Minimax Regret Analysis for Municipal Solid Waste Management: An Interval-Stochastic Programming Approach

Abstract

In this study, an interval minimax regret programming (IMMRP) method is developed for the planning of municipal solid waste (MSW) management under uncertainty. It improves on the existing interval programming and minimax regret analysis methods by allowing uncertainties presented as both intervals and random variables to be effectively communicated into the optimization process. The IMMRP can account for economic consequences under all possible scenarios without any assumption on their probabilities. The developed method is applied to a case study of long-term MSW management planning under uncertainty. Multiple scenarios associated with different cost and risk levels are analyzed. Reasonable solutions are generated, demonstrating complex tradeoffs among system cost, regret level, and system-failure risk. The method can also facilitate examination of the difference between the cost incurred with identified strategy and the least cost under an ideal condition. The results can help determine desired plans and policies for waste management under a variety of uncertainties.     

Modeling Municipal Solid Waste Management System under Uncertainty

Abstract

In this study, a dynamic inexact waste management (DIWM) model is developed for identifying optimal waste-flow-allocation and facility-capacity-expansion strategies under uncertainty and is based on an inexact scenario-based probabilistic programming (ISPP) approach. The DIWM model can handle uncertainties presented as interval values and probability distributions, and it can support assessing the risk of violating system constraints. Several violation levels for facility-capacity and waste-diversion constraints are examined. Solutions associated with different risks of constraint violation were generated. The modeling results are valuable for supporting the planning of the study city’s municipal solid waste (MSW) management practices, the long-term capacity expansion for waste management system, and the identification of desired policies regarding waste diversion. Sensitivity analyses are also undertaken to demonstrate that the violations of different constraints have varied effects on the planning of waste-flow allocation, facility expansion, and waste management cost.     

Factors influencing regional municipal solid waste management strategies

Although regionalization policies have been proven as good strategies for municipal solid waste (MSW) management in previous studies, the optimal allocation of the waste stream is significantly affected by several influential factors, thus further investigation of the impacts of these factors on regional MSW management strategies is necessary. This study demonstrated the impacts of waste-to-electricity transformation coefficient (WETC) of incinerators and the fluctuation of unit tipping fees on the regional MSW flow/allocation of the Taipei metropolitan area from practical and economic perspectives. Real-world data and linear programming were used to obtain the least-cost alternatives under different scenarios. Analytical results indicated some treatment facilities had geographic superiority and their priorities changed when actual WETCs of incinerators were considered. Treatment facilities located at weighted centers were identified. The allocation of MSW among incinerators and landfills is affected by fluctuation of unit tipping fees within a certain range. Treatment facilities sensitive to the changes in unit tipping fees were also identified. Regression equations were also established that can estimate the cost items of MSW management scenarios with different unit tipping fees. The results of this study are very useful for daily basis regulation of MSW administration.     

Factors Influencing Regional Municipal Solid Waste Management Strategies

Abstract

Although regionalization policies have been proven as good strategies for municipal solid waste (MSW) management in previous studies, the optimal allocation of the waste stream is significantly affected by several influential factors, thus further investigation of the impacts of these factors on regional MSW management strategies is necessary. This study demonstrated the impacts of waste-to-electricity transformation coefficient (WETC) of incinerators and the fluctuation of unit tipping fees on the regional MSW flow/allocation of the Taipei metropolitan area from practical and economic perspectives. Real-world data and linear programming were used to obtain the least-cost alternatives under different scenarios. Analytical results indicated some treatment facilities had geographic superiority and their priorities changed when actual WETCs of incinerators were considered. Treatment facilities located at weighted centers were identified. The allocation of MSW among incinerators and landfills is affected by fluctuation of unit tipping fees within a certain range. Treatment facilities sensitive to the changes in unit tipping fees were also identified. Regression equations were also established that can estimate the cost items of MSW management scenarios with different unit tipping fees. The results of this study are very useful for daily basis regulation of MSW administration.     

Municipal solid waste management planning for Xiamen City,China: a stochastic fractional inventory-theory-based approach

In this study, a stochastic fractional inventory-theory-based waste management planning (SFIWP) model was developed and applied for supporting long-term planning of the municipal solid waste (MSW) management in Xiamen City, the special economic zone of Fujian Province, China. In the SFIWP model, the techniques of inventory model, stochastic linear fractional programming, and mixed-integer linear programming were integrated in a framework. Issues of waste inventory in MSW management system were solved, and the system efficiency was maximized through considering maximum net-diverted wastes under various constraint-violation risks. Decision alternatives for waste allocation and capacity expansion were also provided for MSW management planning in Xiamen. The obtained results showed that about 4.24 × 10⁶ t of waste would be diverted from landfills when p _i is 0.01, which accounted for 93% of waste in Xiamen City, and the waste diversion per unit of cost would be 26.327 × 10³ t per $10⁶. The capacities of MSW management facilities including incinerators, composting facility, and landfills would be expanded due to increasing waste generation rate.     

A copula-based chance-constrained waste management planning method: An application to the city of Regina,Saskatchewan, Canada

This study proposes a copula-based chance-constrained waste management planning (CCWMP) method. The method can effectively reflect the interactions between random parameters of the waste management planning systems, and thus can help analyze the influences of their interactions on the entire systems. In particular, a joint distribution function is established using preestimated marginal distributions of random variables and an optimal copula selected from widely used Gaussian, Student’s t, Clayton, Frank, Gumbel, and Ali-Mikhail-Haq copulas. Then a set of joint probabilistic constraints in the chance-constrained programming problems is converted into individual probabilistic constraints using the joint distribution function. Further, this method is applied to residential solid waste management in the city of Regina in Canada for demonstrating its applicability. Nine scenarios based on different joint and marginal probability levels are considered within a multiperiod and multizone context to effectively reflect dynamic, uncertain, and interactive characteristics of the solid waste management systems in the city. The results provide many decision alternatives under these scenarios, including cost-effective and environmentally friendly decision schemes. Moreover, the results indicate that even though the effect of the joint probability levels on the system costs is more significant than that of the marginal probability levels, the effect of marginal probability levels is notable, and there exists a trade-off between the total system cost and the constraint-violation risk. Therefore, the results obtained from the present study would be useful to support the city’s long-term solid waste management planning and formulate local policies and regulation concerning the city’s waste generation and management.Implications: The CCWMP method not only can solve chance-constrained problems with unknown probability distributions of random variables in the right-hand sides of constraints, but also can effectively reflect the interactions between the random parameters and thus help analyze the influences of their interactions on the entire systems. The results obtained through applying this method to the city of Regina in Canada can provide many decision alternatives under different joint probability levels and marginal probability levels, and would be useful to support the city’s long-term solid waste management planning.     

A two-stage interval-stochastic programming model for waste management under uncertainty

This study introduces a two-stage interval-stochastic programming (TISP) model for the planning of solid-waste management systems under uncertainty. The model is derived by incorporating the concept of two-stage stochastic programming within an interval-parameter optimization framework. The approach has the advantage that policy determined by the authorities, and uncertain information expressed as intervals and probability distributions, can be effectively communicated into the optimization processes and resulting solutions. In the modeling formulation, penalties are imposed when policies expressed as allowable waste-loading levels are violated. In its solution algorithm, the TISP model is converted into two deterministic submodels, which correspond to the lower and upper bounds for the desired objective-function value. Interval solutions, which are stable in the given decision space with associated levels of system-failure risk, can then be obtained by solving the two submodels sequentially. Two special characteristics of the proposed approach make it unique compared with other optimization techniques that deal with uncertainties. First, the TISP model provides a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken; second, it furnishes the reflection of uncertainties presented as both probabilities and intervals. The developed model is applied to a hypothetical case study of regional solid-waste management. The results indicate that reasonable solutions have been generated. They provide desired waste-flow patterns with minimized system costs and maximized system feasibility. The solutions present as stable interval solutions with different risk levels in violating the waste-loading criterion and can be used for generating decision alternatives.     

An optimal model and its application for the management of municipal solid waste from regional small cities in China

Based on the basic characteristics of municipal solid waste (MSW) from regional small cities in China, some optimal management principles have been put forward: regional optimization, long-term optimization, and integrated treatment/disposal optimization. According to these principles, an optimal MSW management model for regional small cities is developed and provides a useful method to manage MSW from regional small cities. A case study application of the optimal model is described and shows that the optimal management scenarios in the controlling region can be gained, adequately validating and accounting for the advantages of the optimal model.     

城市固体废物优化管理模型及管理成本影响因素研究

采用不确定性多目标动态优化模型,以优化环境和经济为目标,对中国佛山市固体废物管理进行规划.结果表明,该模型能大幅度降低固体废物管理与处理成本,节省财政支出.研究得出影响固体废物总处理费用的3个重要影响因素为产生量、回收量、处理容量.针对以上3个因素深入分析比较,提出了广义和狭义的综合处理技术,这是经济、环保、可行的技术策略:首先,将广义综合处理应用到实际中,采用分类回收、压缩收集、优化模型对废物进行合理配置,尽量降低经济成本和环境影响;其次,从狭义角度出发,采用多种技术组合,达到处理率高、资源化程度高、环境影响小的目的.     

81st APCA Annual Meeting & Exhibition

Abstract

This study introduces a two-stage interval-stochastic programming (TISP) model for the planning of solid-waste management systems under uncertainty. The model is derived by incorporating the concept of two-stage stochastic programming within an interval-parameter optimization framework. The approach has the advantage that policy determined by the authorities, and uncertain information expressed as intervals and probability distributions, can be effectively communicated into the optimization processes and resulting solutions. In the modeling formulation, penalties are imposed when policies expressed as allowable waste-loading levels are violated. In its solution algorithm, the TISP model is converted into two deterministic submodels, which correspond to the lower and upper bounds for the desired objective-function value. Interval solutions, which are stable in the given decision space with associated levels of system-failure risk, can then be obtained by solving the two submodels sequentially. Two special characteristics of the proposed approach make it unique compared with other optimization techniques that deal with uncertainties. First, the TISP model provides a linkage to prede?ned policies determined by authorities that have to be respected when a modeling effort is undertaken; second, it furnishes the reflection of uncertainties presented as both probabilities and intervals. The developed model is applied to a hypothetical case study of regional solid-waste management. The results indicate that reasonable solutions have been generated. They provide desired waste-flow patterns with minimized system costs and maximized system feasibility. The solutions present as stable interval solutions with different risk levels in violating the waste-loading criterion and can be used for generating decision alternatives.     

A generalized fuzzy linear programming approach for environmental management problem under uncertainty

In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO2) control planning model to identify effective SO2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP and then identify desired policies for SO2-emission control under uncertainty.     

Evaluation of Solid Waste Management Strategies in the Taipei Metropolitan Area of Taiwan

Abstract

Chenfang Lin is with the Department of Soil and Environmental Science at National Chung Hsing University.Municipal solid waste (MSW) management is a major concern for highly urbanized societies. Among proposed MSW management systems, regionalization programs generally have received considerable attention. This study analyzes real-world operational data to assess different MSW management policies, especially regionalization strategies, and their impact on MSW management systems in the Taipei metropolitan area. Linear programming is also used to identify the minimum costs sustained by each policy. The linear programming results show that regionalization programs are more economical and also improve incinerator operation efficiency. Sensitivity analysis indicates that the minimum treatment requirement of incinerators is a very sensitive influence on the MSW flows distributed through the entire region. The MSW of several “sensitive” administrative districts will be allocated to different treatment facilities according to different management strategies. A list of preferential sequences of MSW treatment and disposal facilities can also be identified by the model presented in this study. The results of this study may provide a useful tool for aiding decision-making related to real-world MSW management problems.     

Long short-term memory neural network and improved particle swarm optimization–based modeling and scenario analysis for municipal solid waste generation in Shanghai,China

Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

     

An Optimal Model and Its Application for the Management of Municipal Solid Waste from Regional Small Cities in China

Abstract

Based on the basic characteristics of municipal solid waste (MSW) from regional small cities in China, some optimal management principles have been put forward: regional optimization, long-term optimization, and integrated treatment/disposal optimization. According to these principles, an optimal MSW management model for regional small cities is developed and provides a useful method to manage MSW from regional small cities. A case study application of the optimal model is described and shows that the optimal management scenarios in the controlling region can be gained, adequately validating and accounting for the advantages of the optimal model.     

Evaluation of solid waste management strategies in the Taipei metropolitan area of Taiwan

Municipal solid waste (MSW) management is a major concern for highly urbanized societies. Among proposed MSW management systems, regionalization programs generally have received considerable attention. This study analyzes real-world operational data to assess different MSW management policies, especially regionalization strategies, and their impact on MSW management systems in the Taipei metropolitan area. Linear programming is also used to identify the minimum costs sustained by each policy. The linear programming results show that regionalization programs are more economical and also improve incinerator operation efficiency. Sensitivity analysis indicates that the minimum treatment requirement of incinerators is a very sensitive influence on the MSW flows distributed through the entire region. The MSW of several "sensitive" administrative districts will be allocated to different treatment facilities according to different management strategies. A list of preferential sequences of MSW treatment and disposal facilities can also be identified by the model presented in this study. The results of this study may provide a useful tool for aiding decision-making related to real-world MSW management problems.     

Setting the most robust effluent level under severe uncertainty: Application of information-gap decision theory to chemical management

Decisions in ecological risk management for chemical substances must be made based on incomplete information due to uncertainties. To protect the ecosystems from the adverse effect of chemicals, a precautionary approach is often taken. The precautionary approach, which is based on conservative assumptions about the risks of chemical substances, can be applied selecting management models and data. This approach can lead to an adequate margin of safety for ecosystems by reducing exposure to harmful substances, either by reducing the use of target chemicals or putting in place strict water quality criteria. However, the reduction of chemical use or effluent concentrations typically entails a financial burden. The cost effectiveness of the precautionary approach may be small. Hence, we need to develop a formulaic methodology in chemical risk management that can sufficiently protect ecosystems in a cost-effective way, even when we do not have sufficient information for chemical management. Information-gap decision theory can provide the formulaic methodology. Information-gap decision theory determines which action is the most robust to uncertainty by guaranteeing an acceptable outcome under the largest degree of uncertainty without requiring information about the extent of parameter uncertainty at the outset. In this paper, we illustrate the application of information-gap decision theory to derive a framework for setting effluent limits of pollutants for point sources under uncertainty. Our application incorporates a cost for reduction in pollutant emission and a cost to wildlife species affected by the pollutant. Our framework enables us to settle upon actions to deal with severe uncertainty in ecological risk management of chemicals.     

An economic evaluation and assessment of environmental impact of the municipal solid waste management system for Taichung City in Taiwan

Municipal solid waste management (MSWM) is an important environmental challenge and subject in urban planning. For sustainable MSWM strategies, the critical management factors to be considered include not only economic efficiency of MSW treatment but also life-cycle assessment of the environmental impact. This paper employed linear programming technique to establish optimal MSWM strategies considering economic efficiency and the air pollutant emissions during the life cycle of a MSWM system, and investigated the correlations between the economical optimization and pollutant emissions. A case study based on real-world MSW operating parameters in Taichung City is also presented. The results showed that the costs, benefits, streams of MSW, and throughputs of incinerators and landfills will be affected if pollution emission reductions are implemented in the MSWM strategies. In addition, the quantity of particulate matter is the best pollutant indicator for the MSWM system performance of emission reduction. In particular this model will assist the decision maker in drawing up a friendly MSWM strategy for Taichung City in Taiwan. Implications: Recently, life-cycle assessments of municipal solid waste management (MSWM) strategies have been given more considerations. However, what seems to be lacking is the consideration of economic factors and environmental impacts simultaneously. This work analyzed real-world data to establish optimal MSWM strategies considering economic efficiency and the air pollutant emissions during the life cycle of the MSWM system. The results indicated that the consideration of environmental impacts will affect the costs, benefits, streams of MSW, and throughputs of incinerators and landfills. This work is relevant to public discussion and may establish useful guidelines for the MSWM policies.     

Two-Stage Fuzzy-Stochastic Robust Programming: A Hybrid Model for Regional Air Quality Management

Abstract

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.     

Life cycle and economic assessment of source-separated MSW collection with regard to greenhouse gas emissions: a case study in China

In China, the continuously increasing amount of municipal solid waste (MSW) has resulted in an urgent need for changing the current municipal solid waste management (MSWM) system based on mixed collection. A pilot program focusing on source-separated MSW collection was thus launched (2010) in Hangzhou, China, to lessen the related environmental loads. And greenhouse gas (GHG) emissions (Kyoto Protocol) are singled out in particular. This paper uses life cycle assessment modeling to evaluate the potential environmental improvement with regard to GHG emissions. The pre-existing MSWM system is assessed as baseline, while the source separation scenario is compared internally. Results show that 23 % GHG emissions can be decreased by source-separated collection compared with the base scenario. In addition, the use of composting and anaerobic digestion (AD) is suggested for further optimizing the management of food waste. 260.79, 82.21, and ?86.21 thousand tonnes of GHG emissions are emitted from food waste landfill, composting, and AD, respectively, proving the emission reduction potential brought by advanced food waste treatment technologies. Realizing the fact, a modified MSWM system is proposed by taking AD as food waste substitution option, with additional 44 % GHG emissions saved than current source separation scenario. Moreover, a preliminary economic assessment is implemented. It is demonstrated that both source separation scenarios have a good cost reduction potential than mixed collection, with the proposed new system the most cost-effective one.     

Two-stage fuzzy-stochastic robust programming: a hybrid model for regional air quality management

In this study, a hybrid two-stage fuzzy-stochastic robust programming (TFSRP) model is developed and applied to the planning of an air-quality management system. As an extension of existing fuzzy-robust programming and two-stage stochastic programming methods, the TFSRP can explicitly address complexities and uncertainties of the study system without unrealistic simplifications. Uncertain parameters can be expressed as probability density and/or fuzzy membership functions, such that robustness of the optimization efforts can be enhanced. Moreover, economic penalties as corrective measures against any infeasibilities arising from the uncertainties are taken into account. This method can, thus, provide a linkage to predefined policies determined by authorities that have to be respected when a modeling effort is undertaken. In its solution algorithm, the fuzzy decision space can be delimited through specification of the uncertainties using dimensional enlargement of the original fuzzy constraints. The developed model is applied to a case study of regional air quality management. The results indicate that reasonable solutions have been obtained. The solutions can be used for further generating pollution-mitigation alternatives with minimized system costs and for providing a more solid support for sound environmental decisions.