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不确定型车间作业调度问题是由确定型车间作业调度问题转化而来的一个随机规划问题.针对目前求解SJSSP问题的启发式算法存在的一些局限,利用目标函数理想最值的条件,以最大加工时间最小化的期望为目标函数,提出了自适应超启发式遗传算法(Adaptive Hyper-Heuristics genetic algorithms,AHHGA),解决此类问题.在上层利用目标函数理想最值的条件,对于不同的场景选用不同的启发式规则.在下层根据上层选择的启发式规则,构造可行解,然后搜索获取最优解.通过上下两层的协同搜索,确保在有限的搜索范围内,找到性能更为优良的解,与此同时,尽可能的减少运算时间.仿真分析表明,对于FT类基准问题,当加工时间服从正态分布时,本文提出算法较目前求解此类问题的同类方法的求解质量具有一定的改进.
Uncertain job shop scheduling problem is a stochastic programming problem transformed from deterministic job shop scheduling problem.According to some limitations of the heuristic algorithms for solving SJSSP problems at present, Time minimization as the objective function, this paper proposes adaptive Hyper-Heuristics genetic algorithms (AHHGA) to solve these problems.Using the condition of the ideal value of the objective function in the upper layer, we choose different scenarios Different heuristic rules, construct feasible solutions based on the heuristic rules selected by the upper layer, and then search for the optimal solution.Secondary collaborative search ensures that the solution with better performance is found within a limited search range , And at the same time, reduce the computation time as much as possible.The simulation results show that the proposed algorithm has some improvements over the existing methods for solving such problems when the processing time follows a normal distribution .