A robust bi-level optimization modelling approach for municipal solid waste management; a real case study of Iran. (10th December 2019)

• Record Type: Journal Article
• Authors: Pouriani, Sepideh; Asadi-Gangraj, Ebrahim; Paydar, Mohammad Mahdi
• Is Part Of: Journal of cleaner production. Volume 240(2019)
• Publication Date: 2019-12-10
• Subjects: Municipal solid waste -- Reverse logistics -- MILP model -- Bi-level optimization -- Scenario-based robust; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.118125
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: In recent years, reverse logistics have attracted many attentions due to many reasons, such as extension of new environmental laws, development of social responsibility, and economic interests. Reduction in natural resources and raw material reserves, increasing production costs, and the problems caused by an industrial waste landfill and consumer goods have also increased the focus on reverse logistics over the past decade. This paper proposes a municipal solid waste management network in order to minimize different costs. We develop a bi-level mixed integer linear programming model. The lower-level involves location and establishment costs of solid waste collection stations, and the upper-level includes the allocation of waste to the various centers. In order to deal with uncertainty in the amount of collected solid waste, we need to take a scenario-based robust optimization approach into account. The proposed model is evaluated using a case study in Babol, Mazandaran province, Iran. The results indicate that the collection stations are selected in regions that have less distance from their covered regions which leads to the optimal flow of the wastes/products. The proposed robust optimization approach ensures less sensitivity to model solutions for the provided scenarios. In addition, considering hierarchy in this model leads to a more appropriate analysis compared to when a multi-objective model is used.