An interval-parameter stochastic robust optimization model for supporting municipal solid waste management under uncertainty

Development of an Appropriate Uncertainty Model with an Application to Solid Waste Management Planning

The purpose of this study is to achieve a novel and efficient method for treating the interval coefficient linear programming (ICLP) problems. The problem is used for modeling an uncertain environment that represents most real-life problems. Moreover, the optimal solution of the model represents a decision under uncertainty that has a risk of selecting the correct optimal solution that satisfies the optimality and the feasibility conditions. Therefore, a proposed algorithm is suggested for treating the ICLP problems depending on novel measures such as the optimality ratio, feasibility ratio, and the normalized risk factor. Depending upon these measures and the concept of possible scenarios, a novel and effective analysis of the problem is done. Unlike other algorithms, the proposed algorithm involves an important role for the decision-maker (DM) in defining a satisfied optimal solution by using a utility function and other required parameters. Numerical examples are used for comparing and illustrating the robustness of the proposed algorithm. Finally, applying the algorithm to treat a Solid Waste Management Planning is introduced.

Optimal design of two-dimensional water trading based on risk aversion for sustainable development of Daguhe watershed, China

Water related problems, including water scarcity and pollution, have become increasingly urgent challenges especially in arid and semiarid regions. Two-dimensional water trading (2DWT) mechanism has been designed to unify the quantity and quality of water for relieving the water crisis. This study aims to develop a risk aversion optimization-two dimensional water trading model (RAO-2DWTM) for planning the regional-scale water resources management system (RWMS). This is the first attempt on planning RWMS through risk aversion optimization within the two-dimensional water trading framework. RAO-2DWTM cannot only support in-depth analysis regarding the effect of decision maker's preferences on system risk in different trading scenarios, but also reflect the interaction between water right trading and effluent trading, as well as disclose the optimal scheme of water resource management under uncertainties. Twenty four scenarios associated with different trading scenarios and robust levels are analyzed. The optimization scheme under the optimal risk control level is determined based on TOPSIS. Results revealed that 2DWT would bring high benefit with reduced risk cost, water deficit and emissions, implying the effectiveness of 2DWT mechanism. The results also disclosed that risk aversion behavior can mitigate water scarcity and pollution, as well as reduce risk cost, but may lead to some losses of system benefit. Consequently, decision makers should make trade-offs between system benefit and risk in identifying desired trading schemes.

A mixed integer linear programming model for long-term planning of municipal solid waste management systems: Against restricted mass balances

Long-term planning of municipal solid waste management systems is a complex decision making problem which includes a large number of decision layers. Since all different waste treatment and disposal processes will show different responses to each municipal solid waste component, it is necessary to separately evaluate all waste components for all processes. This obligation creates an obstacle in the programming of mass balances for long-term planning of municipal solid waste management systems. The development of an ideal mixed integer linear programming model that can simultaneously respond to all essential decision layers including waste collection, process selection, waste allocation, transportation, location selection, and capacity assessment has not been made possible yet due to this important modeling obstacle. According to the current knowledge of the literature, all mixed integer linear programming studies aiming to address this obstacle so far have had to restrict many different possibilities in their mass balances. In this study, a novel mixed integer linear programming model was formulated. ALOMWASTE, the new model structure developed in this study, was built to take into consideration different process, capacity, and location possibilities that may occur in complex waste management processes at the same time. The results obtained from a case study showed the feasibility of new mixed integer linear programming model obtained in this study for the simultaneous solution of all essential decision layers in an unrestricted mass balance. The model is also able to provide significant convenience for the multi-objective optimization of financial-environmental-social costs and the solution of some uncertainty problems of decision-making tools such as life cycle assessment.

Municipal solid waste management considering NGO’s role in consumer environmental awareness and government regulations for air pollution

Purpose

The purpose of this study is twofold: first, to examine the role of non-governmental organizations (NGOs) in increasing customer environmental awareness (CEA) to decrease the municipal solid waste (MSW), and secondly, to examine the effect of government policies in the amount of air pollution caused by transfer stations (TSs).

Design/methodology/approach

This study proposes a mixed-integer nonlinear programming model. For solving this multi-objective problem, the authors use epsilon constraint method, which presented eight Pareto solutions. For selecting the best solution, the analytic hierarchy process approach is used. The presented model is applied on a real case study, and the results are discussed and sensitivity analysis is implemented on the parameters of the concern.

Findings

This study confirms the assumption that by allocating budget to NGOs for increasing CEA, the produced waste will be decreased.

Research limitations/implications

In the present study, the authors only investigate air pollution caused by TS. Future studies can investigate other types of pollution. Furthermore, uncertainty in the amount of produced waste can be variable making the problem closer to the real environment. In this case, robust optimization may have better results.

Practical implications

Based on the results of sensitivity analysis, some implications obtain that can highlight by managers in the decision-making process. The operational costs of TS have a critical aspect in founding TS, so using new technology and high-tech machines for operational processes of TSs, can result in decreasing the running cost of TSs. Also, the determination of TS capacity is a remarkable issue in optimization, which should be paid special attention to this for the design of TSs in the planning phase of the system. Moreover, collaborating with NGOs has a good effect on increasing CEA that results in a decrease of MSW.

Originality/value

The role of NGOs and government simultaneity has been considered in a green supply chain. Moreover, the authors considered TS between source and disposal that reduce the time of transferring waste. Therefore, this study can be beneficial for the MSW management system, which faces the problems in the lack of capacity and transportation problems and environmental issues by proposing solutions in three studies including economic, environmental and social aspects.

Managing the uncertainty problems of municipal solid waste disposal

In waste management systems, several parameters such as the rate of waste production, disposal facility, treatment cost, and their relations may be uncertain and can influence the associated optimization processes. These uncertainty problems in waste management were addressed by using various inexact programming methods. For example, fuzzy, stochastic programming, and interval programming techniques were generally used for solving the uncertainty-related waste management problems. The analysis revealed that the efficiency of waste management system can be maximized by the proper use of these optimization techniques. In this approach, an uncertainty problem is reduced into several subproblems with sureness by using the minimax regret optimization technique. And these subproblems are focused on a calculation where the lament of not getting the goal is minimized. The analysis also revealed that the fuzzy-stochastic method was increasingly used for dealing with the waste management system uncertainty in recent times. This paper gives an overview of dealing with the uncertainty problems of waste disposal in urban areas. An indication of the solid waste disposal problems and its management in conjunction with the repercussion of the investigation is described. The rationale and setting of the uncertainty issues in proper waste management are detailed. The applications of fuzzy analysis approach and integrated waste management in dealing with the uncertainty problems are presented. The applications of these techniques in diverse case studies worldwide are discussed and finally, the conclusions of the literature analysis are summarized.

A multi-objective model for sustainable recycling of municipal solid waste

The efficient management of municipal solid waste is a major problem for large and populated cities. In many countries, the majority of municipal solid waste is landfilled or dumped owing to an inefficient waste management system. Therefore, an optimal and sustainable waste management strategy is needed. This study introduces a recycling and disposal network for sustainable utilisation of municipal solid waste. In order to optimise the network, we develop a multi-objective mixed integer linear programming model in which the economic, environmental and social dimensions of sustainability are concurrently balanced. The model is able to: select the best combination of waste treatment facilities; specify the type, location and capacity of waste treatment facilities; determine the allocation of waste to facilities; consider the transportation of waste and distribution of processed products; maximise the profit of the system; minimise the environmental footprint; maximise the social impacts of the system; and eventually generate an optimal and sustainable configuration for municipal solid waste management. The proposed methodology could be applied to any region around the world. Here, the city of Tehran, Iran, is presented as a real case study to show the applicability of the methodology.

Distributed mixed-integer fuzzy hierarchical programming for municipal solid waste management. Part I: System identification and methodology development

Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.

Search for a new economic optimum in the management of household waste in Tiaret city (western Algeria)

In household waste matters, the objective is always to conceive an optimal integrated system of management, where the terms 'optimal' and 'integrated' refer generally to a combination between the waste and the techniques of treatment, valorization and elimination, which often aim at the lowest possible cost. The management optimization of household waste using operational methodologies has not yet been applied in any Algerian district. We proposed an optimization of the valorization of household waste in Tiaret city in order to lower the total management cost. The methodology is modelled by non-linear mathematical equations using 28 variables of decision and aims to assign optimally the seven components of household waste (i.e. plastic, cardboard paper, glass, metals, textiles, organic matter and others) among four centres of treatment [i.e. waste to energy (WTE) or incineration, composting (CM), anaerobic digestion (ANB) or methanization and landfilling (LF)]. The analysis of the obtained results shows that the variation of total cost is mainly due to the assignment of waste among the treatment centres and that certain treatment cannot be applied to household waste in Tiaret city. On the other hand, certain techniques of valorization have been favoured by the optimization. In this work, four scenarios have been proposed to optimize the system cost, where the modelling shows that the mixed scenario (the three treatment centres CM, ANB, LF) suggests a better combination of technologies of waste treatment, with an optimal solution for the system (cost and profit).

A Fuzzy Robust Optimization Model for Waste Allocation Planning Under Uncertainty

In this study, a fuzzy robust optimization (FRO) model was developed for supporting municipal solid waste management under uncertainty. The Development Zone of the City of Dalian, China, was used as a study case for demonstration. Comparing with traditional fuzzy models, the FRO model made improvement by considering the minimization of the weighted summation among the expected objective values, the differences between two extreme possible objective values, and the penalty of the constraints violation as the objective function, instead of relying purely on the minimization of expected value. Such an improvement leads to enhanced system reliability and the model becomes especially useful when multiple types of uncertainties and complexities are involved in the management system. Through a case study, the applicability of the FRO model was successfully demonstrated. Solutions under three future planning scenarios were provided by the FRO model, including (1) priority on economic development, (2) priority on environmental protection, and (3) balanced consideration for both. The balanced scenario solution was recommended for decision makers, since it respected both system economy and reliability. The model proved valuable in providing a comprehensive profile about the studied system and helping decision makers gain an in-depth insight into system complexity and select cost-effective management strategies.

A methodology for optimal MSW management, with an application in the waste transportation of Attica Region, Greece

The paper describes a software system capable of formulating alternative optimal Municipal Solid Wastes (MSWs) management plans, each of which meets a set of constraints that may reflect selected objections and/or wishes of local communities. The objective function to be minimized in each plan is the sum of the annualized capital investment and annual operating cost of all transportation, treatment and final disposal operations involved, taking into consideration the possible income from the sale of products and any other financial incentives or disincentives that may exist. For each plan formulated, the system generates several reports that define the plan, analyze its cost elements and yield an indicative profile of selected types of installations, as well as data files that facilitate the geographic representation of the optimal solution in maps through the use of GIS. A number of these reports compare the technical and economic data from all scenarios considered at the study area, municipality and installation level constituting in effect sensitivity analysis. The generation of alternative plans offers local authorities the opportunity of choice and the results of the sensitivity analysis allow them to choose wisely and with consensus. The paper presents also an application of this software system in the capital Region of Attica in Greece, for the purpose of developing an optimal waste transportation system in line with its approved waste management plan. The formulated plan was able to: (a) serve 113 Municipalities and Communities that generate nearly 2 milliont/y of comingled MSW with distinctly different waste collection patterns, (b) take into consideration several existing waste transfer stations (WTS) and optimize their use within the overall plan, (c) select the most appropriate sites among the potentially suitable (new and in use) ones, (d) generate the optimal profile of each WTS proposed, and (e) perform sensitivity analysis so as to define the impact of selected sets of constraints (limitations in the availability of sites and in the capacity of their installations) on the design and cost of the ensuing optimal waste transfer system. The results show that optimal planning offers significant economic savings to municipalities, while reducing at the same time the present levels of traffic, fuel consumptions and air emissions in the congested Athens basin.

Challenges when performing economic optimization of waste treatment: a review

Strategic and operational decisions in waste management, in particular with respect to investments in new treatment facilities, are needed due to a number of factors, including continuously increasing amounts of waste, political demands for efficient utilization of waste resources, and the decommissioning of existing waste treatment facilities. Optimization models can assist in ensuring that these investment strategies are economically feasible. Various economic optimization models for waste treatment have been developed which focus on different parameters. Models focusing on transport are one example, but models focusing on energy production have also been developed, as well as models which take into account a plant's economies of scale, environmental impact, material recovery and social costs. Finally, models combining different criteria for the selection of waste treatment methods in multi-criteria analysis have been developed. A thorough updated review of the existing models is presented, and the main challenges and crucial parameters that need to be taken into account when assessing the economic performance of waste treatment alternatives are identified. The review article will assist both policy-makers and model-developers involved in assessing the economic performance of waste treatment alternatives.

Cost estimation for solid waste management in industrialising regions--precedents, problems and prospects

The importance of cost planning for solid waste management (SWM) in industrialising regions (IR) is not well recognised. The approaches used to estimate costs of SWM can broadly be classified into three categories - the unit cost method, benchmarking techniques and developing cost models using sub-approaches such as cost and production function analysis. These methods have been developed into computer programmes with varying functionality and utility. IR mostly use the unit cost and benchmarking approach to estimate their SWM costs. The models for cost estimation, on the other hand, are used at times in industrialised countries, but not in IR. Taken together, these approaches could be viewed as precedents that can be modified appropriately to suit waste management systems in IR. The main challenges (or problems) one might face while attempting to do so are a lack of cost data, and a lack of quality for what data do exist. There are practical benefits to planners in IR where solid waste problems are critical and budgets are limited.

An interval-based regret-analysis method for identifying long-term municipal solid waste management policy under uncertainty

In this study, an interval-based regret-analysis (IBRA) model is developed for supporting long-term planning of municipal solid waste (MSW) management activities in the City of Changchun, the capital of Jilin Province, China. The developed IBRA model incorporates approaches of interval-parameter programming (IPP) and minimax-regret (MMR) analysis within an integer programming framework, such that uncertainties expressed as both interval values and random variables can be reflected. The IBRA can account for economic consequences under all possible scenarios associated with different system costs and risk levels without making assumptions on probabilistic distributions for random variables. A regret matrix with interval elements is generated based on a matrix of interval system costs, such that desired decision alternatives can be identified according to the interval minimax regret (IMMR) criterion. The results indicate that reasonable solutions have been generated. They can help decision makers identify the desired alternatives regarding long-term MSW management with a compromise between minimized system cost and minimized system-failure risk.