A reliability based integrated model of maintenance planning with quality control and production decision for improving operational performance

• Published in: Reliability engineering & system safety 2022-10, Vol.226, p.108681, Article 108681
• Main Authors: Tambe, Pravin P., Kulkarni, Makarand S.
• Format: Article
• Language: English
• Subjects: Component reliability; Conditional Reliability; Decision making; Flow charts; Genetic algorithms; Heuristic methods; Inspection; Integrated approach; Integrated planning; Maintenance; Mathematical models; Operating costs; Operational performance; Optimization; Process parameters; Production; Production scheduling; Quality; Quality control; Reliability aspects; Reliability engineering; Schedules; Simulated annealing; Tightness
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2022.108681

•Integrated model of maintenance, quality control and production scheduling.•Batch sequence, maintenance actions and sampling parameters are simultaneously optimized.•Hybrid optimization approach is used for solving the model.•Effect of model parameters on solution is investigated using experimental analysis.•Proposed model results in dynamic planning of decision parameters. This paper presents an integrated planning between the three core functions of shop floor management; maintenance, production scheduling, and quality. The methodology is based on the conditional reliability of components and its effect on system operation. The objective is to minimize the system operation cost of the combined decision and investigate the cost-effectiveness of the integrated policy over the non-integrated planning. The integrated approach helps the decision-maker to find maintenance actions for individual components, production schedule and sampling parameters for process quality inspection. Mathematical models for integration and a flow chart for the combined methodology of the three functions are presented. Metaheuristic approaches, simulated annealing, and genetic algorithm are used for optimization. A simulation study at different levels of component degradation (service age) and schedule tightness factor is presented. Besides, we present an experimental analysis of varying model parameter values for the integrated planning and compare the results with the independent (non-integrated) planning approach. The results show the improved operational performance of the integrated planning and a lesser operating cost than the non-integrated planning. The proposed approach is dynamic as quality control parameters and production decisions will change as per the maintenance decision.