Efficient optimization of robust project scheduling for industry 4.0: A hybrid approach based on machine learning and meta-heuristic algorithms

IF 9.8 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
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引用次数: 0

Abstract

This research contributes significantly to the domain of Industry 4.0 by offering a nuanced approach to the multi-objective optimization of the resource-constrained project scheduling problem (RCPSP) under uncertainty. Focused on the context of smart product platforming, this study introduces a novel methodology that not only considers traditional factors like time and cost but also incorporates quality and risk aspects, crucial for personalized product fulfillment. In this regard, a comprehensive four-objective mathematical model is proposed to minimize project completion time, total project costs, and project risks while simultaneously enhancing overall project quality. Real-world uncertainty is acknowledged through the incorporation of uncertain parameters for the time, risk, and quality associated with each project activity. To address this uncertainty, a robust optimization method is applied based on Bertsimas and Sim's approach. Moreover, to optimize the proposed model, the Hybrid Red Deer and Genetic Algorithm (HRDGA) is proposed, which is leveraging a machine learning approach for clustering solutions. The numerical results demonstrate that increasing the project budget by 30% leads to an upward trend in total project costs and a reduction in the minimum acceptable quality by 10%–30% results in a decreasing trend in the total project cost. This research emphasizes the adoption of Industry 4.0 enabling technology within the project scheduling platform, particularly highlighting its significance for personalized product fulfillment.
高效优化工业 4.0 的稳健项目调度:基于机器学习和元启发式算法的混合方法
本研究为不确定性条件下资源受限项目调度问题(RCPSP)的多目标优化提供了一种细致入微的方法,为工业 4.0 领域做出了重大贡献。本研究以智能产品平台化为重点,引入了一种新颖的方法,不仅考虑了时间和成本等传统因素,还纳入了质量和风险方面的因素,这对个性化产品的实现至关重要。为此,本研究提出了一个全面的四目标数学模型,以最大限度地减少项目完成时间、项目总成本和项目风险,同时提高整体项目质量。通过纳入与每个项目活动相关的时间、风险和质量的不确定参数,承认了现实世界的不确定性。为了解决这种不确定性,采用了基于 Bertsimas 和 Sim 方法的稳健优化方法。此外,为了优化所提出的模型,还提出了混合红鹿和遗传算法(HRDGA),该算法利用机器学习方法对解决方案进行聚类。数值结果表明,项目预算增加 30% 会导致项目总成本呈上升趋势,而最低可接受质量降低 10%-30% 则会导致项目总成本呈下降趋势。这项研究强调了在项目调度平台中采用工业 4.0 使能技术,尤其突出了其对个性化产品实现的重要意义。
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来源期刊
International Journal of Production Economics
International Journal of Production Economics 管理科学-工程:工业
CiteScore
21.40
自引率
7.50%
发文量
266
审稿时长
52 days
期刊介绍: The International Journal of Production Economics focuses on the interface between engineering and management. It covers all aspects of manufacturing and process industries, as well as production in general. The journal is interdisciplinary, considering activities throughout the product life cycle and material flow cycle. It aims to disseminate knowledge for improving industrial practice and strengthening the theoretical base for decision making. The journal serves as a forum for exchanging ideas and presenting new developments in theory and application, combining academic standards with practical value for industrial applications.
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