This paper provides new insights to the area of sustainable manufacturing systems at analyzing the novel paradigm of integrated production logistics, quality, and maintenance design. For this purpose, we investigate the optimal production and repair/major maintenance switching strategy of an unreliable deteriorating manufacturing system. The effects of the deterioration process are mainly observed on the failure intensity and on the quality of the parts produced, where the rate of defectives depends on the production rate. When unplanned failures occur, either a minimal repair or a major maintenance could be conducted. The integration of availability and quality deterioration led us to propose a new stochastic dynamic programming model where optimality conditions are derived through the Hamilton-Jacobi-Bellman equations. The model defined the joint production and repair/major maintenance switching strategies minimizing the total cost over an infinite planning horizon. In the results, the influence of the deterioration process were evident in both the production and maintenances control parameters. A numerical example and an extensive sensitivity analysis were conducted to illustrate the usefulness of the results. Finally, the proposed control policy was compared with alternative strategies based on common assumptions of the literature in order to illustrate its efficiency.

We study the optimal production planning and major maintenance scheduling for an unreliable manufacturing system. We assume that the production unit experiences progressive deterioration that negatively influences product quality. For the production policy, we extend improve traditional threshold policies with a superior alternative, based on a just-in-time (JIT) strategy. The paper brings a new vision on the importance of implementing more effective production strategies based on JIT methods, instead of traditional threshold policies. When a failure occurs, the production unit is minimally repaired, and when the major maintenance is selected, the machine is restored to brand-new conditions. The objective of the model is to determine the simultaneous JIT production and major maintenance strategy that minimizes the total cost. Due to the stochastic features of the system, a simulation-based optimization approach is proposed, which combines the descriptive capabilities of simulation modeling with analytical models, statistical analysis, and optimization techniques. The results verify that the proposed simulation–optimization approach provides new and coherent results that highlight the strong influence of quality deterioration on the determination of the control parameters. A sensitivity analysis and a comparative study are conducted to illustrate that significant cost savings could be obtained with the proposed approach.

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