An exploration of the shift work consideration in production scheduling

IF 3.7 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-10-01 DOI:10.1016/j.jocs.2025.102724

Kuo-Ching Ying , Pourya Pourhejazy , Shih-Cheng Lin

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引用次数: 0

Abstract

Scheduling problems predominantly assume that the same operators work fixed shifts during the day and night. Scheduling with a single-shift approach can result in infeasible or suboptimal production planning solutions when a multiple-shift system is implemented. This study introduces a new scheduling extension that incorporates shift work constraints. A new mathematical model based on the Permutation Flowshop Scheduling Problem is proposed, and the Iterated Greedy algorithm is adapted to solve it. The objective is to minimize the maximum completion time (makespan) and thereby improve the system performance while considering shift work constraints. Experiments reveal that the overall response time in 10-hour and 12-hour shifts is better than that of 8-hour shifts, despite the shorter overall active hours on the shop floor. Additional experiments confirm that the proposed Adjusted Iterated Greedy algorithm outperforms the Variable Neighbourhood Search algorithm in solving medium- and large-scale problems.

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生产调度中班次考虑的探讨

调度问题主要假设相同的操作员在白天和晚上轮班工作。当实施多班制时，单班制调度可能导致不可行或不理想的生产计划解决方案。本研究引入了一个包含轮班工作约束的新的调度扩展。提出了一种新的基于置换流水车间调度问题的数学模型，并采用迭代贪心算法求解该问题。目标是最小化最大完成时间（makespan），从而在考虑轮班工作约束的情况下提高系统性能。实验表明，10小时班和12小时班的总体反应时间优于8小时班，尽管车间的总体活动时间较短。实验结果表明，本文提出的调整迭代贪心算法在求解中、大规模问题时优于可变邻域搜索算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).