考虑物料配送的混流装配线协同优化

Collaborative optimization of mixed-model assembly line considering material delivery

针对混流装配线的平衡排序和物料配送的协同问题,提出了一种装配线同步的协同配送策略,在此基础上以平衡工位负载、最小化最大完工时间和减少物料配送成本为目标,建立了考虑物料配送的协同优化模型。利用改进的非支配排序遗传算法(INSGA)对模型进行求解,基于模型的三层编码设计保证了解集的合法性和完整性。提出的多层编码交叉算子和变异算子,在提高种群多样性的同时也加快了算法的收敛速度。为避免算法陷入局部最优,重新设计了拥挤度距离,并改进了精英选择策略。此外,基于竞争机制,设计了一种舒适度竞争算子,便于在Pareto最优解集中确定具体方案。最后,通过标准算例和具体案例的求解,证明了改进算法的优越性和模型的有效性,案例的优化结果表示,装配线的完工时间缩短了近20%,物料配送成本节省了30%以上,取得了较好的优化效果。

To solve the collaborative problem of balancing,scheduling and material delivery in mixed-model assembly line,a collaborative distribution strategy for assembly line synchronization was proposed.On this basis,a collaborative optimization model was established to balance the load of work stations,minimize the maximum completion time and reduce the cost of material delivery.The Improved Non-Dominated Sorting Genetic Algorithm(INSGA)was used to solve this model,and the design of three-layer coding based on the model ensured the legitimacy and integrity of solution sets.The proposed crossover operator and mutation operator based on multi-layer coding both improved the diversity of population and speed up the convergence of the algorithm.To avoid falling into local optimum,the crowding distance was redesigned and the elitist selection strategy was improved.In addition,based on the competitive mechanism,a comfort competitive operator was designed to determine the specific solution between Pareto optimal solution sets.Finally,the superiority of the improved algorithm and the effectiveness of the model were proved by optimization results of standard examples and specific problems.