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Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem

机译:具有约束力的粒子群优化算法和资源受限项目调度问题的设计机制

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The studied resource-constrained project scheduling problem (RCPSP) is a classical well-known problem which involves resource, precedence, and temporal constraints and has been applied to many applications. However, the RCPSP is confirmed to be an NP-hard combinatorial problem. Restated, it is hard to be solved in a reasonable time. Therefore, there are many metaheuristics-based schemes for finding near optima of RCPSP were proposed. The particle swarm optimization (PSO) is one of the metaheuristics, and has been verified being an efficient nature-inspired algorithm for many optimization problems. For enhancing the PSO efficiency in solving RCPSP, an effective scheme is suggested. The justification technique is combined with PSO as the proposed justification particle swarm optimization (JPSO), which includes other designed mechanisms. The justification technique adjusts the start time of each activity of the yielded schedule to further shorten the makespan. Moreover, schedules are generated by both forward scheduling particle swarm and backward scheduling particle swarm in this work. Additionally, a mapping scheme and a modified communication mechanism among particles with a designed gbest ratio (GR) are also proposed to further improve the efficiency of the proposed JPSO. Simulation results demonstrate that the proposed JPSO provides an effective and efficient approach for solving RCPSP.
机译:研究的资源受限项目调度问题(RCPSP)是一个经典的众所周知的问题,它涉及资源,优先级和时间约束,已被应用到许多应用中。但是,RCPSP被确认是一个NP-hard组合问题。重述后,很难在合理的时间内解决。因此,提出了许多基于元启发式的RCPSP最优方案。粒子群优化(PSO)是元启发式算法之一,并且已被证明是一种针对许多优化问题的高效自然灵感算法。为了提高PSO解决RCPSP的效率,提出了一种有效的方案。证明技术与PSO相结合,成为提出的证明粒子群优化(JPSO),其中包括其他设计的机制。调整技术会调整产生进度表的每个活动的开始时间,以进一步缩短工期。此外,在这项工作中,调度是通过前向调度粒子群和后向调度粒子群生成的。此外,还提出了具有设计最大比率(GR)的粒子之间的映射方案和改进的通信机制,以进一步提高所提出的JPSO的效率。仿真结果表明,所提出的JPSO提供了一种解决RCPSP的有效途径。

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