Simultaneously Solving Mixed Model Assembly Line Balancing and Sequencing problems with FSS Algorithm

TL;DR

This paper introduces a Fish School Search meta-heuristic to simultaneously solve assembly line balancing and sequencing problems, demonstrating its effectiveness through comparative tests against Particle Swarm Optimization.

Contribution

It presents a novel integrated approach using Fish School Search for addressing both assembly line balancing and sequencing problems concurrently.

Findings

FSS outperformed PSO in test instances

Modified FSS versions improved solution quality

Approach is effective for NP-hard assembly line problems

Abstract

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.