A model for distribution centers location-routing problem on a multimodal transportation network with a meta-heuristic solving approach

Subject Areas : Mathematical Optimization

Saeed Fazayeli ¹ , Alireza Eydi ² , Isa Nakhai Kamalabadi ³

1 - Department of Engineering, University of Kurdistan, Pasdaran Blvd., Post Box No.: 416, Sanandaj, Iran
2 - Department of Engineering, University of Kurdistan, Pasdaran Blvd., Post Box No.: 416, Sanandaj, Iran
3 - Department of Engineering, University of Kurdistan, Pasdaran Blvd., Post Box No.: 416, Sanandaj, Iran

Received: 2020-10-06 Accepted : 2020-10-06 Published : 2018-06-01

Keywords: Location, routing problem . Multimodal transportation . Distribution center . Genetic algorithm,

Abstract :

Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.