Developing a Flexible Manufacturing Control System Considering Mixed Uncertain Predictive Maintenance Model: a Simulation-Based Optimization Approach

Behrooz Shahbazi () and Seyed Habib A. Rahmati ()
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Behrooz Shahbazi: Islamic Azad University
Seyed Habib A. Rahmati: Islamic Azad University

SN Operations Research Forum, 2021, vol. 2, issue 4, 1-43

Abstract: Abstract Nowadays, with the development of information technology infrastructure, most systems are moving towards intelligent models, among which the field of production is no exception. One of the important sketch of production models is in term of predictive maintenance control systems such as reliability centered maintenance (RCM). This research focuses on developing a popular flexible manufacturing system called flexible job shop scheduling problem (FJSP) with predictive maintenance terms of RCM that are able to measure the level of production system reliability and determine the required maintenance activities. Moreover, since in production environments, processing time is an approximate parameter due to the activities in which manpower is involved, processing times of the model are defined with fuzzy functions. Meanwhile, to cope with stochastic nature of RCM and fuzzy nature of the process times, Buckley fuzzy numbers are implemented. In fact, this research introduces a mixed uncertain model that considers mentioned natures of probability and possibility at the same time by means developing fuzzy numbers of process time thorough confidence intervals of them. Then, since developed model is NP-Hard and stochastic, two simulation-based optimization (SBO) approaches are introduced based on two meta-heuristic algorithms called genetic algorithm (GA) and imperialist competition algorithm (ICA). Finally, various creative statistical and qualitative outputs are presented to analysis the performance of the introduced SBOs for solving the developed FJSP integrated with RCM control terms.

Keywords: Flexible manufacturing model; Predictive maintenance system; Simulation-based optimization; Uncertainty; Meta-heuristic algorithms (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s43069-021-00098-5

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