Title: Particle swarm optimisation for truck scheduling problem in cross docking network

Authors: Warisa Wisittipanich; Takashi Irohara; Piya Hengmeechai

Addresses: Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, 239 Huay Keaw Road, Suthep, Muang, Chiang Mai 50200, Thailand; Excellence Center in Logistics and Supply Chain Management, Faculty of Engineering, Chiang Mai University, 239 Huay Keaw Road, Suthep, Muang, Chiang Mai 50200, Thailand ' Department of Information and Communication Sciences, Faculty of Science and Technology, Sophia University, 7-1 Kioicho, Chiyoda, Tokyo 102-0094, Japan ' Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, 239 Huay Keaw Road, Suthep, Muang, Chiang Mai 50200, Thailand; Excellence Center in Logistics and Supply Chain Management, Faculty of Engineering, Chiang Mai University, 239 Huay Keaw Road, Suthep, Muang, Chiang Mai 50200, Thailand

Abstract: In cross docking network, multiple products from multiple origins are transferred by trucks through one or more cross docks. One critical concern is the decision on how to synchronise product transshipment through multiple cross docks to achieve timely shipment. This paper presents a mathematical model of truck scheduling problem in cross docking network in order to minimise makespan. Since the problem is NP-hard, a solution method is developed based on particle swarm optimisation (PSO) with two solution representations: randomised truck solution representation (Ra-SR) and prioritised truck solution representation (Pr-SR). The results show that the PSO-based approach performs well in solving the problem. Both solution representations are proven effective when comparing the solution quality and computational time with optimal results obtained from LINGO. However, the Pr-SR yields superior results to the Ra-SR in terms of solution quality and convergence behaviour for most instances especially in the case of large-size problems.

Keywords: truck scheduling; cross docking network; particle swarm optimisation; PSO; makespan.

DOI: 10.1504/IJISE.2020.107778

International Journal of Industrial and Systems Engineering, 2020 Vol.35 No.3, pp.345 - 371

Received: 23 Jan 2018
Accepted: 04 Dec 2018
Published online: 17 Jun 2020 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article