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Mass customization implies in large variety of product models and small lot sizes. Human-based activities are highly affected by the learning process as a new model is put into production. That makes estimation of lot processing times difficult and jeopardizes the efficiency of scheduling techniques. The method proposed in this paper integrates information from learning curve modeling and scheduling heuristics, aiming at minimizing the completion time of jobs to be processed. Lot processing time is estimated by means of learning curve modeling and then used in scheduling heuristics for the unrelated parallel workers problem. The completion time generated by the recommended heuristic, determined through simulation, deviates 4.9% from the optimal sequence and yields good work balance among teams of workers. The heuristic is demonstrated in a shoe manufacturing process.

Programação de tarefas baseada em curvas de aprendizado para linhas de produção customizadas