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Comparative analysis of swarm intelligence and heuristic priority rules for solving multi-project scheduling problem

机译:群体智能与启发式优先规则求解多项目调度问题的比较分析

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Multi-project management is very complex task, which often involves the integration of varieties of resources and schedules. In recent years, many researchers have proposed many techniques for project scheduling problems. However, due to its complexity, it is difficult to find out the best scheduling scheme using heuristics or non-traditional techniques. In this paper, the aim is to find out the high-performance algorithms to solve combination optimisation problems. Related comparative analysis of swarm intelligence and heuristic priority rules for scheduling a multi-project environment with an objective to minimise the makespan of the projects is explored. Firstly, the multi-mode resource constrained multi-project scheduling problem (MRCMPSP) and its conceptual model are proposed. Then, swarm intelligence and heuristic priority rules to the MRCMPSP problem are discussed. Thirdly, the simulation experiments are given in order to test the different searching abilities for various scale scheduling problems using these two methods. Finally, some interesting insights are also illustrated through numerical examples.
机译:多项目管理是一项非常复杂的任务,通常涉及各种资源和进度表的集成。近年来,许多研究人员提出了许多解决项目计划问题的技术。但是,由于其复杂性,很难使用启发式或非传统技术来找到最佳的调度方案。本文的目的是找出解决组合优化问题的高性能算法。探索了用于调度多项目环境的群体智能和启发式优先级规则的相关比较分析,目的是最小化项目的完成时间。首先,提出了多模式资源受限的多项目调度问题(MRCMPSP)及其概念模型。然后,讨论了针对MRCMPSP问题的群体智能和启发式优先级规则。第三,给出了仿真实验,以测试使用这两种方法对各种规模调度问题的不同搜索能力。最后,还通过数值示例说明了一些有趣的见解。

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