AI Insight
This study addresses the reconfigurable assembly line scheduling problem by developing a multi-objective mathematical model that simultaneously minimizes reconfiguration cost, production workload imbalance, and logistics variability. The authors propose a Q-learning-based multi-objective hyper-heuristic algorithm that dynamically selects among four established metaheuristic operators (particle swarm optimization, teaching-learning-based optimization, whale optimization algorithm, and grey wolf optimizer) based on real-time performance feedback. Testing on 120 benchmark instances showed the proposed algorithm consistently outperformed nine competing multi-objective algorithms, with the mathematical model also validated using the epsilon-constraint method to generate Pareto-optimal solution sets.
Why it matters
Reconfigurable assembly lines are increasingly important for manufacturers responding to demand for customized and varied products, and more efficient scheduling algorithms can reduce operational costs and improve production flexibility. This work provides a practical computational framework that could be applied in real manufacturing environments where multiple conflicting objectives must be balanced simultaneously.
by Haoyi Zhao, Xiangming Huang, Guoliang Liu, Zixiang Li, Fan Chen, Gaojie Lu
Reconfigurable assembly lines have emerged as a vital manufacturing paradigm to meet the growing demand for customized and multi-variety products. This study considers the reconfigurable assembly line scheduling problem, involving product sequencing optimization, to minimize reconfiguration cost, production workload equalization, and logistics leveling simultaneously. This study formulates a novel and linearized multi-objective mathematical model, which rectifies deficiencies in prior formulations. A novel Q-learning-based multi-objective hyper-heuristic algorithm is proposed. The algorithm integrates multiple metaheuristic operators, including particle swarm optimization, teaching–learning-based optimization, whale optimization algorithm, and grey wolf optimizer, within a unified search framework. Q-learning is employed to adaptively select the most promising operator at each search stage based on real-time performance feedback. Moreover, the proposed algorithm incorporates a new density-aware leader selection strategy with a survival-time decay factor to select the global best solution for population evolution, favoring superior solutions in sparse regions and increasing selection pressure on high-quality individuals. A numerical case study demonstrates that the models with the ε-constraint method could achieve a set of Pareto solutions. A computational study on 120 generated benchmark instances demonstrates that the proposed methodology outperforms nine other high-performing multi-objective algorithms.