A fuzzy multi-objective optimization model for a sustainable reverse logistics network design of municipal waste-collecting considering the reduction of emissions

Abstract

The most important reason for waste collection is the protection of the environment and the health of the population. Reverse logistics is applied in the sustainable management of municipal waste and is used in the collection, recycling and reuse, as well as the reduction of consumables and environmental compatibility. One of the challenges of sustainable management is costs and customer demand must be considered simultaneously. In this paper, we try to address a comprehensive approach by applying fuzzy mathematical programming to design a multi-objective model for a reverse logistics network. To cover all aspects of this system, we tried to minimize the cost of facility construction, vehicle fuel and environmental damage from the emission of polluting gases, as well as minimize the sum of the ratio of unanswered customer demand to the amount of their demand for all periods, as objective functions of the model. In order to obtain solutions on the Pareto front, a customized multi-objective genetic algorithm (NSGAII) and a customized bee colony algorithm (BCO) were applied. The results of the two algorithms according to the indicators of quality comparison, spacing, diversification and solution time have been compared. The results showed that in all cases, the bee colony algorithm was better able to explore and extract the area to a feasible solution and to achieve near-optimal answers. In terms of spacing metric and resolution time, the genetic algorithm performed better than the bee algorithm.