Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization. (November 2017)

• Record Type: Journal Article
• Authors: Wisittipanich, Warisa; Hengmeechai, Piya
• Is Part Of: Computers & industrial engineering. Volume 113(2017)
• Page Start: 793
• Page End: 802
• Publication Date: 2017-11
• Subjects: Multi-door -- Cross docking -- Particle swarm optimization -- Truck scheduling -- Makespan; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2017.01.004
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A mathematical model for truck scheduling in a multi-door cross docking is shown. The GLNPSO is proposed with particular encoding and decoding schemes. GLNPSO generates high quality solutions with fast convergence. Abstract: In today's distribution environment, one of the main strategies is to minimize cost by reducing inventory and timely shipments. Cross docking is a logistic management strategy in which products delivered to a distribution center by inbound trucks are immediately loaded to outbound trucks with minimum handling and storage time so that the total cost can be reduced. In a multi-door cross docking terminal, one of the most important operational management problems is the truck scheduling problem which is decomposed to the assignment of trucks to dock doors and the sequence of all inbound and outbound trucks. In this paper, a mathematical model of mixed integer programming for door assigning and truck sequencing in a multi-door cross docking system is presented. The objective of the model is to minimize total operational time or makespan. Then, the modified particle swarm optimization, so called GLNPSO, is proposed with particular encoding and decoding schemes for solving the truck scheduling problem in a multi-door cross docking system. The performances of GLNPSO are evaluated and compared the results with those obtained from the original PSO. The experimental results show that the GLNPSO is capable of finding high quality solutions with fast convergence.