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Balancing assembly lines, a family of optimization problems commonly known asAssembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set ofproblems of enormous practical interest to manufacturing industry due to therelevant frequency of this type of production paradigm. For this reason, manyresearchers on Computational Intelligence and Industrial Engineering have beenconceiving algorithms for tackling different versions of assembly linebalancing problems utilizing different methodologies. In this article, it wasproposed a problem version referred as Mixed Model Workplace Time-dependentAssembly Line Balancing Problem with the intention of including pressing issuesof real assembly lines in the optimization problem, to which four versions wereconceived. Heuristic search procedures were used, namely two Swarm Intelligencealgorithms 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 whencompared to Particle Swarm Optimization algorithm.

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