An Improved Genetic Algorithm Combining Tabu Search for Solving Flexible Job Shop Scheduling Problem with Transportation and Start-Stop Constraints

Manufacturing enterprises are facing a significant challenge in managing a large number of small-scale tasks characterized by short processing times. However, to the best of our knowledge, most studies ignore the transportation time of jobs between machines. To solve the multi-objective distributed flexible job shop scheduling problem (MODFJSP, we establish a mathematical model and propose an improved genetic algorithm (IGA) with the goal of minimizing maximum completion time and energy consumption while considering the transportation time of jobs between machines and the start-stop operation of the machines. Firstly, we design a hybrid initialization method to improve the quality of the initial population. Secondly, we design an improved tournament selection and an improved tabu search strategy to avoid the solution set from falling into local optima too early. Finally, we propose an energy-saving strategy to reduce the idletime of machines, thereby reducing the energy consumption. We conduct extensive testing and comprehensive evaluation on 20 instances, the IGA can achieve better solutions in almost all instances, which is of great significance for the improvement of production scheduling in intelligent manufacturing.