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Optimization and Simulation of Resource Constrained Scheduling Problem Using Genetic Algorithm

机译:基于遗传算法的资源受限调度问题的优化与仿真

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摘要

Due to the development of management idea and the scarcity of some resources, the lean management has become the necessary request to implement effective control of resource constrained project. Resource constrained project scheduling is the significant guarantee to attain the lean management. The resource constrained project scheduling problem (RCPSP), with the objective of minimizing project duration and with the precedence relations described by an activity-on-arrow (AOA) network, is formulated as a combination optimization problem and solved using the priority-based genetic algorithm (GA). The activity priorities are represented by chromosome and serial scheduling scheme (SSS) and parallel scheduling scheme (PSS) are developed and utilized to transform chromosome-represented priorities to an active schedule subject to the logic and resource constraints so that project duration corresponding to each chromosome can be evaluated. The overall framework of the GA for the RCPSP is developed and the basic components of the algorithm are designed. Simulation is provided so as to investigate the performance of the priority-based GA with SSS and PSS as decoding method, respectively. The optimal solution to a small-sized resource constrained benchmark instance is scheduled to find the shortest project duration. Comparative simulation results demonstrate not only the effectiveness and efficiency of GA with SSS or PSS as decoding methods in solution to RCPSP with precedence relation of activities diagramed as an AOA network but also the effect of different evolution parameter settings on solution quality of the problem.
机译:由于管理思想的发展和某些资源的匮乏,精益管理已成为实施对资源受限项目进行有效控制的必要要求。资源受限的项目计划是实现精益管理的重要保证。资源约束的项目调度问题(RCPSP),以最小化项目工期和以箭头活动(AOA)网络描述的优先关系为目标,被表述为组合优化问题,并使用基于优先级的遗传算法进行求解。算法(GA)。活动优先级由染色体表示,并开发了串行调度方案(SSS)和并行调度方案(PSS),并利用该方案将染色体表示的优先级转换为受逻辑和资源约束的活动调度,以便与每个染色体相对应的项目工期可以评估。开发了用于RCPSP的GA的总体框架,并设计了算法的基本组件。通过仿真,以SSS和PSS作为解码方法分别研究基于优先级的遗传算法的性能。计划针对小型资源受限的基准实例的最佳解决方案,以找到最短的项目工期。对比仿真结果不仅证明了将SSS或PSS作为解码方法的GA在RCPSP解决方案中的有效性和效率,并且将活动的优先级关系描绘为AOA网络,而且还说明了不同进化参数设置对问题解决质量的影响。

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