| Issue |
RAIRO-Oper. Res.
Volume 59, Number 5, September-October 2025
|
|
|---|---|---|
| Page(s) | 2657 - 2681 | |
| DOI | https://doi.org/10.1051/ro/2025050 | |
| Published online | 12 September 2025 | |
Integrated scheduling optimization of production and shipping schemes for multi-variety and small-batch orders
1
School of Management, Hefei University of Technology, Hefei, Anhui 230009, P.R. China
2
Computer Center, Gree Electric Appliances Inc. of Zhuhai, Zhuhai 519000, P.R. China
3
China Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, Anhui 230009, P.R. China
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
6
May
2023
Accepted:
10
April
2025
This paper explores the integrated scheduling optimization of production and shipping schemes for multi-variety and small-batch orders. It considers the challenges posed by cross-regional distribution of factories, less-than-container-loading (LCL), and destination port restrictions, and develops a mathematical model to minimize the comprehensive cost of all orders. A heuristic search based adaptive genetic algorithm (HSAGA) is proposed, which includes the heuristic generation rule for chromosomes, constraint checking and adjustment strategy, layered crossover, guided mutation operator and adaptive reboot mechanism to improve genetic algorithm. The proposed algorithm is compared with Gurobi on small-size problems. Furthermore, an ablation study is conducted to explore the role of improved strategies and operators. The effectiveness of the integrated optimization strategy is analyzed in detail by comparing with two typical strategies in enterprises, and the specific situation of each cost is dissected with a typical case.
Mathematics Subject Classification: 90B06 / 90C05 / 90C27
Key words: Multi-variety and small-batch order / production / shipping schemes / heuristic search
© The authors. Published by EDP Sciences, ROADEF, SMAI 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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