Location-Allocation and Scheduling of Inbound and Outbound Trucks in Multiple Cross-Dockings Considering Breakdown Trucks

محورهای موضوعی : Business and Marketing

javad Behnamian ¹ , Seyed Mohammad Taghi Fatemi Ghomi ² , Fariborz Jolai ³ , Pooya Heidary ⁴

1 - Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
2 - Professor, Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.
3 - Professor, Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran
4 - MSc, Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran.

تاریخ دریافت : 1394/12/04 تاریخ پذیرش : 1396/08/29 تاریخ انتشار : 1396/12/10

کلید واژه: Cross-docking, Transhipment, Location of cross-docking centers, Metaheuristic,

چکیده مقاله :

This paper studies multiple cross-dockings where the loads are transferred from origins (suppliers) to destinations (customers) through cross-docking facilities. Products are no longer stored in intermediate depots and incoming shipments are consolidated based on customer demands and immediately delivered to them to their destinations. In this paper, each cross-docking has a covering radius that customers can be served by at least one cross-docking provided. In addition, this paper considers the breakdown of trucks. We present a two-stage model for the location of cross-docking centers and scheduling inbound and outbound trucks in multiple cross-dockings.We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and route them to the customers via cross-docking facilities. The objective, in the first stage, is to minimize transportation cost of delivering products from suppliers to open cross-docks and cross-docks to the customers; in the second-stage, the objective is to minimize the makespans of open cross-dockings and the total weighted summation of completion time. Due to the difficulty of obtaining the optimum solution tomedium- and large-scale problems, we ‌propose four types of metaheuristic algorithms, i.e., genetic, simulated annealing, differential evolution, and hybrid algorithms.The result showed that simulated annealing is the best algorithm between the four algorithms.