一种新的混合优化算法及其在车间调度中的应用

提出了一种解决车间调度问题的新方法, 该方法将序优化思想融入巢分区算法框架, 采用"序比较"的方法进行算法的局部寻优. "序"的指数收敛性加快了巢分区算法的局部收敛速度, 从而提高了算法整体的优化效率. 最优计算量分配技术则依据在线数据对计算量进行合理的分配, 进一步提高算法的收敛速度和结果的可靠性. 混合算法继承了巢分区算法的全局搜索特性以及序优化的快速收敛性. 用该算法解决标准 Jobshop 调度问题, 并与序优化方法和模拟退火算法进行比较, 发现本文算法在收敛速度与优化质量方面均优于这些算法.

A New Hybrid Optimization Algorithm and Its Application in Job Shop Scheduling

This paper proposes a new hybrid optimization algorithm for solving stochastic job shop scheduling problems, which integrates the idea of ordinal optimization into the frame- work of nested partitions(NP)algorithm.Ordinal comparison method is used to perform local search of NP and its exponen- tial convergence rate improves the efficiency of local search and thereby the efficiency of global search greatly.And the optimal computing budget allocation technique further improves the con- vergence rate and reliability of the optimization result.The new algorithm retains the feature of global search of nested partitions and fast convergence of ordinal optimization.The algorithm is used to solve job shop scheduling benchmark problems.The re- sults of numerical simulations,which are compared with those of other well-known algorithms,show better performance of the proposed algorithm.