A Two-Stage Individual Feedback NSGA-III for Dynamic Many-Objective Flexible Job Shop Scheduling Problem

Dynamic events, such as machine fault and rush order insertion, are fairly common in the job shop scheduling, which may lead to significant delay in order delivery and low production efficiency. Under such circumstance, it is urgent to consider more perspectives in the scheduling, such as delay time and equipment load rate. In this article, a dynamic many-objective flexible job shop scheduling problem (DMaFJSP) is founded to simultaneously optimize the completion time, delay time, total equipment load and energy consumption. Canonical many-objective optimization algorithms are seeing difficulties in maintaining population diversity and enduring poor adaptability in dynamic scheduling problems. The paper proposes a two-stage individual feedback non-dominated sorting genetic algorithm-III (TSIF-NSGA-III), where a new population diversity strategy and an individual feedback strategy are added to expand the global search faculty and stronger dynamic adaptability. Numerical study in many-objective problem and dynamic many-objective problem are conducted. The final results illustrate that the proposed algorithm can with effect dispose of the DMaFJSP. Note to Practitioners —This paper was motivated by the flexible job shop scheduling problem (FJSP) in practical dynamic situations. In the actual production procedure, however, FJSP is a more challenging issue. Not only operation sequencing and machine allocation matters, but also uncertain factors in the environment, such as machine fault, rush order insertion, etc., are important. In addition, the majority of current researchers formulate the FJSP simply focusing on maximum completion time. However, low carbon and high efficient manufacturing calls for more objectives. In this paper, two dynamic incidents, machine stoppage and rush order insertion, are considered. In addition, the model of DMaFJSP is established with many objectives such as total energy consumption, completion time, equipment load and delay time. To resolve foregoing problems, this article proposes a TSIF-NSGA-III algorithm, which adopts a diversity generation strategy and an individual feedback strategy to strengthen the search ability and dynamic adaptability of this algorithm. Preliminary simulation outcomes illuminate that this algorithm has certain advantages. In addition, the algorithm can also be applied to other multi-objective workshop scheduling problems, such as mixed flow workshop, distributed workshop, etc.