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为了提高企业生产效率,降低生产成本,以仪器仪表制造业为背景,针对某企业流量计生产车间的特点,建立优化目标函数,提出了多加工路径柔性作业车间调度模型(MPFJSP)。为了避免该模型中非可行解的干扰,采用新的顺序编码方式和在非可行解周围搜索可行解并代替之的方法;且为提高收敛速度,将粒子分为若干小段分别更新和扰动,以保留优秀基因,由此形成分段多目标粒子群优化算法(PMOPSO)。通过算法对比,发现所提方法能在工件的每一条加工路径中寻优,验证了其性能更优越,也验证了MPFJSP相对于传统柔性作业车间调度(FJSP)更灵活,得到的解也更优,在流量计生产车间的实验性生产中,违约成本约下降了12%,生产效率提升约10%。
In order to improve the production efficiency and reduce the production cost, aiming at the characteristics of an enterprise flowmeter production workshop, an optimization objective function was established and a multi-path flexible job shop scheduling model (MPFJSP) was proposed. In order to avoid the interference of non-feasible solutions in the model, a new sequential coding method and a method of searching for feasible solutions around non-feasible solutions are used. In order to improve the convergence speed, the particle is divided into several small segments to be updated and perturbed separately The excellent genes are retained, thus forming a segmented multi-objective particle swarm optimization algorithm (PMOPSO). The algorithm is compared to find that the proposed method can be optimized in every working path of the workpiece, which proves its superior performance. It also proves that MPFJSP is more flexible than the traditional flexible job shop scheduling (FJSP) In the experimental production of the flowmeter production workshop, the default cost decreased by about 12% and the production efficiency by about 10%.