Solving a bi-objective location routing problem by a NSGA-II combined with clustering approach: application in waste collection problem

Subject Areas : Mathematical Optimization

Masoud Rabbani ¹ , Hamed Farrokhi-Asl ² , Bahare Asgarian ³

1 - School of Industrial Engineering, College of Engineering, University of Tehran, P.O.Box: 11155-4563, Tehran, Iran
2 - School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
3 - Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran

Received: 2020-10-04 Accepted : 2020-10-04 Published : 2017-03-01

Keywords: Waste collection problem  Treatment facility , Location routing problem  Multi objective optimization,

Abstract :

It is observed that the separated design of location for depots and routing for servicing customers often reach a suboptimal solution. So, solving location and routing problem simultaneously could achieve better results. In this paper, waste collection problem is considered with regard to economic and societal objective functions. A non-dominated sorting genetic algorithm (NSGA-II) is used to locate depots and treatment facilities and design the routes starting from depots to serve customers. A new mathematical model is proposed and two objective functions including economic objective (opening cost of depots and treatment facility and transportation cost) and societal objective; that is, negative impact of treatment facilities which are close to towns are addressed in this study. A straightforward order based solution representation is applied for coding solutions of the problem and clustering approach is used to generate appropriate initial solutions. Moreover, three multi-objective decomposition methods including weighted sum, goal programming, and goal attainment are applied to validate the performance of the proposed algorithm. Number of test problems are conducted and the results obtained by algorithms are compared with respect to some comparison metrics. Finally, the experimental results show that the proposed hybrid NSGA-II outperforms all decomposition methods, but the computational times for decomposition methods are less than NSGA-II.