Tackling the 1/3 variant of the time and space assembly line balancing problem by means of a multiobjective genetic algorithm

The time and space assembly line balancing problem (TSALBP) considers realistic multiobjective versions of the classical assembly line balancing involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. This industrial problem is very difficult to solve and of crucial importance in the manufacturing context. As TSALBP-1/3 contains a set of hard constraints like precedences or cycle time limits for each station it has been mainly tackled using multiobjective constructive metaheuristics (e.g. ant colony optimization). Global search algorithms in general -and multiobjective genetic algorithms in particular have shown to be ineffective to solve this family of problems up to now. The goal of this contribution is to present a new multiobjective genetic algorithm design, taking the well known NSGA-II algorithm as a base and new coding scheme and specific operators, to properly tackle with the TSALBP. An experimental study on six different problem instances is used to compare the proposal with the state-of-the-art methods.