Multi-objective fuzzy modeling of project scheduling with limitations of multi-skilled resources able to change skill levels and interrupt activities

Q3 Decision Sciences
M. Salehi, Efat Jabarpoor
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引用次数: 0

Abstract

Project scheduling is one of the most important and applicable concepts of project management. Many project-oriented companies and organizations apply variable cost reduction strategies in project implementation. Considering the current business environments, in addition to lowering their costs, many companies seek to prevent project delays. This paper presents a multi-objective fuzzy mathematical model for the problem of project scheduling with the limitation of multi-skilled resources able to change skills levels, optimizing project scheduling policy and skills recruitment. Given the multi objectivity of the model, the goal programming approach was used, and an equivalent single-objective model was obtained. Since the multi-skilled project scheduling is among the NP-Hard problems and the proposed problem is its extended state, so it is also an NPHard problem. Therefore, NSGA II and MOCS meta-heuristic algorithms were used to solve the large-sized model proposed using MATLAB software. The results show that the multi-objective genetic algorithm performs better than the multi-objective Cuckoo Search in the criteria of goal solution distance, spacing, and maximum performance enhancement.
具有可改变技能水平和中断活动的多技能资源限制的项目进度多目标模糊建模
项目进度是项目管理中最重要、最适用的概念之一。许多以项目为导向的公司和组织在项目实施中应用可变成本降低策略。考虑到当前的商业环境,除了降低成本外,许多公司还寻求防止项目延迟。针对可改变技能水平的多技能资源限制下的项目调度问题,提出了优化项目调度策略和技能招聘的多目标模糊数学模型。考虑模型的多目标性,采用目标规划方法,得到等效的单目标模型。由于多技能项目调度属于NP-Hard问题,所提问题是其扩展状态,因此也是一个NP-Hard问题。因此,采用NSGA II和MOCS元启发式算法对MATLAB软件提出的大型模型进行求解。结果表明,多目标遗传算法在目标解距离、间隔和最大性能增强等指标上优于多目标布谷鸟搜索。
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
International Journal of Industrial Engineering and Production Research
International Journal of Industrial Engineering and Production Research Engineering-Industrial and Manufacturing Engineering
CiteScore
1.60
自引率
0.00%
发文量
0
审稿时长
10 weeks
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