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Research on a resource-constrained project scheduling problem in a hazardous environment and its staffing strategies based on PSO algorithm

机译:基于PSO算法的危险环境中资源受限项目调度问题研究及其人员配置策略

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摘要

We study a resource-constrained project scheduling problem in a hazardous environment considering some different strategies of staffing. An overhaul project of a nuclear power plant is chosen as a typical example. The same as the conventional projects scheduling, the problem is constrained by the availability of resources. However, due to the unique working environment in this project, the availability of resources is constrained by the accumulated amount of harm that the workers could withstand. As this extremely increases the complexity of the problem. In order to address the investigated problem, we propose a novel particle swarm optimization algorithm: probable mechanism-based discrete particle swarm optimization algorithm (PMPSO). The PMPSO algorithm is a discretization form of the traditional particle swarm optimization (PSO) algorithm. We use the PMPSO algorithm to solve the problem thinking of nine combinations of staffing strategies respectively. Comparison experiments of the combination of staffing strategies show that strategy of ‘3’ outperforms the other strategies. Numerical experiments indicate the adaptability of the PMPSO algorithm and the validity of the conclusion.
机译:我们在危险环境中考虑人员的一些不同的策略,研究了资源受限项目调度问题。核电厂的大修项目被选为一个典型的例子。同样作为常规的项目调度,该问题由资源的可用性的限制。然而,由于在这个项目独特的工作环境,资源的可用性被伤害的职工可以承受的累计金额限制。由于这极大地增加了问题的复杂性。为了解决所研究的问题,我们提出了一个新颖的粒子群优化算法:可能基于机制的离散粒子群优化算法(PMPSO)。所述PMPSO算法是传统的粒子群优化(PSO)算法的离散形式。我们使用PMPSO算法分别解决人员编制战略,九组组合问题的思考。人员配置策略相结合的比较实验结果表明“3”优于其他战略的这一战略。数值试验表明PMPSO算法的适应性与结论的正确性。

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