关键词: 混流装配, 车辆排序, 流水车间调度, 多目标优化, 蚁群算法

Abstract: 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.

Key words: mixed-flow assembly, car sequencing, flow shop scheduling, multi-objective optimization, ant colony optimization(ACO) algorithm