We describe an assembly line problem with worker heterogeneity and uncertainty.Two mixed-integer formulations and one heuristic method are proposed.Extensive numerical results show the importance of considering uncertainty.Computational experiments also show that the proposed heuristic is fast and accurate. Assembly lines are manufacturing systems in which a product is assembled progressively in workstations by different workers or machines, each executing a subset of the needed assembly operations (or tasks). We consider the case in which task execution times are worker-dependent and uncertain, being expressed as intervals of possible values. Our goal is to find an assignment of tasks and workers to a minimal number of stations such that the resulting productivity level respects a desired robust measure. We propose two mixed-integer programming formulations for this problem and explain how these formulations can be adapted to handle the special case in which one must integrate a particular set of workers in the assembly line. We also present a fast construction heuristic that yields high quality solutions in just a fraction of the time needed to solve the problem to optimality. Computational results show the benefits of solving the robust optimization problem instead of its deterministic counterpart.
['Mayron César O. Moreira', 'Jean-François Cordeau', 'Alysson M. Costa', 'Gilbert Laporte']
Robust assembly line balancing with heterogeneous workers