4.7 Article

A delay-time model to integrate the sequence of resumable jobs, inspection policy, and quality for a single-component system

Journal

RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 230, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2022.108902

Keywords

Delay-time; Inspection policy; Job sequence; Quality; Rework; Schedule

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This study proposes a model that integrates the scheduling of resumable jobs and inspection policy in a critical single-component system. The deterioration process is modeled with a delay-time concept, allowing the system to be in good, defective, or failed states. The proposed model includes a limit on consecutive minimal repairs to prevent the production of a substantial proportion of non-conforming items. A mathematical framework, simulation model, and Genetic Algorithm are developed to minimize expected total costs. The study compares the Genetic Algorithm with a heuristic algorithm and the proposed model with its simplified version, achieving a significant improvement in some cases.
This study proposes a model that integrates the schedule of a sequence of resumable jobs and inspection policy in a critical single-component system, following a deterioration process modeled with a delay-time concept. Thus, the system can be in a good, defective or failed state. When the system is in a defective state, it decreases in product quality, which leads to an increase in the proportion of non-conforming items and rework activities. If the system fails, a minimal repair is required to restore the system to an operational condition but in a defective state. Consequently, we established a limit of m consecutive minimal repairs to prevent the system from producing a substantial proportion of non-conforming items. So, we propose a mathematical framework and a simulation model to minimize the expected total costs. Also, we develop a Genetic Algorithm to tackle medium-size instances and analyze some special cases of the model. Therefore, we solved a numerical case and performed a sensitive analysis. We compare the Genetic Algorithm with a heuristic algorithm, and our model with its straight simplification. In this last comparison, in the worst case tested, the ignorance cost'' could reach 335%.

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