This paper presents a model to solve the multi-objective location-routing problem with capacitated vehicles. The main purposes of the model are to find the optimal number and location of depots, the optimal number of vehicles, and the best allocation of customers to distribution centers and to the vehicles. | A multi-depot location routing problem to reduce the differences between the vehicles traveled distances a comparative study of heuristics Uncertain Supply Chain Management 7 2019 17 32 Contents lists available at GrowingScience Uncertain Supply Chain Management homepage uscm A multi-depot location routing problem to reduce the differences between the vehicles traveled distances a comparative study of heuristics Hengameh Hadiana Amir-Mohammad Golmohammadib Akbar Hemmatic and Omolbanin Mashkanid a Department of Industrial Engineering University of Nahavand Nahavand Iran b Department of Industrial Engineering Yazd University Yazd Iran c Department of Industrial Engineering K. N. Toosi University of Technology Tehran Iran d University of Technology Sydney Sydney Australia CHRONICLE ABSTRACT Article history This paper presents a model to solve the multi-objective location-routing problem with Received February 2 2018 capacitated vehicles. The main purposes of the model are to find the optimal number and Accepted June 7 2018 location of depots the optimal number of vehicles and the best allocation of customers to Available online distribution centers and to the vehicles. In addition the model seeks to optimize vehicle routes June 7 2018 Keywords and sequence to serve the customers. The proposed model considers vehicles traveled Location routing problem LRP distances service time and waiting time while guaranteeing that the sum of these parameters Vehicle routing Facility location is lower than a predetermined value. Two objective functions are investigated. First objective Imperialist competitive algorithm function minimizes the total cost of the system and the second one minimizes the gap between ICA the vehicles traveled distances. To solve the problem a Multi-Objective Imperialist NSGA-II Competitive Algorithm MOICA is developed. The efficiency of the MOICA is demonstrated via comparing with a famous meta-heuristics named Non-Dominated .
