Title: Improving cross-docking systems by load integration: meta-heuristics approach

Authors: Zohreh Khalilpourshiraz; Mahdi Yousefi Nejad Attari; Sajjad Gozalzadeh

Addresses: Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran ' Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran ' Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran

Abstract: In order to reduce costs and increase the efficiency of the supply chain system, cross-docking is one of the essential warehousing management strategies to combine products from different suppliers to different customers. In this study, a particular state of cross-docking is considered in which inbound trucks can also be used as outbound trucks while it brings benefits such as reduced unloading, loading, and truck rental costs. In this state of the problem, a mathematical model has been developed to obtain the appropriate answer. Also, we solve the model and determine the accuracy of modelling from optimisation software such as GAMS and MATLAB. Also, we consider meta-heuristic algorithms, and the experimental results demonstrate the ability to compete with the proposed different meta-heuristic algorithms. They can improve the best-known solutions for occurrences inbound and obtain better outcomes for the ant lion optimiser than two other algorithms, with an average improvement of 53.6%. The proposed meta-heuristic outperforms a standard ant lion optimiser algorithm on more significant capacity instances, maintaining solution quality within reasonable CPU times.

Keywords: cross-docking systems; mixed-integer programming; meta-heuristics optimisation; truck schedule.

DOI: 10.1504/IJVCM.2023.135621

International Journal of Value Chain Management, 2023 Vol.14 No.4, pp.377 - 397

Received: 11 Aug 2021
Accepted: 23 Jan 2022
Published online: 19 Dec 2023 *

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