Multi-objective assembly line rebalancing problem based on complexity measurement in green manufacturing

Abstract

Mass personalized production in green manufacturing requires flexible adjustments in the assembly line, and the contradiction between the impact on resources and regaining production efficiency makes it difficult for decision-makers to balance. In order to quantify the cost of adjustment and provide a theoretical basis for rebalancing optimization, this paper introduces an adjustment complexity measurement method and a rebalancing optimization model. To solve the optimization problem, an adaptive rebalancing algorithm is designed based on the nondominated sorted genetic algorithm-II (NSGA-II). In this case, the algorithm is tailored to address the rebalancing problem by acquiring optimal operation distribution plans that minimize adjustment complexity. Finally, the effectiveness of the proposed rebalancing method is demonstrated through two case studies. Through analysis of algorithm performance and solutions, as well as comparison with existing algorithms, the results show that the proposed algorithm has good convergence and optimization performance. The proposed method can provide decision-makers with rebalancing solutions with different focuses. When adjusting the assembly line, the impact of the solution on resources and adjustment costs is fully considered.