An Improved Sparrow Search Algorithm for Solving the Flexible Job Shop Scheduling Problem

For the Flexible Job Shop Scheduling Problem (FJSP) aimed at minimizing makespan, an Improved Sparrow Search Algorithm (ISSA) is proposed. Firstly, a dual-segment encoding approach of equal length is adopted to represent the FJSP. A transformation mechanism based on the ROV rule is introduced to convert between sparrow individual position vectors and scheduling solutions. Improve the quality of optimal solutions and search efficiency. Secondly, a population initialization method is designed To ensure the quality and diversity of the initial solutions. Arrange operations for machines, optimize resource utilization rate, and enhance production stability and controllability. A variable neighborhood search strategy is embedded to enhance local search capabilities. To mitigate local optima, a cautionary sparrow integrating Cauchy mutation is employed within the Sparrow Search Algorithm (SSA). Furthermore, genetic algorithm crossover and mutation operators are integrated to enhance SSA's global search capabilities. Finally, through simulation experiments and analysis on 15 standard instances of flexible job shop scheduling, the effectiveness of the algorithm in solving FJSP has been verified.