Using metaheuristic algorithms to solve a multi-objective industrial hazardous waste location-routing problem considering incompatible waste types

Rapid progress in technology is a primary cause of acceleration in the rate of the industrial hazardous waste generation all over the world. Management of hazardous waste has magnetized researcher's attention because of its considerable impacts on the economy, ecology, and the environment. In this regard, this paper addresses a new industrial hazardous waste location-routing problem by putting emphasis on some new aspects in its formulation such as considering restriction about the incompatibility between some kinds of wastes and incorporating routing decisions into the model. Simultaneously minimization of three significant criteria, including total cost, total transportation risk of hazardous waste related to population exposure, and site risk persuades authors to implement two multi-objective evolutionary algorithms, Nondominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) for tackling the problem. The results obtained from experiments on several problem instances confirm the superiority of NSGA-II over MOPSO in terms of most of the evaluation metrics. Therefore, the significance of the paper is firstly the novelty of the model, and secondly, the comparison of two solution methods allows for the identification of the method resulting in the best results.