郑州大学
河南省科技研发计划联合基金项目(242103810046)；河南省科技攻关计划项目(232102321093, 232102321026)；河南省哲学社会科学规划项目(2023BJJ085)。
Zhengzhou University
Henan Province Science and Technology Research Program Project(232102321093, 232102321026)；Henan Province Philosophy and Social Science Planning Project(2023BJJ085)
为了解决工业制造过程中一个生产任务同时需多台处理器加工的问题，提出带运输时间的多阶段混合流水车间多处理器任务调度问题，该问题被证明是NP-hard问题，为此，构建以最小化最大完成时间（makespan）为优化目标的整数规划模型，提出一种融合改进粒子群算法、修正遗传算法和模拟退火算法的混合离散粒子群算法。首先，改进粒子群算法的相关行为以避免该算法存在的过早收敛问题；然后，引入遗传算法的交叉变异算子，进一步改善粒子群算法和遗传算法中的优秀个体；最后，采用模拟退火算法对得到的粒子群进行局域搜索以获取更高质量的解。所提算法与现有一些算法的对比分析说明了所提出的混合粒子群算法具有更好的优化效果。
In order to handle that one production task simultaneously requires multiple processors to process it,multiprocessor task scheduling in a multi-stage hybrid flowshop with transportation times is proposed.This problem has been shown to be NP-hard. For this, an integer programming model is constructed with the optimization objective of minimizing the maximum completion time (makespan).A mixed discrete particle swarm algorithm is developed combined with an improved particle swarm algorithm, a modified genetic algorithm and a simulated annealing algorithm. Firstly, the relevant behaviors of particle swarm algorithm are improved to avoid the premature convergence of this algorithm. Next,crossover and mutation operators of genetic algorithm are introduced to further enhance the excellent individuals in the particle swarm algorithm and genetic algorithm. Finally, a simulated annealing algorithm isapplied to perform local search for the obtained particle swarm so as to get solutions with higher quality. The comparison and analyses between the proposed algorithm and some existing algorithms show that the developed hybrid particle swarm algorithm has better performance.
