关键词: 预制构件生产调度, 时变运输时间, 工件装车组合, 提前拖期惩罚, 果蝇优化算法

Abstract: Production scheduling,job loading combination and traffic congestion are important subjective and objective factors affecting the production and operation efficiency of prefabricated manufacturing enterprises.Therefore,how to integrate production scheduling and job loading combination scheme effectively is an urgent problem for prefabricated components manufacturers with considering the time-varying characteristics of traffic congestion.To deal with the integrated optimization problem,a mathematical model for the integrated optimization problem was formulated to minimize the sum of product inventory cost,tardiness penalty and vehicle transportation cost.To reduce the difficulty of solving the problem,an Adaptive Multiple Neighborhoods Collaborative Search-based Fruit Fly Optimization Algorithm (AMNCS-FOA) was proposed.Its main characters were as follows:an integrated decision coding method with zero insertion operation was designed to represent the combination scheme of production scheduling and job loading;in the olfactory search phase of the algorithm,an adaptive probabilistic neighborhood selection strategy was proposed based on four neighborhood structures including intra group exchange,inter group exchange,intra group insertion and inter group insertion;in the visual search stage,to improve the global search ability of the algorithm,the iterative search was performed with a certain probability to accept the inferior solution as the population center.The results showed that AMNCS-FOA algorithm had faster convergence speed and better solution quality for dealing with the integrated optimization problem.Compared with the rule-based heuristics commonly used in prefabricated component manufacturing enterprises,the proposed algorithm had an average improvement rate of no less than 13% in solving quality,which was expected to significantly increase the net profit of prefabricated component enterprises and improve customer satisfaction.

Key words: precast production scheduling, time-varying transportation time, job loading combination, earliness/tardiness penalties, fruit fly optimization algorithm