A genetic algorithm for tool-constrained scheduling with dynamic expedited orders in smart manufacturing

Yi-Han Chen; Hsi-Wen Wang; Chiung-Hui Tsai; Chih-Hung Wu; Yi-Han Chen; Hsi-Wen Wang; Chiung-Hui Tsai; Chih-Hung Wu

doi:10.3934/jimo.2026007

Journal of Industrial and Management Optimization

2026, Volume 22, Issue 1: 182-213. doi: 10.3934/jimo.2026007

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A genetic algorithm for tool-constrained scheduling with dynamic expedited orders in smart manufacturing

1.
Department of Electrical Engineering, National University of Kaohsiung, 700, Kaohsiung University Rd., Nanzih District, 81148, Kaohsiung, Taiwan
2.
Department of Information Engineering, Da-Yeh University, 168, Xuefu Rd., Dacun Township, 515006, Changhua County, Taiwan

Received: 17 July 2025 Revised: 16 October 2025 Accepted: 22 October 2025 Published: 18 November 2025
90B35, 68W50

This paper addresses a practical scheduling challenge in smart manufacturing systems, where dynamic expedited orders and tool change constraints must be handled in real time. While deep learning and multi-objective optimization methods offer strong theoretical capabilities, they are often unsuitable for industrial deployment due to high computational demands, training costs, and lack of transparency. We propose a lightweight, interpretable genetic algorithm framework that incorporates a real-time insertion mechanism and tooling-aware encoding to minimize disruptions when handling expedited orders. The method jointly considers machine-tool compatibility, job priorities, and system stability. It is validated using real production data from a precision screw manufacturing plant, demonstrating superior performance in handling expedited orders and tool constraints. The proposed method requires no training, supports online adjustments, and is well-suited for deployment in real-world production settings.
- flexible job shop scheduling problem (FJSSP),
- expedited orders,
- tool constraints,
- genetic algorithm,
- job priority,
- scheduling decision support
Citation: Yi-Han Chen, Hsi-Wen Wang, Chiung-Hui Tsai, Chih-Hung Wu. A genetic algorithm for tool-constrained scheduling with dynamic expedited orders in smart manufacturing[J]. Journal of Industrial and Management Optimization, 2026, 22(1): 182-213. doi: 10.3934/jimo.2026007

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Abstract

This paper addresses a practical scheduling challenge in smart manufacturing systems, where dynamic expedited orders and tool change constraints must be handled in real time. While deep learning and multi-objective optimization methods offer strong theoretical capabilities, they are often unsuitable for industrial deployment due to high computational demands, training costs, and lack of transparency. We propose a lightweight, interpretable genetic algorithm framework that incorporates a real-time insertion mechanism and tooling-aware encoding to minimize disruptions when handling expedited orders. The method jointly considers machine-tool compatibility, job priorities, and system stability. It is validated using real production data from a precision screw manufacturing plant, demonstrating superior performance in handling expedited orders and tool constraints. The proposed method requires no training, supports online adjustments, and is well-suited for deployment in real-world production settings.

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