design of vertical cut-off walls
Authors: Jonathan Dungca, Joenel Galupino, Charlle Sy, and Anthony Shun Fung Chiu
Publication title: International Journal of GEOMATE 14(44), April 2018
Abstract
In order to prevent the contamination of surrounding groundwater in a landfill, cutoff walls were recommended. Cutoff walls are walls utilized when there is a need to restrict horizontal movement of liquids. Currently, the factors in designing cutoff walls are effective permeability, and relatively inexpensive materials in containing contaminants. It was suggested to provide a mix of 96% soil and 4% bentonite in the design of cutoff walls, but bentonite is relatively expensive, thus the viability of fly ash as a replacement for bentonite was considered. Soil mixtures were proposed and rigorous laboratory tests was performed to determine the individual properties. Tests such as specific gravity tests, Atterberg limit tests (liquid limit, plastic limit and plasticity index), emax and emin test/relative density tests, particle size analyses, microscopic characterizations, elemental composition tests and permeability tests were performed to garner data, and were utilized for the model. A linear optimization model was generated to achieve the least cost with the minimum required permeability. The minimum permeability requirement for the cutoff wall was achieved by providing various mixtures for soil-bentonite-fly ash.
Full text link https://tinyurl.com/avdxxsk4
Article title: Multi-objective target oriented robust optimization for the design of an integrated biorefinery
Authors: Charlle L. Sy, Aristotle T. Ubando, Kathleen B. Aviso, Raymond R.Tan
Publication title: Journal of Cleaner Production 170, September 2017
Abstract
Integrated biorefineries offer an efficient way of producing biofuels while increasing economic potential through the production of valuable co-products. Such bioenergy systems can potentially reduce environmental footprints by reusing waste streams from other processes within the system. An integrated biorefinery utilizes multiple conversion technologies to produce an array of products, such as biofuels, biochemicals, and bioenergy, from biomass feedstocks. Methodologies such as mathematical programming models have been instrumental in the optimal synthesis and design of such complex systems. However, uncertainties in model parameters may result in economic losses or increased environmental emissions during actual operations. Uncertainties may be accounted for in the design procedure of an integrated biorefinery through target-oriented robust optimization methodology. This new approach identifies the optimal design of an integrated biorefinery by maximizing a robustness index against uncertainties. However, since practical engineering problems often involve satisfying multiple targets, a multi-objective target-oriented robust optimization approach is developed in this paper. This new approach allows the advantages of target-oriented robust optimization to be realized for multi-objective problems. A case study on the design of an integrated algal biorefinery, with both economic performance and environmental footprint considerations, is used to illustrate the methodology. Results of Monte Carlo simulation show that the robust optimal configuration of the integrated biorefinery is relatively immune to parametric uncertainties compared to the nominal optimum found using deterministic models.
Full text available upon request to the author
Article title: A Policy Development Model for Reducing Bullwhips in Hybrid Production-Distribution Systems
Authors: Charlle Lee Sy
Publication title: International Journal of Production Economics, September 2016
Abstract
The study considered a hybrid production-distribution system (PDS) in which products move both downstream and upstream. The System Dynamics (SD) modeling methodology was used to examine the effects of integrating product returns and recovery options to the traditional downstream flow in the PDS. The recovery options of remanufacture, cannibalization and refurbish were found to have the most significant effects to the resulting degree of bullwhips and inventory variances. The SD model was then used to identify effective policies that could manage inventory, production and distribution flows in the PDS. Among these policies included the coupling of complementary recovery options and centralization of demand information. It was observed that these policies could actually smoothen the flow of production, which eventually leads to significant decreases in the inventory variances and amplifications in all echelons of the hybrid PDS.
Full text available upon request to the author
Article title: Optimal planning of incentive-based quality in closed-loop supply chains
Authors: Antonio Yamzon, Veanney Ventura, Paolo Guico, Charlle Sy
Publication title: Clean Technologies and Environmental Policy 18(5)
Abstract
Electronic firms are being required to collect used products for environmental purposes. In order to meet requirements, these firms carry out collection activities and provide incentive offers to attract product returns. These product returns may then undergo recovery options such as refurbishing, remanufacturing, cannibalizing, and controlled disposal. A mixed integer nonlinear programming model for a closed-loop supply chain that includes decisions for collection activities, incentive offers, and recovery options is formulated and validated. Quantity is modeled as a function of incentive offers between the collection centers and consumers, while quality of product returns follows an arbitrary probability distribution based on the incentive level. Quality of product returns dictates the possible recovery options, which these products can undergo. The model is then subjected to scenario analysis, which identified conditions wherein rebate or discount incentives are preferred, and when low or high incentive levels are favored. High stockout costs to secondary consumers encouraged the model to perform more cash rebate activities to stimulate more product returns. Meanwhile, when both the costs of activities and stockouts are high, the model is induced to carry out discount activities as this would generate sales rather than the cash rebate which simply incentivizes the participation in the take-back program.
Full text link https://tinyurl.com/s6zhb7du
Article title: Target-oriented robust optimization of polygeneration systems under uncertainty
Authors: Charlle L. Sy, Kathleen B. Aviso, Aristotle T. Ubando, Raymond R.Tan
Publication title: Energy 116, June 2016
Abstract
Production of clean, low-carbon energy and by-products is possible through the use of highly integrated, efficient systems such as polygeneration plants. Mathematical programming methods have proven to be valuable for the optimal synthesis of such systems. However, in practice, numerical parameters used in optimization models may be subject to uncertainties. Examples include cost coefficients in volatile markets, and thermodynamic coefficients in new process technologies. In such cases, it is necessary for the uncertainties to be incorporated into the optimization procedure. This paper presents a target-oriented robust optimization (TORO) approach for the synthesis of polygeneration systems. The use of this methodology leads to the development of a mathematical model that maximizes robustness against uncertainty, subject to the achievement of system targets. Its properties allow us to preserve computational tractability and obtain solutions to realistic-sized problems. The methodology is demonstrated for the synthesis of polygeneration systems using TORO with an illustrative case study.
Full text available upon request to the author
Article title: Target-Oriented Robust Optimization of Polygeneration Systems
Authors: Kathleen B. Aviso, Charlle L. Sy, Aristotle T. Ubando, Raymond R. Tan
Publication title: Chemical Engineering Transactions 45:199-204, October 2015
Abstract
Polygeneration systems offer the possibility of efficient, low-carbon production of different product streams from a single facility. Such systems take advantage of opportunities for integrating processes to achieve effective recovery of waste energy and material streams. Mathematical programming methods have proven to be valuable for the optimal synthesis of such polygeneration systems. However, in practice, numerical parameters used in optimization models may be subject to uncertainties. Examples include cost coefficients in volatile markets, and technical or thermodynamic coefficients in new process technologies. In such cases, it is necessary for the uncertainties to be incorporated into the optimization procedure. The target-oriented robust optimization (TORO) is a new methodology that is inspired by robust optimization. The use of this methodology leads to the development of a mathematical model that maximizes robustness against uncertainty, subject to the achievement of system targets. Its properties allow us to preserve computational tractability and obtain solutions to realistic-sized problems. To this end, we propose a methodology for the synthesis of polygeneration systems using TORO. We illustrate this new approach with an industrial polygeneration case study.
Full text link https://tinyurl.com/47r9dvcr
Article title: P-graph for Optimising Industrial Symbiotic Networks
Authors: Kathleen B. Avisoa, Anthony S. F. Chiu, Krista Danielle S. Yu,. Michael Angelo B. Promentilla
Publication title: Chemical Engineering Transactions 45:1345-1350, October 2015
Abstract
Industrial symbiosis (IS) intends to reduce the consumption of resources as well as reduce the generation of waste streams through utilising by-products of other firms as raw materials of another firm. Mathematical optimisation models have been developed for identifying the optimal design of by-product exchange and utilisation to maximise the benefits of IS networks. However, these models are unable to provide alternative network structures which may have other desirable qualities such as a simpler design, but may be sub-optimal in their realisation of the objective function. Process graph (P-graph) theory is an alternative approach based on graph theory for optimising networks. It has been primarily used for the design and optimisation of process networks, but may be applied to structurally analogous systems. This work thus proposes the development of a P-graph approach for the optimization of IS networks. The methodology is demonstrated using a case study involving a combination of retrofit and grassroots design scenarios, representing existing as well as new plants within an eco-industrial park. The P-graph model is able to provide a graphical representation of the optimal IS system, as well as alternative near-optimal network designs.
Full text link https://tinyurl.com/54ctxpuv
Article title: An affine adjustable robust model for generation and transmission network planning
Authors: Tsan Sheng Ng and Charlle Sy
Publication title: International Journal of Electrical Power & Energy Systems 60:141–152, September 2014
Abstract
This work studies an electricity generation and transmission network planning problem where loads and cost parameters are uncertain. The problem is to first determine the generation and transmission capacities to install in the supply network. When the uncertainties are revealed, a flow plan is developed to minimize the total costs and to balance loads. A two-stage mixed-integer programming model is proposed to maximize the robustness of the plan in achieving a total cost budget target. The modeling approach in this study synthesizes recent developments in affine adjustable robust optimization technology and decision-making behavior under uncertainty. A novel solution approach is also proposed to achieve a safe and tractable approximation of the model. This involves the partitioning of the total cost budget target in order to transform the original problem into a small collection of mixed integer programming models that can be solved efficiently using standard mixed integer programming solvers. Numerical studies using a power generation network are performed, which demonstrate that the proposed robust planning model performs favorably compared to a stochastic programming model across different performance measures. The computational results strongly suggest the ability of the robust planning model to effectively mitigate the effect of uncertainties.
Full text available upon request to the author
Article title: Robust system design using dynamic small signal stability and linear programming for high-performance mechatronics
Authors: Charlle Lee Sy
Publication title: Australian Journal of Electrical and Electronics Engineering 11(3), January 2014
Abstract
All realistic engineering systems operate in uncertain environments, and in some cases even small levels of noise can severely compromise their performance and stability. In this paper, we propose a robust design approach to improve the control performance of dynamic systems. Our proposed approach first specifies a set of linear constraints describing dynamic performance requirements based on pole placement, and develops a robust linear programming model which is solved to yield a parameter design that achieves the performance requirements under uncertainties. The advantage of our proposed approach is its computational efficiency since it reduces the problem to solving a small sequence of linear programming problems, and the proposed optimisation model can be embedded easily in a local search framework. The effectiveness of our proposed approach is verified xvith theoretical developments and computational studies on a typical high-performance mechatronic system; the hard disc drive.
Full text available upon request to the author
Article title: A resilience optimization approach for workforce-inventory control dynamics under uncertainty
Authors: Tsan Sheng Ng & Charlle Lee Sy
Publication title: Journal of Scheduling 17(5), October 2013
Abstract
The presence of uncertainties in manufacturing systems and supply chains can cause undesirable behavior. Failure to account for these in the design phase can further impair the capability of systems to respond to changes effectively. In this work, we consider a dynamic workforce-inventory control problem wherein inventory planning, production releases, and workforce hiring decisions need to be made. The objective is to develop planning rules to achieve important requirements related to dynamic transient behavior when system parameters are imprecisely known. To this end, we propose a resilience optimization model for the problem and develop a novel local search procedure that combines the strengths of recent developments in robust optimization technology and small signal stability analysis of dynamic systems. A numerical case study of the problem demonstrates significant improvements of the proposed solution in controlling fluctuations and high variability found in the system’s inventory, work-in-process, and workforce levels. Overall, the proposed model is shown to be computationally efficient and effective in hedging against model uncertainties.
Full text available upon request to the author
Article title: Robust parameter design for system dynamics models: A formal approach based on goal-seeking behavior
Authors: Tsan Sheng Ng, Charlle Lee Sy, Loo Hay Lee
Publication title: System Dynamics Review 28(3), July 2012
Abstract
Parametric uncertainties in system dynamics models can cause undesirable behavior, and if unaccounted for in the design phase can impair the ability of the system to meet design requirements. Sensitivity analysis, while useful in the modeling process, does not by itself offer a systematic method to the problem of system design under uncertainties. In this work, we propose an optimization model‐based approach with the aim of obtaining a parametric design for system dynamics models that is robust against uncertainties. Our research synthesizes recent developments in robust optimization technology, eigenvalue analysis and the goal‐seeking behavior of decision agents. To this end, we develop a mathematical model that is computationally efficient to solve and effective in hedging against parametric uncertainties. Numerical case studies conducted using a hare and lynx model and an inventory–workforce model demonstrate significant improvements of the proposed designs in achieving system requirements under uncertainty. Copyright © 2012 System Dynamics Society.
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Article title: A System Dynamics Model of Singapore Healthcare affordability
Authors: Adam, Tsan Sheng Ng Charlle Sy
Publication title: Proceedings - Winter Simulation Conference
Abstract
In many countries, healthcare expenditure has witnessed an accelerated pace of increase over the years. This has placed a strain on both public and private sectors to effectively mitigate the surmounting pres-sures of healthcare costs, affordability and accessibility. This paper looks into these issues within Singa-pore's healthcare system. The system dynamics simulation method has been used to elucidate complexi-ties brought about by multiple interconnected subsystems and their complex relationships. Simulations have been carried out to understand how the different entities in the system influence healthcare afforda-bility. For instance, this included observing how demand for hospital services affected the various critical hospital resources and their respective costs. Four different classes of policies have then been developed and subsequently tested for their effectiveness in improving healthcare affordability.
Full text link https://tinyurl.com/tmhkvn5u
Sex: Female
Education
Doctor of Philosophy in Industrial and Systems Engineering, National University of Singapore, 2013
Master of Science in Industrial Engineering, De La Salle University, 2009
Bachelor of Science in Industrial Engineering, De La Salle University, 2003
Field of Specialization
Dynamic systems
Robustness
Mathematical modelling
Systems dynamics
System modelling
Modeling and simulation
System dynamics modelling
Stability analysis
Researches
Article title: A Multi-period Optimization Model for the Design of an Off-Grid Micro Hydro Power Plant with Profitability and Degradation Considerations
Authors: Juan Carlo Hernandez, Carlos Jan Peñas, Adrianne Ressa Tiu, Charlle Sy
Publication title: Process Integration and Optimization for Sustainability 5(10):1-13, June 2021
Abstract
Micro hydroelectric power is a clean and efficient source of energy that has been used for the electrification of rural off-grid communities. However, numerous micro hydro installations have failed as caused by factors such as poor site selection and uneconomical design of materials, among others. A multi-period mixed integer linear programming model for the design of an off-grid micro hydro power plant is then developed. The proposed model is able to provide technical specifications such as the penstock dimensions, turbine choice, weir height, and site choice in order to fulfill a community’s demand while simultaneously maximizing the net present value of the investment. The model may choose among different productive end uses, with each being subject to a respective investment cost as well as a set-up time and degradation rate. Computational experiments demonstrate the different capabilities of the model to address real-life scenarios such as population growth and streamflow variability. An increase in energy consumption due to population growth leads to the requirement of a more powerful turbine. Capacity limitations likewise prevent the community to invest in productive end usage. Meanwhile, streamflow variability potentially reduces the capability of the power plant to produce electricity. In these instances, batteries had to simultaneously be used in order to augment the increase in energy requirement.
Full text link https://tinyurl.com/xr24nt8t
Article title: AI Methods for Modeling the Vacuum Drying Characteristics of Chlorococcum infusionum for Algal Biofuel Production
Authors: Phoebe Mae L. Ching, Andres Philip Mayol, Jayne Lois G. San Juan, Andre Marvin Calapatia, et al.
Publication title: Process Integration and Optimization for Sustainability 5(46):1-10
Abstract
Algal-based biofuels offer distinct advantages over other types of biofuels currently available within the fuel industry. However, one important disadvantage is that over their entire life cycle, they consume significant amounts of energy through cultivation, pretreatment, and production. Under pretreatment, drying is an energy-intensive yet highly critical process in standardizing the production of algal biofuel products. The current study proposes the use of artificial intelligence (AI) methods in optimizing the efficiency of a vacuum drying process. Previously, vacuum drying was modeled using least-squares regression methods, which captured the general linear or non-linear trend of the samples, but secured poor accuracy for individual sample points. In addition, these methods are unsuitable for online parameter optimization. Three AI-based models were developed to model the vacuum drying process, specifically an artificial neural network (ANN), a support vector machine (SVM), and an extreme gradient boosting machine (XGB). Based on error values, the ANN (RMSE = 0.0437) and XGB (RMSE = 0.0308) outperformed polynomial regression, and all models obtained meaningful lower values than multivariate linear regression (MLR). There is a notable difference in the ability of XGB to approximate individual sample points, particularly at high and low tail-ends of the dataset. Overall, the AI methods exhibited higher accuracy in estimating the drying characteristics for the chosen strain of algae. The current study may be extended to optimization by relating the control parameters to energy consumption, and automation based on the mathematical model. Graphical abstract
Full text link https://tinyurl.com/svrnpyvm
Article title: An integral linear programming formulation for post-departure air traffic flow management
Authors: Miriam Bongo and Charlle Sy
Publication title: ASEAN Engineering Journal 11(2): 101-117, March 2021
Abstract
The air transportation domain faces issues in air traffic congestion which leads to delays affecting a network of flights. As stakeholders strive to address such issue by applying air traffic flow management (ATFM) actions, there exists an apparent divide in the solution objective and perspectives. In the extant literature, solution approaches involving ATFM actions are often framed from the perspective of only one stakeholder to another. Such a scheme does not comprehensively cover the overall goal of the stakeholders, thus, provides inadequate, even conflicting, solutions. Therefore, this paper proposes an integer linear programming model for a rerouting problem that satisfactorily incorporates the individual interests of stakeholders (i.e., airport management, airline sector, air traffic management) in the commercial aviation industry and the common goal of ensuring safety in flight operations. The proposed model is designed to tactically select an alternate route when the primary route is constrained due to uncertainties such as inclement weather in a post-departure scenario. A hypothetical case study involving multiple destinations and alternate routes is carried out to illustrate the validity of the model. A Demo version of Lingo software is used to run the proposed model. Notable computational results show significant differences of selected routes as individual system interests are taken into isolation compared to when the general, collaborative model is implemented. In other words, the proposed model is able to show that preferences in alternate routes do vary with the individual interests of stakeholders, more so with the integration of the collaborative decision among stakeholders. Therefore, this research work provides a groundwork to a more comprehensive take of managing air traffic scenario involving all phases of flights. This is realized by providing a proof that significant shifts of decision solutions occur when the overall goal of stakeholders is considered rather than taking their individual interests into isolation.
Full text available upon request to the author
Article title: Systems Dynamics Modeling of Pandemic Influenza for Strategic Policy Development: a Simulation-Based Analysis of the COVID-19 Case
Authors: Charlle Sy, Phoebe Mae Ching, Jayne Lois San Juan, Ezekiel Bernardo, et al.
Publication title: Process Integration and Optimization for Sustainability, January 2021
Abstract
The novel coronavirus disease 2019 (COVID-19) is a truly wicked problem which has remained a stubborn issue plaguing multiple countries worldwide. The continuously increasing number of infections and deaths has driven several countries to implement control and response strategies including community lockdowns, physical distancing, and travel bans with different levels of success. However, a disease outbreak and the corresponding policies can cause disastrous economic consequences due to business closures and risk minimization behaviors. This paper develops a system dynamics framework of a disease outbreak system covering various policies to evaluate their effectiveness in mitigating transmission and the resulting economic burden. The system dynamics modeling approach captures the relationships, feedbacks, and delays in such a system, revealing meaningful insights on the dynamics of several response strategies.
Full text link https://tinyurl.com/4adby7nd
Article title: Fuzzy optimization of carbon management networks based on direct and indirect biomass co-firing
Authors: K.B.Aviso, C.L.Sy, R.R.Tan, A.T.Ubando
Publication title: Renewable and Sustainable Energy Reviews 132(3):110035, October 2020
Abstract
A drastic reduction in greenhouse gas emissions from electricity generation will be needed to mitigate climate change to a safe level. Residual biomass from agriculture is an underutilized energy source that can contribute to the needed emissions cut, but its geographic dispersion presents logistical problems. Direct and indirect co-firing of biomass in existing power plants presents a flexible means of utilizing this resource. Indirect co-firing of biomass with biochar co-production can even give greater reduction in greenhouse gas emissions if the biochar is applied to soil as a form of carbon sequestration. In this paper, a fuzzy linear programming model is developed for optimizing a carbon management network based on direct and indirect biomass co-firing, coupled with biochar application to soil for the latter case. The model can match biomass sources to power plants; the power plants that use indirect co-firing are also matched to biochar application sites. The model is illustrated using a case study representative of a developing country with an agriculture-intensive economy. Results show that not all powerplants need to implement co-firing to reach a balance between reducing GHG emissions and the risk of introducing contaminants in soil. The model provides effective decision support for decarbonizing power generation, particularly in developing countries that still make use of coal-fired power plants and which have abundant biomass resources in the form of agricultural waste.
Full text available upon request to the author
Article title: Multi-Objective Target-Oriented Robust Optimization of Biomass Co-Firing Networks Under Quality Uncertainty
Authors: Jayne San Juan and Charlle Sy
Publication title: Journal of Sustainable Development of Energy Water and Environment Systems , October 2020
Abstract
Reductions in coal use and greenhouse gas emissions may be achieved through implementing biomass co-firing in existing coal-fired power plants with minor retrofits. Furthermore, the biomass may be sourced sustainably from agricultural wastes. Under direct co-firing, biomass is directly used as secondary fuel, while indirect co-firing separately processes the biomass reducing risks for equipment damage from unconventional feedstock. Despite the increased costs, this approach generates a biochar by-product that may be applied directly to soil for permanent carbon sequestration. However, these systems face uncertainties in biomass quality that may increase costs and environmental impacts during actual operations. This work develops a multi-objective target-oriented robust optimization model to design biomass co-firing networks integrating uncertainty in biomass properties with investment and operations planning. A case study is solved to demonstrate model capabilities. Monte Carlo simulation shows that the robust optimal network is relatively insusceptible to uncertainties compared to the deterministic solution.
Full text link https://tinyurl.com/7hutjje4
Article title: Multi-Objective Optimization of an Integrated Algal and Sludge-Based Bioenergy Park and Wastewater Treatment System
Authors: Jayne Lois San Juan, Carlo James Caligan, Maria Mikayla Garcia, Jericho Mitra, et al.
Publication title: Sustainability 12(18):7793, September 2020
Abstract
Given increasing energy demand and global warming potential, the advancements in bioenergy production have become a key factor in combating these issues. Biorefineries have been effective in converting biomass into energy and valuable products with the added benefits of treating wastewater used as a cultivation medium. Recent developments enable relationships between sewage sludge and microalgae that could lead to higher biomass and energy yields. This study proposes a multi-objective optimization model that would assist stakeholders in designing an integrated system consisting of wastewater treatment systems, an algal-based bioenergy park, and a sludge-based bioenergy park that would decide which processes to use in treating wastewater and sludge while minimizing cost and carbon emissions. The baseline run of the model showed that the three plants were utilized in treating both sludge and water for the optimal answer. Running the model with no storage prioritizes water disposal, while having storage can help produce more energy. Sensitivity analysis was performed on storage costs and demand. Results show that decreasing the demand is directly proportional to the total costs while increasing it can help reduce expected costs through storage and utilizing process capacities. Costs of storage do not cause a huge overall difference in costs and directly follow the change.
Full text link https://tinyurl.com/f57umjvk
Article title: Process integration foremerging challenges: optimal allocation ofantivirals underresource constraints
Authors: C.L.Sy, K.B.Aviso, C.D.Cayamanda, A.S.F.Chiu, et al.
Publication title: Clean Technologies and Environmental Policy 22(6), August 2020
Abstract
The global scientific community has intensified efforts to develop, test, and commercialize pharmaceutical products to deal with the COVID-19 pandemic. Trials for both antivirals and vaccines are in progress; candidates include existing repurposed drugs that were originally developed for other ailments. Once these are shown to be effective, their production will need to be ramped up rapidly to keep pace with the growing demand as the pandemic progresses. It is highly likely that the drugs will be in short supply in the interim, which leaves policymakers and medical personnel with the difficult task of determining how to allocate them. Under such conditions, mathematical models can provide valuable decision support. In particular, useful models can be derived from process integration techniques that deal with tight resource constraints. In this paper, a linear programming model is developed to determine the optimal allocation of COVID-19 drugs that minimizes patient fatalities, taking into account additional hospital capacity constraints. Two hypothetical case studies are solved to illustrate the computational capability of the model, which can generate an allocation plan with outcomes that are superior to simple ad hoc allocation.
Full text link https://tinyurl.com/jkxzkcv4
Article title: Policy Development for Pandemic Response Using System Dynamics: a Case Study on COVID-19
Authors: Charlle Sy, Ezekiel Bernardo, Angelimarie Miguel, Jayne Lois San Juan, et al.
Publication title: Process Integration and Optimization for Sustainability 4(4), July 2020
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has burdened several countries. Its high transmissibility and mortality rate have caused devastating impacts on human lives. This has led countries to implement control strategies, such as social distancing, travel bans, and community lockdowns, with varying levels of success. However, a disease outbreak can cause significant economic disruption from business closures and risk avoidance behaviors. This paper raises policy recommendations through a system dynamics modeling approach. The developed model captures relationships, feedbacks, and delays present in a disease transmission system. The dynamics of several policies are analyzed and assessed based on effectiveness in mitigating infection and the resulting economic strain.
Full text link https://tinyurl.com/2h57x9z2
Article title: A Multi-Objective Optimization Model for the Design of Biomass Co-Firing Networks Integrating Feedstock Quality Considerations
Authors: Jayne Lois G. San Juan 1, Kathleen B. Aviso 2, Raymond R. Tan and Charlle L. Sy
Publication title: Energies 12(12):2252, June 2019
Abstract
The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversion yield and equipment efficiency. These considerations are shown to be important drivers of network decisions, emphasizing the importance of managing biomass and coal blend ratios to ensure that acceptable fuel properties are obtained.
Full text link https://tinyurl.com/byedzck
Article title: Multi-Objective Optimization of an Integrated Algal and Sludge-Based Bioenergy Park and Wastewater Treatment System
Authors: Jayne Lois San Juan, Carlo James Caligan , Maria Mikayla Garcia, Jericho Mitra, et al.
Publication title: Sustainability 12(18):7793, September 2020
Abstract
Given increasing energy demand and global warming potential, the advancements in bioenergy production have become a key factor in combating these issues. Biorefineries have been effective in converting biomass into energy and valuable products with the added benefits of treating wastewater used as a cultivation medium. Recent developments enable relationships between sewage sludge and microalgae that could lead to higher biomass and energy yields. This study proposes a multi-objective optimization model that would assist stakeholders in designing an integrated system consisting of wastewater treatment systems, an algal-based bioenergy park, and a sludge-based bioenergy park that would decide which processes to use in treating wastewater and sludge while minimizing cost and carbon emissions. The baseline run of the model showed that the three plants were utilized in treating both sludge and water for the optimal answer. Running the model with no storage prioritizes water disposal, while having storage can help produce more energy. Sensitivity analysis was performed on storage costs and demand. Results show that decreasing the demand is directly proportional to the total costs while increasing it can help reduce expected costs through storage and utilizing process capacities. Costs of storage do not cause a huge overall difference in costs and directly follow the change.
Full text l;ink https://tinyurl.com/f57umjvk
Article title: Process integration for emerging challenges: optimal allocation of antivirals under resource constraints
Authors: C.L.Sy, K.B.Aviso, C.D.Cayamanda, A.S.F.Chiu, et al.
Publication title: Clean Technologies and Environmental Policy 22(6), August 2020
Abstract
The global scientific community has intensified efforts to develop, test, and commercialize pharmaceutical products to deal with the COVID-19 pandemic. Trials for both antivirals and vaccines are in progress; candidates include existing repurposed drugs that were originally developed for other ailments. Once these are shown to be effective, their production will need to be ramped up rapidly to keep pace with the growing demand as the pandemic progresses. It is highly likely that the drugs will be in short supply in the interim, which leaves policymakers and medical personnel with the difficult task of determining how to allocate them. Under such conditions, mathematical models can provide valuable decision support. In particular, useful models can be derived from process integration techniques that deal with tight resource constraints. In this paper, a linear programming model is developed to determine the optimal allocation of COVID-19 drugs that minimizes patient fatalities, taking into account additional hospital capacity constraints. Two hypothetical case studies are solved to illustrate the computational capability of the model, which can generate an allocation plan with outcomes that are superior to simple ad hoc allocation.
Full text link https://tinyurl.com/jkxzkcv4
Article title: Policy Development for Pandemic Response Using System Dynamics: a Case Study on COVID-19
Authors: Charlle Sy, Ezekiel Bernardo, Angelimarie Miguel, Jayne Lois San Juan, et al.
Publication title: Process Integration and Optimization for Sustainability 4(4), July 2020
Abstract
The coronavirus disease 2019 (COVID-19) outbreak has burdened several countries. Its high transmissibility and mortality rate have caused devastating impacts on human lives. This has led countries to implement control strategies, such as social distancing, travel bans, and community lockdowns, with varying levels of success. However, a disease outbreak can cause significant economic disruption from business closures and risk avoidance behaviors. This paper raises policy recommendations through a system dynamics modeling approach. The developed model captures relationships, feedbacks, and delays present in a disease transmission system. The dynamics of several policies are analyzed and assessed based on effectiveness in mitigating infection and the resulting economic strain.
Full text link https://tinyurl.com/2h57x9z2
Article title: A Multi-Objective Optimization Model for the Design of Biomass Co-Firing Networks Integrating Feedstock Quality Considerations
Authors: Jayne Lois G. San Juan 1, Kathleen B. Aviso, Raymond R. Tan, and Charlle L. Sy
Publication title: Energies 12(12):2252, June 2019
Abstract
The growth in energy demand, coupled with declining fossil fuel resources and the onset of climate change, has resulted in increased interest in renewable energy, particularly from biomass. Co-firing, which is the joint use of coal and biomass to generate electricity, is seen to be a practical immediate solution for reducing coal use and the associated emissions. However, biomass is difficult to manage because of its seasonal availability and variable quality. This study proposes a biomass co-firing supply chain optimization model that simultaneously minimizes costs and environmental emissions through goal programming. The economic costs considered include retrofitting investment costs, together with fuel, transport, and processing costs, while environmental emissions may come from transport, treatment, and combustion activities. This model incorporates the consideration of feedstock quality and its impact on storage, transportation, and pre-treatment requirements, as well as conversion yield and equipment efficiency. These considerations are shown to be important drivers of network decisions, emphasizing the importance of managing biomass and coal blend ratios to ensure that acceptable fuel properties are obtained.
Full text link https://tinyurl.com/byedzck
Article title: A multi-period and multi-criterion optimization model integrating multiple input configurations, reuse, and disposal options for a wastewater treatment facility
Authors: Michael S.Ang, JensenDuyag, Kimberly C.Tee, Charlle L.Sy
Publication title: Journal of Cleaner Production 231(1), May 2019
Abstract
Rapid global industrial development has led to a significant increase in waste generation, including wastewater. Improper disposal of wastewater leads to the degradation of water bodies, endangering marine life and posing health hazards to the nearby communities. This study addresses the current lack of integration in the design of wastewater treatment plants and the challenge presented by the conflicting criteria of economic impact and environmental cost. A multi-period and multi-criterion non-linear programming model for a wastewater treatment plant that simultaneously considers economic and environmental tradeoffs, alternative plant configurations, disposal and reuse options is then developed. The study considers the variability of inputs in the form of water quality and quantity in order to demonstrate the natural variations presented by wastewater sources across a planning horizon. The proposed model is applied to a case study of an actual water utility company in the Philippines. It was seen that the integration of disposal and reuse options had facilitated the realization of improved economic and environmental benefits as it was able to match the effluent water quality to the best suited option. The model enabled the improvement of the treatment process of wastewater inputs by considering alternative methods of entry such as series and parallel configurations instead of just having a mixed input configuration. This significantly improved relevant metrics such as processing time and operational costs.
Full text available upon request to the author
Article title: Target-oriented robust optimization of emissions reduction measures with uncertain cost and performance
Authors: Kathleen B. Aviso, Janne Pauline S. Ngo, Charlle L. Sy, RaymondR.Tan
Publication title: Clean Technologies and Environmental Policy 21(553), January 2019
Abstract
Emissions can be reduced through the implementation of various combinations of control or prevention measures, or combinations thereof. However, the total cost and performance of such emissions reduction measures can often be difficult to predict precisely. Such uncertainties result in techno-economic risks that firms will have to deal with when implementing a project aimed at cutting emissions. In this work, an integer linear programming model is extended using the target-oriented robust optimization (TORO) framework for determining the best mix of emissions reduction measures. This framework allows optimization to be carried out with uncertain model parameters given in the form of intervals. A range of potential solutions can then be generated and subjected to Monte Carlo simulation to gauge their robustness. The decision maker can select a solution to implement based on the information regarding the expected performance, cost and robustness of the mix of emissions reduction measures. Case studies on reduction of hydrogen fluoride emissions from brick manufacturing and CO2 emissions from maritime vessels are solved to illustrate the methodology. The examples demonstrate the capability of the TORO model to identify good solutions that are able to perform well despite variations in techno-economic conditions. Graphical abstract Open image in new window
Full text link https://tinyurl.com/yu85kf52
Article title: Target-oriented robust optimization of a microgrid system investment model
Authors: Lanz Uy, Patric Uy, Jhoenson Siy, Anthony Shun Fung Chiu, Charlle Sy
Publication title: Frontiers in Energy 12(1), June 2018
Abstract
An emerging alternative solution to address energy shortage is the construction of a microgrid system. This paper develops a mixed-integer linear programming microgrid investment model considering multi-period and multi-objective investment setups. It further investigates the effects of uncertain demand by using a target-oriented robust optimization (TORO) approach. The model was validated and analyzed by subjecting it in different scenarios. As a result, it is seen that there are four factors that affect the decision of the model: cost, budget, carbon emissions, and useful life. Since the objective of the model is to maximize the net present value (NPV) of the system, the model would choose to prioritize the least cost among the different distribution energy resources (DER). The effects of load uncertainty was observed through the use of Monte Carlo simulation. As a result, the deterministic model shows a solution that might be too optimistic and might not be achievable in real life situations. Through the application of TORO, a profile of solutions is generated to serve as a guide to the investors in their decisions considering uncertain demand. The results show that pessimistic investors would have lower NPV targets since they would invest more in storage facilities, incurring more electricity stock out costs. On the contrary, an optimistic investor would tend to be aggressive in buying electricity generating equipment to meet most of the demand, however risking more storage stock out costs.
Full text available upon request to the author
Article title: Linear optimization of soil mixes in the design of vertical cut-off walls
Authors: Jonathan Dungca, Joenel Galupino, Charlle Sy, and Anthony Shun Fung Chiu
Publication title: International Journal of GEOMATE 14(44), April 2018
Abstract
In order to prevent the contamination of surrounding groundwater in a landfill, cutoff walls were recommended. Cutoff walls are walls utilized when there is a need to restrict horizontal movement of liquids. Currently, the factors in designing cutoff walls are effective permeability, and relatively inexpensive materials in containing contaminants. It was suggested to provide a mix of 96% soil and 4% bentonite in the design of cutoff walls, but bentonite is relatively expensive, thus the viability of fly ash as a replacement for bentonite was considered. Soil mixtures were proposed and rigorous laboratory tests was performed to determine the individual properties. Tests such as specific gravity tests, Atterberg limit tests (liquid limit, plastic limit and plasticity index), emax and emin test/relative density tests, particle size analyses, microscopic characterizations, elemental composition tests and permeability tests were performed to garner data, and were utilized for the model. A linear optimization model was generated to achieve the least cost with the minimum required permeability. The minimum permeability requirement for the cutoff wall was achieved by providing various mixtures for soil-bentonite-fly ash.
Full text link https://tinyurl.com/avdxxsk4
Article title: Multi-objective target oriented robust optimization for the design of an integrated biorefinery
Authors: Charlle L. Sy, Aristotle T. Ubando, Kathleen B. Aviso, Raymond R.Tan
Publication title: Journal of Cleaner Production 170, September 2017
Abstract
Integrated biorefineries offer an efficient way of producing biofuels while increasing economic potential through the production of valuable co-products. Such bioenergy systems can potentially reduce environmental footprints by reusing waste streams from other processes within the system. An integrated biorefinery utilizes multiple conversion technologies to produce an array of products, such as biofuels, biochemicals, and bioenergy, from biomass feedstocks. Methodologies such as mathematical programming models have been instrumental in the optimal synthesis and design of such complex systems. However, uncertainties in model parameters may result in economic losses or increased environmental emissions during actual operations. Uncertainties may be accounted for in the design procedure of an integrated biorefinery through target-oriented robust optimization methodology. This new approach identifies the optimal design of an integrated biorefinery by maximizing a robustness index against uncertainties. However, since practical engineering problems often involve satisfying multiple targets, a multi-objective target-oriented robust optimization approach is developed in this paper. This new approach allows the advantages of target-oriented robust optimization to be realized for multi-objective problems. A case study on the design of an integrated algal biorefinery, with both economic performance and environmental footprint considerations, is used to illustrate the methodology. Results of Monte Carlo simulation show that the robust optimal configuration of the integrated biorefinery is relatively immune to parametric uncertainties compared to the nominal optimum found using deterministic models.
Full text available upon request to the author
Article title: A Policy Development Model for Reducing Bullwhips in Hybrid Production-Distribution Systems
Authors: Charlle Lee Sy
Publication title: International Journal of Production Economics, September 2016
Abstract
The study considered a hybrid production-distribution system (PDS) in which products move both downstream and upstream. The System Dynamics (SD) modeling methodology was used to examine the effects of integrating product returns and recovery options to the traditional downstream flow in the PDS. The recovery options of remanufacture, cannibalization and refurbish were found to have the most significant effects to the resulting degree of bullwhips and inventory variances. The SD model was then used to identify effective policies that could manage inventory, production and distribution flows in the PDS. Among these policies included the coupling of complementary recovery options and centralization of demand information. It was observed that these policies could actually smoothen the flow of production, which eventually leads to significant decreases in the inventory variances and amplifications in all echelons of the hybrid PDS.
Full text available upon request to the author
Article title: Optimal planning of incentive-based quality in closed-loop supply chains
Authors: Antonio Yamzon, Veanney Ventura, Paolo Guico, Charlle Sy
Publication title: Clean Technologies and Environmental Policy 18(5)
Abstract
Electronic firms are being required to collect used products for environmental purposes. In order to meet requirements, these firms carry out collection activities and provide incentive offers to attract product returns. These product returns may then undergo recovery options such as refurbishing, remanufacturing, cannibalizing, and controlled disposal. A mixed integer nonlinear programming model for a closed-loop supply chain that includes decisions for collection activities, incentive offers, and recovery options is formulated and validated. Quantity is modeled as a function of incentive offers between the collection centers and consumers, while quality of product returns follows an arbitrary probability distribution based on the incentive level. Quality of product returns dictates the possible recovery options, which these products can undergo. The model is then subjected to scenario analysis, which identified conditions wherein rebate or discount incentives are preferred, and when low or high incentive levels are favored. High stockout costs to secondary consumers encouraged the model to perform more cash rebate activities to stimulate more product returns. Meanwhile, when both the costs of activities and stockouts are high, the model is induced to carry out discount activities as this would generate sales rather than the cash rebate which simply incentivizes the participation in the take-back program.
Full text link https://tinyurl.com/s6zhb7du
Article title: Target-oriented robust optimization of polygeneration systems under uncertainty
Authors: Charlle L. Sy, Kathleen B. Aviso, Aristotle T. Ubando, Raymond R.Tan
Publication title: Energy 116, June 2016
Abstract
Production of clean, low-carbon energy and by-products is possible through the use of highly integrated, efficient systems such as polygeneration plants. Mathematical programming methods have proven to be valuable for the optimal synthesis of such systems. However, in practice, numerical parameters used in optimization models may be subject to uncertainties. Examples include cost coefficients in volatile markets, and thermodynamic coefficients in new process technologies. In such cases, it is necessary for the uncertainties to be incorporated into the optimization procedure. This paper presents a target-oriented robust optimization (TORO) approach for the synthesis of polygeneration systems. The use of this methodology leads to the development of a mathematical model that maximizes robustness against uncertainty, subject to the achievement of system targets. Its properties allow us to preserve computational tractability and obtain solutions to realistic-sized problems. The methodology is demonstrated for the synthesis of polygeneration systems using TORO with an illustrative case study.
Full text available upon request to the author
Article title: Target-Oriented Robust Optimization of Polygeneration Systems
Authors: Kathleen B. Aviso, Charlle L. Sy, Aristotle T. Ubando, Raymond R. Tan
Publication title: Chemical Engineering Transactions 45:199-204, October 2015
Abstract
Polygeneration systems offer the possibility of efficient, low-carbon production of different product streams from a single facility. Such systems take advantage of opportunities for integrating processes to achieve effective recovery of waste energy and material streams. Mathematical programming methods have proven to be valuable for the optimal synthesis of such polygeneration systems. However, in practice, numerical parameters used in optimization models may be subject to uncertainties. Examples include cost coefficients in volatile markets, and technical or thermodynamic coefficients in new process technologies. In such cases, it is necessary for the uncertainties to be incorporated into the optimization procedure. The target-oriented robust optimization (TORO) is a new methodology that is inspired by robust optimization. The use of this methodology leads to the development of a mathematical model that maximizes robustness against uncertainty, subject to the achievement of system targets. Its properties allow us to preserve computational tractability and obtain solutions to realistic-sized problems. To this end, we propose a methodology for the synthesis of polygeneration systems using TORO. We illustrate this new approach with an industrial polygeneration case study.
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Article title: P-graph for Optimising Industrial Symbiotic Networks
Authors: Kathleen B. Avisoa, Anthony S. F. Chiu, Krista Danielle S. Yu,. Michael Angelo B. Promentilla
Publication title: Chemical Engineering Transactions 45:1345-1350, October 2015
Abstract
Industrial symbiosis (IS) intends to reduce the consumption of resources as well as reduce the generation of waste streams through utilising by-products of other firms as raw materials of another firm. Mathematical optimisation models have been developed for identifying the optimal design of by-product exchange and utilisation to maximise the benefits of IS networks. However, these models are unable to provide alternative network structures which may have other desirable qualities such as a simpler design, but may be sub-optimal in their realisation of the objective function. Process graph (P-graph) theory is an alternative approach based on graph theory for optimising networks. It has been primarily used for the design and optimisation of process networks, but may be applied to structurally analogous systems. This work thus proposes the development of a P-graph approach for the optimization of IS networks. The methodology is demonstrated using a case study involving a combination of retrofit and grassroots design scenarios, representing existing as well as new plants within an eco-industrial park. The P-graph model is able to provide a graphical representation of the optimal IS system, as well as alternative near-optimal network designs.
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Article title: An affine adjustable robust model for generation and transmission network planning
Authors: Tsan Sheng Ng and Charlle Sy
Publication title: International Journal of Electrical Power & Energy Systems 60:141–152, September 2014
Abstract
This work studies an electricity generation and transmission network planning problem where loads and cost parameters are uncertain. The problem is to first determine the generation and transmission capacities to install in the supply network. When the uncertainties are revealed, a flow plan is developed to minimize the total costs and to balance loads. A two-stage mixed-integer programming model is proposed to maximize the robustness of the plan in achieving a total cost budget target. The modeling approach in this study synthesizes recent developments in affine adjustable robust optimization technology and decision-making behavior under uncertainty. A novel solution approach is also proposed to achieve a safe and tractable approximation of the model. This involves the partitioning of the total cost budget target in order to transform the original problem into a small collection of mixed integer programming models that can be solved efficiently using standard mixed integer programming solvers. Numerical studies using a power generation network are performed, which demonstrate that the proposed robust planning model performs favorably compared to a stochastic programming model across different performance measures. The computational results strongly suggest the ability of the robust planning model to effectively mitigate the effect of uncertainties.
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Article title: Robust system design using dynamic small signal stability and linear programming for high-performance mechatronics
Authors: Charlle Lee Sy
Publication title: Australian Journal of Electrical and Electronics Engineering 11(3), January 2014
Abstract
All realistic engineering systems operate in uncertain environments, and in some cases even small levels of noise can severely compromise their performance and stability. In this paper, we propose a robust design approach to improve the control performance of dynamic systems. Our proposed approach first specifies a set of linear constraints describing dynamic performance requirements based on pole placement, and develops a robust linear programming model which is solved to yield a parameter design that achieves the performance requirements under uncertainties. The advantage of our proposed approach is its computational efficiency since it reduces the problem to solving a small sequence of linear programming problems, and the proposed optimisation model can be embedded easily in a local search framework. The effectiveness of our proposed approach is verified xvith theoretical developments and computational studies on a typical high-performance mechatronic system; the hard disc drive.
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Article title: A resilience optimization approach for workforce-inventory control dynamics under uncertainty
Authors: Tsan Sheng Ng & Charlle Lee Sy
Publication title: Journal of Scheduling 17(5), October 2013
Abstract
The presence of uncertainties in manufacturing systems and supply chains can cause undesirable behavior. Failure to account for these in the design phase can further impair the capability of systems to respond to changes effectively. In this work, we consider a dynamic workforce-inventory control problem wherein inventory planning, production releases, and workforce hiring decisions need to be made. The objective is to develop planning rules to achieve important requirements related to dynamic transient behavior when system parameters are imprecisely known. To this end, we propose a resilience optimization model for the problem and develop a novel local search procedure that combines the strengths of recent developments in robust optimization technology and small signal stability analysis of dynamic systems. A numerical case study of the problem demonstrates significant improvements of the proposed solution in controlling fluctuations and high variability found in the system’s inventory, work-in-process, and workforce levels. Overall, the proposed model is shown to be computationally efficient and effective in hedging against model uncertainties.
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Article title: Robust parameter design for system dynamics models: A formal approach based on goal-seeking behavior
Authors: Tsan Sheng Ng, Charlle Lee Sy, Loo Hay Lee
Publication title: System Dynamics Review 28(3), July 2012
Abstract
Parametric uncertainties in system dynamics models can cause undesirable behavior, and if unaccounted for in the design phase can impair the ability of the system to meet design requirements. Sensitivity analysis, while useful in the modeling process, does not by itself offer a systematic method to the problem of system design under uncertainties. In this work, we propose an optimization model‐based approach with the aim of obtaining a parametric design for system dynamics models that is robust against uncertainties. Our research synthesizes recent developments in robust optimization technology, eigenvalue analysis and the goal‐seeking behavior of decision agents. To this end, we develop a mathematical model that is computationally efficient to solve and effective in hedging against parametric uncertainties. Numerical case studies conducted using a hare and lynx model and an inventory–workforce model demonstrate significant improvements of the proposed designs in achieving system requirements under uncertainty. Copyright © 2012 System Dynamics Society.
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Article title: A System Dynamics Model of Singapore Healthcare affordability
Authors: Adam, Tsan Sheng Ng Charlle Sy
Publication title: Proceedings - Winter Simulation Conference
Abstract
In many countries, healthcare expenditure has witnessed an accelerated pace of increase over the years. This has placed a strain on both public and private sectors to effectively mitigate the surmounting pres-sures of healthcare costs, affordability and accessibility. This paper looks into these issues within Singa-pore's healthcare system. The system dynamics simulation method has been used to elucidate complexi-ties brought about by multiple interconnected subsystems and their complex relationships. Simulations have been carried out to understand how the different entities in the system influence healthcare afforda-bility. For instance, this included observing how demand for hospital services affected the various critical hospital resources and their respective costs. Four different classes of policies have then been developed and subsequently tested for their effectiveness in improving healthcare affordability.
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