The performance of priority rules for the decentralized resource-constrained multi-project scheduling

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems Pub Date : 2024-09-18 DOI:10.1016/j.knosys.2024.112530

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引用次数: 0

Abstract

Decentralized resource-constrained multi-project scheduling (DRCMPSP) is becoming increasingly common in construction, supply chains, and many other industrial disciplines. DRCMPSP faces difficult decisions in resolving resource conflicts to generate a baseline schedule to optimize global objectives. We propose an agent-based approach to address the DRCMPSP based on two global objectives: average project delay and total project delay. A heuristic based on the priority rule (PR) is developed to coordinate the global resource allocation. A comprehensive analysis of 30 PRs was conducted on 16,000 portfolios containing 48,000 projects . We confirmed that using the same PR to allocate global resources on all occasions often results in unnecessarily poor performance. The best PR depends on project and portfolio characteristics such as serial/parallel indicators, global resource distribution, and tightness. Moreover, the best PR differs from various perspectives (e.g., projects and portfolios). We summarized our results in three decision tables and further distilled these results for practical use, which only provide a rough estimate of the project and portfolio characteristics.

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分散式资源受限多项目调度优先规则的性能

分散式资源受限多项目调度（DRCMPSP）在建筑、供应链和许多其他工业领域越来越常见。DRCMPSP 在解决资源冲突以生成优化全局目标的基线计划时面临着困难的决策。我们提出了一种基于代理的方法来解决 DRCMPSP，该方法基于两个全局目标：平均项目延迟和总项目延迟。我们开发了一种基于优先权规则（PR）的启发式方法来协调全局资源分配。我们对包含 48,000 个项目的 16,000 个项目组合中的 30 个 PR 进行了综合分析。我们证实，在所有场合使用相同的 PR 来分配全局资源往往会导致不必要的低绩效。最佳 PR 取决于项目和投资组合的特征，如串行/并行指标、全球资源分配和紧张程度。此外，从不同角度（如项目和投资组合）来看，最佳 PR 也不尽相同。我们在三个决策表中总结了我们的结果，并进一步提炼了这些结果供实际使用，这些结果只提供了对项目和组合特征的粗略估计。

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

Knowledge-Based Systems 工程技术-计算机：人工智能

CiteScore

14.80

自引率

12.50%

发文量

1245

审稿时长

7.8 months

期刊介绍： Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.