Learning dependent job scheduling in mass customized scenarios considering ergonomic factors

Industrial environments that rely on Mass Customization are characterized by high variety of product models and reduced batch sizes, demanding prompt adaptation of resources to a new product model. In such environment it is difficult to schedule tasks that require manual procedures with different levels of complexity and repetitiveness. This article integrates learning curves, scheduling heuristics and ergonomic factors to sequence batches in teams of workers. For that matter, we propose the ATCE rule (Apparent Tardiness Cost with Ergonomics Factors), which simultaneously reduces the total weighted tardiness and the allocation of batches with similar complexities to the same team (measured by percentage of saturation). When applied to two assembly lines in a case study from the footwear industry, the ATCE presented outstanding performance in ergonomic terms by reducing the percentage of work saturation from 66% to 1% in Team 1, and from 62% to 0% in Team 2, compared to results yielded by the Apparent Tardiness Cost (ATC) rule. In addition, the objective function value (total tardiness) increased only 3.53% in Team 1, and 2.18% in Team 2. In addition to the case study results, we assessed the robustness of the ATCE rule through simulation experiments. In all evaluated instances, the ATCE remarkably reduced the percent of saturation compared to the ATC while slightly increasing the total tardiness.