Single and multi-objective evolutionary algorithms for the coordination of serial manufacturing operations

This paper focuses on a typical problem arising in serial production, where two consecutive departments must sequence their internal work, each taking into account the requirements of the other one. Even if the considered problem is inherently multi-objective, to date the only heuristic approaches dealing with this problem use single-objective formulations, and also require specific assumptions on the objective function, leaving the most general case of the problem open for innovative approaches. In this paper, we develop and compare three evolutionary algorithms for dealing with such a type of combinatorial problems. Two algorithms are designed to perform directed search by aggregating the objectives of each department in a single fitness, while a third one is designed to search for the Pareto front of non-dominated solutions. We apply the three algorithms to considerably complex case studies derived from industrial production of furniture. Firstly, we validate the effectiveness of the proposed genetic algorithms considering a simple case study for which information about the optimal solution is available. Then, we focus on more complex case studies, for which no a priori indication on the optimal solutions is available, and perform an extensive comparison of the various approaches. All the considered algorithms are able to find satisfactory solutions on large production sequences with nearly 300 jobs in acceptable computation times, but they also exhibit some complementary characteristics that suggest hybrid combinations of the various methods