{"title":"分散式资源受限多项目调度优先规则的性能","authors":"","doi":"10.1016/j.knosys.2024.112530","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The performance of priority rules for the decentralized resource-constrained multi-project scheduling\",\"authors\":\"\",\"doi\":\"10.1016/j.knosys.2024.112530\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S095070512401164X\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095070512401164X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
The performance of priority rules for the decentralized resource-constrained multi-project scheduling
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.
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.