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针对再制造系统中能力约束下的拆卸批量计划问题,应用两阶段启发式遗传算法进行了优化求解.首先对再制造产品结构进行了描述,建立了再制造系统中能力约束下的拆卸批量计划优化模型;其次在不考虑能力约束情况下应用遗传算法求解出初始的拆卸批量计划,其中,染色体编码采用拆卸决策变量来表示,同时对适应度函数进行了线性变换,设计了具有自适应的交叉概率和变异概率;然后应用转移算法对初始得到的批量计划进行了修正,使其符合拆卸能力的约束.大量随机算例的仿真实验说明所提出的算法不论在寻找最优解方面还是在求解速度和稳定性方面,都要大大优于精确算法,能够较好地解决实际生产中面临的拆卸批量计划问题.
In order to solve the problem of disassembly and batch planning under capacity constraint in remanufacturing system, a two-stage heuristic genetic algorithm is used to solve the optimization problem.Firstly, the structure of remanufactured products is described and the disassembly and batch plan optimization Model. Secondly, genetic algorithm is used to solve the initial disassembly batch plan without considering the capacity constraints. Chromosome coding is represented by disassembly decision variables. At the same time, the fitness function is transformed linearly, and the crossover probability And the mutation probability. Then the initial batch program is modified by transfer algorithm to make it meet the constraints of disassembly ability.A large number of randomized examples of simulation experiments show that the proposed algorithm, whether in finding the optimal solution or in solving the speed and In terms of stability, it must be much better than the exact algorithm, which can solve the problem of disassembly and batch planning in actual production.