Siting and Routing Assessment for Solid Waste Management Under Uncertainty Using the Grey Mini-Max Regret Criterion

Solid waste management (SWM) is at the forefront of environmental concerns in the Lower Rio Grande Valley (LRGV), South Texas. The complexity in SWM drives area decision makers to look for innovative and forward-looking solutions to address various waste management options. In decision analysis, it is not uncommon for decision makers to go by an option that may minimize the maximum regret when some determinant factors are vague, ambiguous, or unclear. This article presents an innovative optimization model using the grey mini-max regret (GMMR) integer programming algorithm to outline an optimal regional coordination of solid waste routing and possible landfill/incinerator construction under an uncertain environment. The LRGV is an ideal location to apply the GMMR model for SWM planning because of its constant urban expansion, dwindling landfill space, and insufficient data availability signifying the planning uncertainty combined with vagueness in decision-making. The results give local decision makers hedged sets of options that consider various forms of systematic and event-based uncertainty. By extending the dimension of decision-making, this may lead to identifying a variety of beneficial solutions with efficient waste routing and facility siting for the time frame of 2005 through 2010 in LRGV. The results show the ability of the GMMR model to open insightful scenario planning that can handle situational and data-driven uncertainty in a way that was previously unavailable. Research findings also indicate that the large capital investment of incineration facilities makes such an option less competitive among municipal options for landfills. It is evident that the investment from a municipal standpoint is out of the question, but possible public–private partnerships may alleviate this obstacle.     

Violation analysis for solid waste management systems: an interval fuzzy programming approach

This paper introduces a violation analysis approach for the planning of regional solid waste management systems under uncertainty, based on an interval-parameter fuzzy integer programming (IPFIP) model. In this approach, several given levels of tolerable violation for system constraints are permitted. This is realized through a relaxation of the critical constraints using violation variables, such that the model's decision space can be expanded. Thus, solutions from the violation analysis will not necessarily satisfy all of the model's original constraints. Application of the developed methodology to the planning of a waste management system indicates that reasonable solutions can be generated through this approach. Considerable information regarding decisions of facility expansion and waste flow allocation within the waste management system were generated. The modeling results help to generate a number of decision alternatives under various system conditions, allowing for more in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability.     

Landfill space consumption dynamics in the Lower Rio Grande Valley by grey integer programming-based games

The Lower Rio Grande Valley (LRGV) region in South Texas emerges as a warehouse and transportation center between Central America and the US with positive growth impacts due to the North American Free Trade Agreement (NAFTA). In 10 years time, a 39.8% population increase has resulted in a 25% boost in solid waste per capita disposal rate in the region. A landfill space shortage drives a need for landfill operators to understand their optimal management strategies in this highly-competitive market. Initially, a strategic plan for optimal solid waste pattern distribution minimizes net costs for cities. This is accomplished through a grey integer programming algorithm that encapsulates all uncertainty present in the solid waste system. Secondly, a series of grey integer submodels construct payoff matrices for a zero-sum two-person game. The ensuing game theoretic analysis is critical for evaluating optimal pricing strategies for tipping fees available to the most significant regional landfills (e.g. Browning-Ferris Industries (BFI) and City of Edinburg) as they compete over disposal contracts. The BFI landfill intrinsically benefits from its competitive pricing policy and central location to solid waste generators. The City of Edinburg landfill, on the other hand, wishes to secure its lucrative solid waste management revenue. It desires a gaming strategy backed by optimality that integrates ambiguity in solid waste generation, design capacity boundaries, and unitary shipping costs. Results show that a two-tiered analysis via grey integer programming-based games may pave the way for 'grey Nash equilibria' pricing tactics that will help the Edinburg landfill maintain its waste contracts.     

An inexact two-stage mixed integer linear programming method for solid waste management in the City of Regina

In this study, an interval-parameter two-stage mixed integer linear programming (ITMILP) model is developed for supporting long-term planning of waste management activities in the City of Regina. In the ITMILP, both two-stage stochastic programming and interval linear programming are introduced into a general mixed integer linear programming framework. Uncertainties expressed as not only probability density functions but also discrete intervals can be reflected. The model can help tackle the dynamic, interactive and uncertain characteristics of the solid waste management system in the City, and can address issues concerning plans for cost-effective waste diversion and landfill prolongation. Three scenarios are considered based on different waste management policies. The results indicate that reasonable solutions have been generated. They are valuable for supporting the adjustment or justification of the existing waste flow allocation patterns, the long-term capacity planning of the City's waste management system, and the formulation of local policies and regulations regarding waste generation and management.     

A two-stage support-vector-regression optimization model for municipal solid waste management - a case study of Beijing, China

In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM can not only predict the city's future waste generation amount, but also reflect dynamic, interactive, and uncertain characteristics of the MSW management system. Four kernel functions such as linear kernel, polynomial kernel, radial basis function, and multi-layer perception kernel are chosen based on three quantitative simulation performance criteria [i.e. prediction accuracy (PA), fitting accuracy (FA) and over all accuracy (OA)]. The SVR with polynomial kernel has accurate prediction performance for MSW generation rate, with all of the three quantitative simulation performance criteria being over 96%. Two cases are considered based on different waste management policies. The results are valuable for supporting the adjustment of the existing waste-allocation patterns to raise the city's waste diversion rate, as well as the capacity planning of waste management system to satisfy the city's increasing waste treatment/disposal demands.     

IFRP: a hybrid interval-parameter fuzzy robust programming approach for waste management planning under uncertainty

In this study, an interval-parameter fuzzy-robust programming (IFRP) model is developed and applied to the planning of solid waste management systems under uncertainty. As an extension of the existing fuzzy-robust programming and interval-parameter linear programming methods, the IFRP can explicitly address system uncertainties with complex presentations. Parameters in the IFRP model can be represented as interval numbers and/or fuzzy membership functions, such that the uncertainties can be directly communicated into the optimization process and resulting solution. Furthermore, highly uncertain information for the lower and upper bounds of interval parameters that exist due to the complexity of the real world can be effectively handled through introducing the concept of fuzzy boundary interval. Consequently, robustness of the optimization process and solution can be enhanced. Results of the case study indicate that useful solutions for planning municipal solid waste management practices can be generated. They reflect a compromise between optimality and stability of the study system. Willingness to pay higher costs will guarantee the system stability; however, a desire to reduce the costs will run the risk of potential instability of the system. The results also suggest that the proposed hybrid methodology is applicable to practical problems that are associated with highly complex and uncertain information.     

A hybrid inexact optimization approach for solid waste management in the city of Foshan,China

An interval-parameter two-stage chance-constraint mixed integer linear programming (ITCMILP) model is provided for supporting long-term planning of solid waste management in the City of Foshan, China. The ITCMILP is formulated by integrating interval-parameter, two-stage, mixed integer, and chance-constraint programming methods into a general framework, and can thus deal with multiple uncertainties associated with model parameters, constraints and objectives. Three scenarios are examined, covering combinations of various system conditions and waste management policies. Scenario 1 is designed for comparison purposes. Scenarios 2 and 3 correspond to situations when the existing landfill's life is to be extended. The results demonstrate that the centralized composting and incinerating facilities are desired for treating the organic waste flows. The tradeoff among system cost, violation risk, and the related policy implications are also analyzed. The results obtained could help decision makers gain in-depth insights into the impact of uncertainties on long-term solid waste management in the City of Foshan.     

An inexact optimization approach for river water-quality management

A previously developed fuzzy waste load allocation model (FWLAM) for a river system is extended to address uncertainty involved in fixing the membership functions for the fuzzy goals of the pollution control agency (PCA) and the dischargers using the concept of grey systems. The model provides flexibility for the PCA and the dischargers to specify their goals independently, as the parameters for membership functions are considered as interval grey numbers instead of deterministic real numbers. An inexact or a grey fuzzy optimization model is developed in a multiobjective framework, to maximize the width of the interval valued fractional removal levels for providing latitude in decision-making and to minimize the width of the goal fulfillment level for reducing the system uncertainty. The concept of an acceptability index for order relation between two partially or fully overlapping intervals is used to get a deterministic equivalent of the grey fuzzy optimization model developed. The improvement of the optimal solutions over a previously developed grey fuzzy waste load allocation model (GFWLAM) is shown through an application to a hypothetical river system. The fuzzy multiobjective optimization and fuzzy goal programming techniques are used to solve the deterministic equivalent of the GFWLAM.     

Flexible interval mixed-integer bi-infinite programming for environmental systems management under uncertainty

A number of inexact programming methods have been developed for municipal solid waste management under uncertainty. However, most of them do not allow the parameters in the objective and constraints of a programming problem to be functional intervals (i.e., the lower and upper bounds of the intervals are functions of impact factors). In this study, a flexible interval mixed-integer bi-infinite programming (FIMIBIP) method is developed in response to the above concern. A case study is also conducted; the solutions are then compared with those obtained from interval mixed-integer bi-infinite programming (IMIBIP) and fuzzy interval mixed-integer programming (FIMIP) methods. It is indicated that the solutions through FIMIBIP can provide decision support for cost-effectively diverting municipal solid waste, and for sizing, timing and siting the facilities’ expansion during the entire planning horizon. These schemes are more flexible than those identified through IMIBIP since the tolerance intervals are introduced to measure the level of constraints satisfaction. The FIMIBIP schemes may also be robust since the solutions are “globally-optimal” under all scenarios caused by the fluctuation of gas/energy prices, while the conventional ones are merely “locally-optimal” under a certain scenario.     

Urban water resources planning by using a modified particle swarm optimization algorithm

A new optimization algorithm by coupling the mutation process to the particle swarm optimization (PSO) is developed in this paper. This algorithm, entitled particle swarm optimization with mutation similarity (PSOMS), is successfully applied to an urban water resources management problem for the large city of Tabriz, Iran. The objective functions of the optimization problem are to minimize the cost, maximize water supply and minimize the environmental hazards. The constraints are physical limits such as pipelines capacity, ground water, the demand and the impact of conservation tools. Due to the parameters uncertainty, the water supply objective is modeled with fuzzy set theory and the objectives are then combined with compromise programming. The resulted single objective is solved using PSOMS, and its efficiency is then compared with the basic PSO and two kinds of genetic algorithms. Among them, PSOMS shows rapid convergence and suitable results compared to other methods. PSOMS is also improved to provide the Pareto frontier, which is needed to proper selecting of the optimal solutions in the uncertain conditions. Finally, the diversity of solutions is checked based on an indicator of the distances between different solutions, which show the efficiency of the PSOMS algorithm with respect to the genetic algorithm. Then by using the non-symmetric Kalai–Smorodinsky method a guideline is provided for comfort selection of the most preferred solution in the Pareto frontier. Based on these outcomes, the multi-objective PSOMS provides more appropriate results needed for urban systems management.     

An inexact reverse logistics model for municipal solid waste management systems

This paper proposed an inexact reverse logistics model for municipal solid waste management systems (IRWM). Waste managers, suppliers, industries and distributors were involved in strategic planning and operational execution through reverse logistics management. All the parameters were assumed to be intervals to quantify the uncertainties in the optimization process and solutions in IRWM. To solve this model, a piecewise interval programming was developed to deal with Min-Min functions in both objectives and constraints. The application of the model was illustrated through a classical municipal solid waste management case. With different cost parameters for landfill and the WTE, two scenarios were analyzed. The IRWM could reflect the dynamic and uncertain characteristics of MSW management systems, and could facilitate the generation of desired management plans. The model could be further advanced through incorporating methods of stochastic or fuzzy parameters into its framework. Design of multi-waste, multi-echelon, multi-uncertainty reverse logistics model for waste management network would also be preferred.     

Waste management with recourse: An inexact dynamic programming model containing fuzzy boundary intervals in objectives and constraints

The existing inexact optimization methods based on interval-parameter linear programming can hardly address problems where coefficients in objective functions are subject to dual uncertainties. In this study, a superiority–inferiority-based inexact fuzzy two-stage mixed-integer linear programming (SI-IFTMILP) model was developed for supporting municipal solid waste management under uncertainty. The developed SI-IFTMILP approach is capable of tackling dual uncertainties presented as fuzzy boundary intervals (FuBIs) in not only constraints, but also objective functions. Uncertainties expressed as a combination of intervals and random variables could also be explicitly reflected. An algorithm with high computational efficiency was provided to solve SI-IFTMILP. SI-IFTMILP was then applied to a long-term waste management case to demonstrate its applicability. Useful interval solutions were obtained. SI-IFTMILP could help generate dynamic facility-expansion and waste-allocation plans, as well as provide corrective actions when anticipated waste management plans are violated. It could also greatly reduce system-violation risk and enhance system robustness through examining two sets of penalties resulting from variations in fuzziness and randomness. Moreover, four possible alternative models were formulated to solve the same problem; solutions from them were then compared with those from SI-IFTMILP. The results indicate that SI-IFTMILP could provide more reliable solutions than the alternatives.     

Comparative risk analysis for metropolitan solid waste management systems

Conventional solid waste management planning usually focuses on economic optimization, in which the related environmental impacts or risks are rarely considered. The purpose of this paper is to illustrate the methodology of how optimization concepts and techniques can be applied to structure and solve risk management problems such that the impacts of air pollution, leachate, traffic congestion, and noise increments can be regulated in the long-term planning of metropolitan solid waste management systems. Management alternatives are sequentially evaluated by adding several environmental risk control constraints stepwise in an attempt to improve the management strategies and reduce the risk impacts in the long run. Statistics associated with those risk control mechanisms are presented as well. Siting, routing, and financial decision making in such solid waste management systems can also be achieved with respect to various resource limitations and disposal requirements.     

Euro-trash: searching Europe for a more sustainable approach to waste management

How an economically affordable, environmentally effective and socially acceptable municipal solid waste management system can be developed is currently unclear. Considerable research has been carried out on the practical aspects of municipal waste management (i.e. transport, treatment and disposal) and how citizens feel about source separation, recycling, incineration and landfill but the perspective of the waste manager within the context of long term planning is often ignored. In this study, waste managers from 11 different leading-edge European municipal solid waste programs in nine different countries were interviewed. The economic, social, political, environmental, legal and technical factors of their specific programs were explored and analyzed. The transition of municipal solid waste management to urban resources management was observed and key ‘system drivers’ for more sustainable waste management practices were identified. Programs visited were: Brescia (I), Copenhagen (DK), Hampshire (UK), Helsinki (FI), Lahn-Dill-Kreis (D), Malmö (SE), Pamplona (E), Prato (I), Saarbrücken (D), Vienna (A), and Zürich (CH).     

A mixed integer linear programming model for optimisation of organics management in an integrated solid waste system

In this paper, the authors propose a mixed integer linear programming model for designing an Integrated Solid Waste Management System (ISWMS) to meet specific economic goals. The model refers to a set of municipalities, known as ‘local basin’, which have to share a common waste management system. At the municipal level the model allows for an identification of the optimal collection service option; at the local basin level, the model provides the optimal waste flow appropriate to the collection service option of each municipality. The model has been applied to a full-scale case study of an area located in southeast Italy. A scenario analysis was carried out to investigate alternative municipal solid waste management options, which fundamentally differ in the organic flow mass rate to be either collected and composted or landfilled. Findings show that an increase in the cost of landfilling determines the optimal collection scenario and the configuration plants tend to recover higher rates of organics in separate collection and thus higher refuse derived fuel productions. The results obtained validate the application of the model in both the strategic planning and operational phases, by supporting public administrators at both municipality and local basin level in decision making and evaluation of technical and economic performances of ISWMSs.     

Risk explicit interval linear programming model for long-term planning of vehicle recycling in the EU legislative context under uncertainty

With the number of vehicles expected to increase to 1.85 billion by 2030 and the scrap generated from end-of-life vehicles (ELVs) expected to be 3.71 billion tonnes, there is a strong motivation to properly process the flow of these materials. The EU Directive on end-of-life vehicles (EU ELV Directive) aims to increase recovery and recycling rates of ELVs in order to reduce waste and improve environmental performances. Long-term optimization planning of vehicle recycling is increasingly important. However, there is a lack of research of uncertainties in the vehicle recycling system, none of the previous studies analyzed the linkage and trade-offs between decision risk and system performances, and no previous research was reported on interval-based programming for vehicle recycling planning problem. In order to meet the imposed eco-efficiency quotas, maximize system profit and minimize decision risk, and at the same time fill the identified research gaps, a risk explicit interval linear programming model for optimal long-term planning in the EU vehicle recycling factories was developed. It can create optimal plans for procuring vehicle hulks, sorting of generated material fractions, allocation of sorted waste flows and allocation of sorted metals for desired value of the system aspiration level. A numerical study demonstrated the potentials and applicability of the proposed model. Vehicle recycling factories aim at reaching the highest possible level of quantity and quality of sorted metal flows. The future eco-efficiency quotas will not endanger their business. The success of the final phase of implementation of the EU ELV Directive is not jeopardized, because even the future eco-efficiency quotas were reached in all created test problems. Quantity of land-filled wastes will be radically reduced after January 1, 2015. The model results and trade-offs would be valuable for supporting the EU vehicle recycling factories in creating optimal long-term production strategies and reducing the risk for uncertain situations.     

A multi-objective location-allocation optimization for sustainable management of municipal solid waste

The location problem of treatment and service facilities in municipal solid waste (MSW) management system is of significant importance due to the socioeconomic and environmental concerns. The consideration of waste treatment costs, environmental impact, greenhouse gas (GHG) emissions, social fairness as well as other relevant aspects should be simultaneously taken into account when a MSW management system is planned. Development of sophisticated decision support tools for planning MSW management system in an economic-efficient and environmental friendly manner is therefore important. In this paper, a general multi-objective location-allocation model for optimally managing the interactions among those conflicting factors in MSW management system is proposed. The model is comprised of a three-stage conceptual framework and a mixed integer mathematical programming. The inclusion of environmental impact and GHG emission objectives push the output of the model tightening toward more environmentally friendly and sustainable solutions in MSW management. The application of this model is demonstrated through an illustrative example, and the computational efficiency of the programming is also tested through a set of incremental parameters. Latter in this paper, a comparison with previous case studies of MSW system design is presented in order to show the applicability and adaptability of the generic model in practical decision-making process, and the perspectives of future study are also discussed.     

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.     

Why recycle? A comparison of recycling motivations in four communities

Four Illinois communities with different sociode-mographic compositions and at various stages of planning for solid waste management were surveyed to determine the influence of sociodemographic variables and planning stages on the factors that motivate recycling behavior. A factor analysis of importance ratings of reasons for recycling and for not recycling yielded five factors interpreted as altruism, personal inconvenience, social influences, economic incentives, and household storage. The four communities were shown to be significantly different in multivariate analyses of the five motivational factors. However, attempts to explain these community differences with regression analyses, which predicted the motivational factors with dummy codes for planning stages, a measure of self-reported recycling behavior, and sociodemographic measures were unsatisfactory. Contrary to expectation, the solid waste management planning stages of the cities (curbside pickup, recycling dropoff center, and planning in progress) contributed only very slightly to the prediction of motivational factors for recycling. Community differences were better explained by different underlying motivational structures among the four communities. Altruistic reasons for recycling (e.g., conserving resources) composed the only factor which was similar across the four communities. This factor was also perceived to be the most important reason for recycling by respondents from all four communities. The results of the study supported the notion that convenient, voluntary recycling programs that rely on environmental concern and conscience for motivation are useful approaches to reducing waste.     

Solid waste management in urban areas: Development and application of a decision support system

A decision support system (DSS) developed to assist the planner in decisions concerning the overall management of solid waste at a municipal scale is described. The DSS allows to plan the optimal number of landfills and treatment plants, and to determine the optimal quantities and the characteristics of the refuse that has to be sent to treatment plants, to landfills and to recycling. The application of the DSS is based on the solution of a constrained non-linear optimization problem. Various classes of constraints have been introduced in the problem formulation, taking into account the regulations about the minimum requirements for recycling, incineration process requirements, sanitary landfill conservation, and mass balance. The cost function to be minimized includes recycling, transportation and maintenance costs. The DSS has been tested on the municipality of Genova, Italy, and the results obtained are presented.