Scheduling optimization of flexible flow shop with buffer capacity limitation based on an improved discrete particle swarm optimization algorithm
Daliang Chang, Haibo Shi, Chang Liu, Fu Meng
Abstract
To address the challenge of production scheduling in flexible flow shops with buffer capacity limitations, a mathematical model is established to minimize the maximum completion time and maximize equipment utilization. To efficiently solve the problem, an improved discrete particle swarm optimization algorithm with multi-population reduction (MPREDPSO) is developed. It integrates an elite retention strategy, which significantly contributes to its efficiency and robustness. The algorithm stands out for its ability to effectively navigate through potential local optima, a common pitfall in optimization problems, through a Euclid algorithm coupled with a sophisticated gene backtracking mechanism. The performance of MPREDPSO is rigorously tested and proven through comparative analyses. Its effectiveness is proven through comparisons with other algorithms, namely the discrete particle swarm algorithm, genetic algorithm, whale algorithm, grey wolf optimizer and whale swarm algorithm. In addition, MPREDPSO’s practical utility is demonstrated in a high-temperature ham sausage production workshop, showcasing its industrial application capabilities.