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

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 del...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of optimization in industrial engineering 2018-03, Vol.11 (1), p.51-65
Hauptverfasser: javad Behnamian, Seyed Mohammad Taghi Fatemi Ghomi, Fariborz Jolai, Pooya Heidary
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:2251-9904
2423-3935
DOI:10.22094/joie.2017.594.1382