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Application of genetic algorithms in resource constrained network optimization

机译:遗传算法在资源受限网络优化中的应用

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There are limited solution techniques available for resource constrained project scheduling problems with stochastic task durations. Due to computational complexity, scheduling heuristics have been found useful for large deterministic problems. In this paper, the authors demonstrate the use of a genetic algorithm to optimize over a linear combination of scheduling heuristics. A simulation model is used to evaluate the performance of each combination of the heuristics selected by the genetic algorithm, and this performance information is used by the genetic algorithm to select the next combinations to evaluate. The genetic algorithm and simulation based approach is demonstrated using a multiple resource constrained project scheduling problem with stochastic task durations.
机译:对于具有随机任务工期的资源受限的项目计划问题,可用的解决方案技术有限。由于计算复杂性,已发现调度启发式方法对于大型确定性问题很有用。在本文中,作者演示了遗传算法的使用,以优化调度启发式算法的线性组合。仿真模型用于评估由遗传算法选择的启发式方法的每个组合的性能,遗传算法使用此性能信息来选择下一个要评估的组合。使用具有随机任务工期的多资源受限项目调度问题,证明了基于遗传算法和仿真的方法。

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