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

TL;DR

This paper introduces a new, complex assembly line balancing problem considering real-world factors and proposes two Fish School Search algorithms, demonstrating their effectiveness over Particle Swarm Optimization in solving it.

Contribution

It formulates a novel mixed model, time-dependent assembly line balancing problem and applies two FSS-based heuristics, advancing optimization methods for practical manufacturing scenarios.

Findings

FSS algorithms outperform Particle Swarm Optimization

Both FSS variants effectively solve the new problem

Stagnation avoidance improves FSS performance

Abstract

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.