Simultaneously Solving Mixed Model Assembly Line Balancing and Sequencing problems with FSS Algorithm
This work addresses optimization challenges for decision-makers in the manufacturing industry, but it is incremental as it applies an existing meta-heuristic to a known problem combination.
The paper tackled the NP-hard Mixed Model Assembly Line Balancing and Sequencing problems by proposing a simultaneous solution approach using the Fish School Search meta-heuristic algorithm, achieving results compared to Particle Swarm Optimization on three test instances.
Many assembly lines related optimization problems have been tackled by researchers in the last decades due to its relevance for the decision makers within manufacturing industry. Many of theses problems, more specifically Assembly Lines Balancing and Sequencing problems, are known to be NP-Hard. Therefore, Computational Intelligence solution approaches have been conceived in order to provide practical use decision making tools. In this work, we proposed a simultaneous solution approach in order to tackle both Balancing and Sequencing problems utilizing an effective meta-heuristic algorithm referred as Fish School Search. Three different test instances were solved with the original and two modified versions of this algorithm and the results were compared with Particle Swarm Optimization Algorithm.