关键词: 预制构件生产调度, 联合优化, 协同进化, 遗传算法, 差分进化

Abstract: In real production process of precast components manufacturing,an order often contains multiple jobs.To meet customer delivery time and facilitate management,it is necessary to processing jobs from the same order continuously.Thus,it is crucial to solve the Joint Optimization Problem of Inter Order Scheduling and Intra Order Scheduling (JOP_IOSIOS).JOP_IOSIOS is more complicated than the traditional flowshop scheduling problem,is also a NP-hard problem.To solve this problem,based on the Just-in-Time (JIT) policy,a mixed-integer programming model was formulated to minimize the total earliness and tardiness penalties through analyzing the process constraints,inter-order constraints and intra-order constraints in-depth.In view of the complexity of the problem,an effective Co-Evolutionary Hybrid Genetic Algorithm—Discrete Differential Evolution algorithm (CoHGA-DDE) was proposed based on the ideas of decomposition and co-evolution.The main idea was to construct two populations at first,then adopt different evolutionary strategies to optimize them,and finally improve the performance through the interaction of two populations.To verify the effectiveness of the co-evolutionary framework and the proposed method,a Co-Evolutionary Genetic Algorithm (CoGA),a Co-Evolutionary Discrete Differential Evolution (CoDDE),a Genetic Algorithm (GA),a Discrete Differential Evolution (DDE) and an Iterative Greedy (IG) algorithm were designed.Through testing on orders of different sizes,the calculation results showed that CoHGA-DDE achieved the best solution quality and strongest robustness under the co-evolution framework.Compared with the heuristic which was often adopted by a precast manufacturer,the proposed algorithm improved solution quality obviously,which was expected to reduce production cost,improve on-time delivery rate and ensure construction progress.

Key words: precast scheduling, joint optimization, co-evolution, genetic algorithms, differential evolution