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

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

Solving Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with FSS Algorithm

Balancing assembly lines, a family of optimization problems commonly known as Assembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set of problems of enormous practical interest to manufacturing industry due to the relevant frequency of this type of production paradigm. For this reason, many researchers on Computational Intelligence and Industrial Engineering have been conceiving algorithms for tackling different versions of assembly line balancing problems utilizing different methodologies. In this article, it was proposed a problem version referred as Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with the intention of including pressing issues of real assembly lines in the optimization problem, to which four versions were conceived. Heuristic search procedures were used, namely two Swarm Intelligence algorithms from the Fish School Search family: the original version, named "vanilla", and a special variation including a stagnation avoidance routine. Either approaches solved the newly posed problem achieving good results when compared to Particle Swarm Optimization algorithm