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考虑到现实流水车间调度中设备具有恶化特性,针对作业处理时间是其开始时间的线性递增函数的流水车间调度问题,建立了最小化最大完成时间和总延迟时间的多目标优化模型;进而设计了一种基于分解的自适应多种群多目标遗传算法进行求解.该算法将多目标优化问题分解为多个单目标子问题,并分阶段地将这些子问题引入求解过程.在每次迭代时,根据种群在目标空间和解空间的分布情况,自适应地为当前求解的子问题分别构造子种群进行求解.通过对数值算例仿真实验,验证和分析了所提出的算法在解决该问题上能够获得较好质量和分布性的非支配解集.
Considering the deterioration of equipment in the real-time flowshop scheduling, aiming at the problem of pipeline shop scheduling problem whose job processing time is a linear increasing function of its starting time, a multi-objective optimization model was established to minimize the maximum completion time and the total delay time. A decomposition-based adaptive multi-population multi-objective genetic algorithm is proposed to solve the multi-objective optimization problem, which decomposes the multi-objective optimization problem into several single-objective subproblems and introduces these subproblems into the solving process step by step.In each iteration, According to the distribution of the population in the target space and the solution space, the sub-population of the current solution is constructed adaptively to solve the sub-population respectively.A numerical example is given to validate and analyze the proposed algorithm to solve the problem Better quality and distribution of non-dominated solution set.