A Two-Individual Based Evolutionary Algorithm for the Flexible Job Shop Scheduling Problem

  • Junwen Ding Huazhong University of Science and Technology
  • Zhipeng Lü Huazhong University of Science and Technology
  • Chu-Min Li Université de Picardie Jules Verne
  • Liji Shen WHU - Otto Beisheim School of Management
  • Liping Xu Huazhong University of Science and Technology
  • Fred Glover University of Colorado


Population-based evolutionary algorithms usually manage a large number of individuals to maintain the diversity of the search, which is complex and time-consuming. In this paper, we propose an evolutionary algorithm using only two individuals, called master-apprentice evolutionary algorithm (MAE), for solving the flexible job shop scheduling problem (FJSP). To ensure the diversity and the quality of the evolution, MAE integrates a tabu search procedure, a recombination operator based on path relinking using a novel distance definition, and an effective individual updating strategy, taking into account the multiple complex constraints of FJSP. Experiments on 313 widely-used public instances show that MAE improves the previous best known results for 47 instances and matches the best known results on all except 3 of the remaining instances while consuming the same computational time as current state-of-the-art metaheuristics. MAE additionally establishes solution quality records for 10 hard instances whose previous best values were established by a well-known industrial solver and a state-of-the-art exact method.

AAAI Technical Track: Heuristic Search and Optimization