Resource allocation models and heuristics for the multi-project scheduling with global resource transfers and local resource constraints

IF 6.5 1区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Industrial Engineering Pub Date : 2025-02-01 Epub Date: 2025-01-02 DOI:10.1016/j.cie.2024.110843
Wanjun Liu , Jingwen Zhang , Mario Vanhoucke , Weikang Guo
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Abstract

The transfer times and costs of global resources between different projects and the choice of transfer modes significantly affect the multi-project scheduling. This paper investigates four versions of the resource-constrained multi-project scheduling problem with global resource transfers and local resource constraints based on four realistic transfer scenarios, in which the global resource transfer times and costs are considered with a single transfer mode or multiple transfer modes. Three classes of heuristics with huge amount of priority rules are adapted and tested for the new problems. The schedule generation schemes of each class of heuristics are improved from two aspects. On the one hand, resource availability checks are divided into global and local phases due to their different characteristics. On the other hand, resource transfer rules and transfer mode rules are introduced to deal with resource transfer and transfer mode issues, respectively. The three class of heuristics are tested on well-known datasets of the multi-project problem, which are extended with transfer data using a transfer time/cost generation procedure. The numerical experiments first evaluate the performance of a set of priority rules, then effectively apply the priority rule heuristics in the genetic algorithm, and finally compare the performance of the priority rule heuristics with CPLEX on small-scale instances. Additionally, a multi-project case study verifies the applicability and good performance of priority rules that perform well in numerical experiments. Furthermore, the best performing rules are used by two machine learning methods in literature to automatically select the most promising ones.
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具有全局资源转移和局部资源约束的多项目调度的资源分配模型及启发式方法
全局资源在不同项目间的转移时间和成本以及转移方式的选择对多项目调度有重要影响。本文在考虑全局资源转移和局部资源约束的四种实际转移场景下,分别考虑单转移模式和多转移模式下的全局资源转移时间和成本的情况下,研究了四种版本的具有全局资源转移和局部资源约束的资源约束多项目调度问题。针对新问题,采用了三种具有大量优先级规则的启发式算法,并对其进行了测试。从两个方面改进了每一类启发式算法的调度生成方案。一方面,由于资源可用性检查的特点不同,将其分为全局阶段和局部阶段。另一方面,引入资源转移规则和转移方式规则,分别处理资源转移和转移方式问题。在已知的多项目问题数据集上对这三类启发式算法进行了测试,并使用传输时间/成本生成过程对传输数据进行了扩展。数值实验首先评价了一组优先规则的性能,然后将优先规则启发式算法有效地应用于遗传算法中,最后在小尺度实例上比较了优先规则启发式算法与CPLEX算法的性能。通过多工程实例研究,验证了优先级规则的适用性和良好的性能。此外,文献中的两种机器学习方法使用表现最好的规则来自动选择最有前途的规则。
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来源期刊
Computers & Industrial Engineering
Computers & Industrial Engineering 工程技术-工程:工业
CiteScore
12.70
自引率
12.70%
发文量
794
审稿时长
10.6 months
期刊介绍: Computers & Industrial Engineering (CAIE) is dedicated to researchers, educators, and practitioners in industrial engineering and related fields. Pioneering the integration of computers in research, education, and practice, industrial engineering has evolved to make computers and electronic communication integral to its domain. CAIE publishes original contributions focusing on the development of novel computerized methodologies to address industrial engineering problems. It also highlights the applications of these methodologies to issues within the broader industrial engineering and associated communities. The journal actively encourages submissions that push the boundaries of fundamental theories and concepts in industrial engineering techniques.
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