关键词: 遗传算法, 可行域, 装配作业调度, 可行解空间, 装配约束, 种群多样性

Abstract: To solve the Job Shop scheduling problems in assembly environment, a genetic algorithm based on feasible solution space searching named Feasible Solution Space Genetic Algorithm (FSSGA) was proposed. To ensure the validity and feasibility of chromosomes in the whole evolution process, the first generation of repair operator in the stage of population initialization, the feasible crossover operator in the stage of crossover and the feasible mutation operator in the stage of mutation were designed and realized. The combinatorial design of feasible crossover operator and feasible mutation operator realized the feasible solution search of FSSGA, which not only reduced the searching space but also omitted the complex operations of decoding and repairing. FSSGA improved the solution efficiency and provided valuable reference for solving complex assembly Job Shop scheduling problems. The rationality and superiority of FSSGA was embodied in the comparative experiment of simple rules, tabu search, simple genetic algorithm and FSSGA.

Key words: genetic algorithm, feasible solution space, assembly Job Shop scheduling, feasible solution space, assembly constraints, population diversity, tabu search