Construction method of genetic algorithms for large-scale complex production scheduling problems

1.提出了一种新的能保持种群多样性的选择方法。研究表明，该选择方法(称为部分枚举选择方法：PESM)可以在不影响解的精度的情况下，使种群在世代中保持较高的多样性。该算法对多个调度参数的变化也具有较好的鲁棒性。分解和搜索空间约简方法的提出通过对大规模调度问题的计算实验表明，本文提出的分解和搜索空间约简方法与遗传算法具有很好的协同作用。能够灵活处理复杂约束的遗传算法的设计遗传算法的设计是这样一种方式，即只需添加一个与添加的约束相关的模块即可解决调度问题。用该算法求解了具有并行机的车间作业调度问题和车间作业过程中的工人分配问题。针对无缓冲作业车间问题，提出了一种新的译码方法。将基于PESM的遗传算法应用于多目标流水作业问题的求解，得到了平滑的Pareto字体。

1. Proposition of a new selection procedure capable of keeping a diverse populationIt was shown that the proposed selection procedure (called Partial Enumeration Selection Method : PESM) can keep a high diversity population through the generations without deteriorating the accuracy of the solution. The robustness of the algorithm was also shown for variations of several schedule parameters.2. Proposition of decomposition and search space reduction methodsIt was shown from computational experiments for large-scale scheduling problems that the proposed decomposition and search space reduction methods work well with genetic algorithms.3. Design of genetic algorithms capable of dealing flexibly with complex constraintsThe design of genetic algorithms was proposed in such a way that the scheduling problems can be solved by only adding a module related to the added constraints. The scheduling problems of a job shop process with parallel machines and the worker allocation problem in a job shop process were solved by the proposed algorithm. Moreover a new decoding method was proposed for no-buffer job shop problems.4. Application to multi-objective optimization problemsThe PESM-based genetic algorithm was applied to solving multi-objective flowshop problems and smooth Pareto fonts were obtained.