Abstract: Taking massive customized production of home appliance as the research background, the scheduling of home appliance manufacturing is abstracted as a problem of dynamic heterogenous parallel machine scheduling with sequence-dependent setup time (HPMS-SST). Manual scheduling rules are simple and efficient in solving dynamic scheduling problems, but their adaptability to different scenarios is weak. To this end, an automatic design framework for rules based on genetic programming (GP) is introduced. First, by analyzing the features and optimization requirements of home appliance production, a model of HPMS-SST is established with the objective of minimizing the mean product tardiness. Subsequently, based on the characteristics of this problem, an improved GP algorithm is proposed for the coevolution of machine assignment and queue sequencing rules; Also, the feature attributes of machines and orders are extracted and input into the GP algorithm framework to automatically design scheduling rules; Finally, a number of test cases are generated from the real production data of a home appliance manufacturer. By comparing the experimental results of the proposed algorithm and manual designed rules in various conditions, the effectiveness of the GP algorithm is verified. Besides, sensitive analysis is conducted to evaluate the influence of parameters for different production conditions on the generated GP-based rules.

Key words: heterogenous parallel machines, dynamic scheduling, heuristic rules, genetic programming