Balancing assembly lines with tabu search

Balancing assembly lines is a crucial task for manufacturing companies in order to improve productivity and minimize production costs. Despite some progress in exact methods to solve large scale problems, softwares implementing simple heuristics are still the most commonly used tools in industry. Some metaheuristics have also been proposed and shown to improve on classical heuristics but, to our knowledge, no computational experiments have been performed on real industrial applications to clearly assess their performance as well as their flexibility. Here we present a new tabu search algorithm and discuss its differences with respect to those in the literature. We then evaluate its performance on the Type I assembly line balancing problem. Finally, we test our algorithm on a real industrial data set involving 162 tasks, 264 precedence constraints, and where the assembly is carried out on a sequential line with workstations located on both sides of the conveyor, with two possible conveyor heights and no re-positioning of the product. We discuss the flexibility of the metaheuristic and its ability to solve real industrial cases.