Assessment of Integrated Assembly Sequence Planning and Line Balancing Optimization Using Metaheuristic Algorithms

In assembly optimization, there has been an integration of Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) optimization, taking into account the advantages of improved solution quality, reduced error rates, and faster time-to-market for products. Previously, only a limited number of publications explored the integrated ASP and ALB optimization. These studies primarily compared the performance of algorithms within the Genetic Algorithm and Ant Colony Optimization classes. Moreover, the number of test problems used in these works was restricted to only three problems. In an ideal scenario, the efficacy of an algorithm can only be deduced when it is tested across a wide range of problem types. In this paper, the performance of six different metaheuristic algorithms for optimizing integrated ASP and ALB are compared. These algorithms include Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To rigorously test these metaheuristic algorithms, 45 test problems of various sizes were employed to evaluate their performance across different categories. The results show that ACO outperforms in larger-sized problems, while PSO exhibits potential to be explored further due to its satisfactory overall performance in terms of solution quality and distribution.