Designing machine operating strategy with simulated annealing and Monte Carlo simulation |
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| Institution: | 1. Department of Organic Chemistry, Faculty of Chemistry, University of Mazanadaran, P.O. Box 47416, Babolsar, Iran;2. Department of Polyurethane, Iran Polymers and Petrochemicals Institute, P.O. Box 14965-115, Tehran, Iran;1. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China;3. Texas A & M University at Qatar, Doha 5825, Qatar;4. Platform Technologies Research Institute, RMIT University, VIC 3001, Australia;1. University of Michigan, Ann Arbor, MI 48109, USA;2. Duke University, Durham, NC 27708, USA;1. Department of Systems & Information Engineering, University of Virginia, Charlottesville, Virginia;2. Department of Surgery, University of Virginia Health System, Charlottesville, Virginia |
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| Abstract: | This paper describes a simulation-based parameter design (PD) approach for optimizing machine operating strategy under stochastic running conditions. The approach presents a Taguchi-based definition to the PD problem in which control factors include machine operating hours, operating pattern, scheduled shutdowns, maintenance level, and product changeovers. Random factors include machine random variables (RVs) of cycle time (CT), time-between-failure (TBF), time-to-repair (TTR), and defects rate (DR). Machine performance, as a complicated function of control and random factors, is defined in terms of net productivity (NP) based on three key performance indicators: gross throughput (GT), reliability rate (RR), and quality rate (QR). It is noticed that the resulting problem definition presents both modeling and optimization difficulties. Modeling complications result from the sensitivity of machine RVs to different settings of machine operating parameters and the difficulty to estimate machine performance in terms of NP under stochastic running conditions. Optimization complications result from the limited capability of mathematical modeling and experimental design in tackling the resulting large-in-space combinatorial optimization problem. To tackle such difficulties, therefore, the proposed approach presents a combined empirical modeling and Monte Carlo simulation (MCS) method to model the sensitive factors interdependencies and to estimate NP under stochastic running conditions. For combinatorial optimization, the approach utilizes a simulated-annealing (SA) heuristic to solve the defined PD problem and to provide optimal or near optimal settings to machine operating parameters. Approach procedure and potential benefits are illustrated through a case study example. |
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