Title: Applying a modified adaptive large neighbourhood search for truck scheduling and pile assignment in a two-stage sorting system

Authors: James C. Chen; Tzu-Li Chen; Yin-Yann Chen; Yung-Hsin Su

Addresses: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, 30013, Taiwan ' Graduate Institute of Intelligent Manufacturing Technology, National Taiwan University of Science and Technology, Taipei, 106335, Taiwan ' Department of Industrial Management, National Formosa University, Yunlin, 632301, Taiwan ' Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, 30013, Taiwan

Abstract: In this study, we tackle the complexities of a two-stage semi-automatic sorting system, considering the diverse distribution requirements of parcels and the constraints imposed by sorting equipment. Our objective is to integrate two decision points - the inbound truck schedule and the parcel sorting plan - to minimise overall operational costs. We first formulate the problem using a mixed-integer linear programming model and then propose a mixed-coded modified adaptive large neighbourhood search (MCMALNS) algorithm to enhance performance. In our computational study, the proposed approach demonstrated the ability to quickly obtain high-quality solutions compared to other algorithms. Furthermore, a full factorial experiment was conducted to analyse cost variations across 36 scenarios. Factors including loading, deadline, arrival pattern, pile/commodity ratio, and algorithm were all identified as significant and exhibited considerable influence on the outcomes. The insights derived from this analysis provide valuable guidance for management personnel in decision-making. [Received: 30 January 2023; Accepted: 25 August 2023]

Keywords: truck scheduling; two-stage sorting system; modified adaptive large neighbourhood search.

DOI: 10.1504/EJIE.2025.144705

European Journal of Industrial Engineering, 2025 Vol.19 No.2, pp.128 - 161

Received: 30 Jan 2023
Accepted: 25 Aug 2023
Published online: 28 Feb 2025 *

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