This study proposes a method to combine the improved gene expression programming (IGEP) and buffer-local scheduling to solve the optimization problem of flexible job shop production scheduling with buffer capacity lim...
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This study proposes a method to combine the improved gene expression programming (IGEP) and buffer-local scheduling to solve the optimization problem of flexible job shop production scheduling with buffer capacity limitation (BLFJSP). The IGEP algorithm was first used for the overall optimization and later combined with the flexible job shop production mode to design a multi-gene chromosome coding method to realize the overall optimization of the production schedule using the genetic biological evolution function. Furthermore, when the algorithm fell into a local extremum, the Jaccard similarity coefficient method was used to filter the approximate chromosomes, and the historical excellent chromosome backtracking method was used to increase the diversity of the population. Lastly, we designed the local scheduling rules of the job in and out of the buffer area. Based on the IGEP algorithm and considering the influence of buffer capacity limitation on the scheduling scheme, the final production scheduling scheme was generated by dual optimization methods aiming at minimizing blocking time and minimizing maximum completion time. Based on the actual production data of an assembly manufacturing company, multiple sets of simulation programs were designed to validate the IGEP algorithm for solving the optimization problem of BLFJSP.
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