Abstract
A fuzzy random conditional value-at-risk-based linear programming (FCVLP) model was proposed in this study for dealing with municipal solid waste (MSW) management problems under uncertainty. FCVLP improves upon the existing fuzzy linear programming and fuzzy random conditional value-at-risk methods by allowing analysis of the risks of violating constraints that contain fuzzy parameters. A long-term MSW management problem was used to illustrate the applicability of FCVLP. The optimal feasibility solutions under various significance risk levels could be generated in order to analysis the trade-offs among the system cost, the feasibility degree of capacity constraints, and the risk level of waste-disposal-demand constraints. The results demonstrated that (1) a lower system cost may lead to a lower feasibility of waste-facility-capacity constraint and a higher risk of waste-disposal-demand constraint; (2) effects on system cost from vague information in incinerator capacity inputs would be greater than those in landfill capacity inputs; (3) the total allowable waste allocation would vary significantly because of the variations of risk levels and feasibility degrees. The proposed FCVLP method could be used to identify optimal waste allocation scenarios associated with a variety of complexities in MSW management systems.
Highlights
Due to rising waste generation rates, municipal solid waste (MSW) management is still a major challenge for urban development and planning throughout the world [1,2,3]
The relation between system cost feand feasibility degree ω corresponds to a trade-off between system cost and degrees of capacity feasibility, and the relation between system cost feand risk level β corresponds to a trade-off between system cost and the risk level of the waste-disposal-demand constraint
It can deal with uncertainty presented as fuzzy sets, and tackle problems with respect to trade-offs among the system cost, the risk level of demand constraints, and the feasibility degree of capacity constraints
Summary
Due to rising waste generation rates, municipal solid waste (MSW) management is still a major challenge for urban development and planning throughout the world [1,2,3]. The contradiction between decreasing capacities of waste disposal and increasing rates of waste generation is becoming more acute than before [4,5]. In response to this concern, there is increasing interest in developing effective optimization models for MSW management problems. Numbers of MSW-management models were developed by using linear programming (LP) methods [6,7,8,9,10,11,12]. Baetz [8] formulated a mixed-integer linear programming model to generate optimal facility-expansion patterns for MSW management problems. Harijani et al [12] developed a multi-objective mixed-integer linear programming model in a MSW management system
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