Workforce‐constrained Preventive Maintenance Scheduling Using Evolution Strategies
研究了进化策略在劳动力约束下最小化预防性维护任务完工时间的调度问题，通过小规模问题验证其最优解能力，并在852个大规模问题中测试了任务特征、劳动力变量和种群大小对计算时间的影响，与模拟退火对比显示更快的收敛速度。
ABSTRACT Heavy equipment overhaul facilities such as aircraft service centers and railroad yards face the challenge of minimizing the makespan for a set of preventive maintenance (PM) tasks, requiring single or multiple skills, within workforce availability constraints. In this paper, we examine the utility of evolution strategies to this problem. Comparison of the computational efforts of evolution strategies with exhaustive enumeration to reach optimal solutions for 60 small problems illustrates the ability of evolution strategies to yield optimal solutions increasingly efficiently with increasing problem size. A set of 852 large‐scale problems was solved using evolution strategies to examine the effects of task‐related problem characteristics, workforce‐related variables, and evolution strategies population size (μ) on CPU time. The results empirically supported practical utility of evolution strategies to solve large‐scale, complex preventive maintenance problems involving single‐ and multiple‐skilled workforce. Finally, comparison of evolution strategies and simulated annealing for the 852 experiments indicated much faster convergence to optimality with evolution strategies.