基于改进蚁群算法的汽车混流装配调度模型求解

An Improved ACO Algorithm for Automobile Mixed-flow Assembly Scheduling Problems

针对某汽车总装车间混流装配过程涉及大量人工以及人机协同操作而导致工位过载、整车装配质量无法得到保证的问题,建立了瓶颈选装工位负载平衡化、考虑换装与提前作业时间的加工滞后次数最小化的分层序列双目标优化模型,同时设计了一种改进蚁群算法。该算法在信息素全局更新以及概率转移规则过程中,使用一种特定启发式函数,并更改迭代过程中最优解的评价方法。仿真对比实验结果表明,该算法在优化目标函数过程中的收敛速度、收敛精度、最优解质量等方面均优于传统蚁群算法和对比遗传算法,验证了模型和算法的有效性。此外,该算法还可反向求解加工滞后次数为零时的计划生产节拍,具有一定的生产指导意义。

Since overload work stations were common and the vehicle assembly quality could not be guaranteed in mixed-model lines which involved a great deal of manual and human-machine operations,a bi-objective car sequencing model was proposed with load balancing at the bottleneck selection as a primary criterion,and minimizing the number of processing delay when considering the replacement and advanced operation time as a secondary criterion.Besides,an improved ACO was designed which used a specific heuristic function in the processes of pheromone global update and probability transfer rules,and changed the evaluation method of the optimal solution in the iterative processes.Compared with traditional ACO and genetic algorithm,computational experiments illustrate that both objectives may be effectively solved using the presented ACO algorithm.In addition,the algorithm may also reversely compute the planned production cycle when the second objective value is zero,which has certain guiding significance in vehicle production.