Investigation Of Genetic Operators And Priority Heuristics for Simulation Based Optimization Of Multi-Mode Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP)
Investigation Of Genetic Operators And Priority Heuristics for Simulation Based Optimization Of Multi-Mode Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP)
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DOI：
10.7148/2016-0481
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发表时间：
2016-06
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通讯作者：
Mathias Kühn;Taiba Zahid;Michael Völker;Zhugen Zhou;O. Rose
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作者：
Mathias Kühn;Taiba Zahid;Michael Völker;Zhugen Zhou;O. Rose
多模式资源受限多项目调度问题(MMRCMPSP)等NP-Hard问题的求解需要高效的搜索和优化策略。不同方法的组合，例如用于模式分配的元启发式(遗传算法)和用于作业选择的启发式(优先级规则)，允许两步求解过程。在本文中，我们提出了这样一种求解MMRCMPSP的方法，并用一个基于仿真的优化工具来实现。我们考察了算法特定参数的影响，以找出哪些参数对MMRCMPSP的结果影响最大。
Solving NP-hard Problems like Multi-Mode Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP) needs efficient search and optimization strategies. The combination of different approaches such as a meta-heuristic (Genetic Algorithm) for the mode assignment and a Heuristic (Priority Rules) for the job selection allows a 2-step solving-process. In this paper, we present such an approach for solving MMRCMPSP implemented with a simulation-based optimization tool. We investigate the influence of specific parameters of the algorithm to figure out which parameters mostly affect the result of MMRCMPSP.