Optimization Scheme for Flexible Job Shop Scheduling Considering Parallel Operations and Sequence Constraints of Jobs

This paper investigates the Flexible Job Shop Scheduling Problem with Parallel Operations and Job Priority Constraints (JSPCPOJP). To address the limitations of existing job shop scheduling models that fail to accurately describe this type of problem, a mixed-integer programming-based optimization model is constructed. An improved genetic algorithm (RPEGA) is proposed to effectively solve the problem. The algorithm employs a Mixed Sequence and Operation Selection (MSOS) encoding method, a deep recursive population initialization mechanism, and POXIC crossover and EIB mutation operators that satisfy the problem constraints. It also combines elitism and roulette wheel selection strategies. The algorithm is validated through a simulation environment. The results show that the RPEGA algorithm can generate feasible solutions that meet the constraints of parallel operations and job priorities, significantly reducing the solution time. It demonstrates high solution efficiency and practicality, providing an effective method for solving similar scheduling problems in actual production.