A Multi-Fidelity Model Approach for Simultaneous Scheduling of Machines and Vehicles in Flexible Manufacturing Systems

James T. Lin, Chun-Chih Chiu (), Edward Huang () and Hung-Ming Chen ()
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James T. Lin: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Section 2, Guangfu Road, Hsinchu 300, Taiwan R. O. C.
Chun-Chih Chiu: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Section 2, Guangfu Road, Hsinchu 300, Taiwan R. O. C.
Edward Huang: Department of System Engineering and Operations Research, George Mason University, Fairfax, VA 22030, USA
Hung-Ming Chen: Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Section 2, Guangfu Road, Hsinchu 300, Taiwan R. O. C.

Asia-Pacific Journal of Operational Research (APJOR), 2018, vol. 35, issue 01, 1-20

Abstract: Driven by sensor technologies and Internet of Things, massive real-time data from highly interconnected devices are available, which enables the improvement of decision-making quality. Scheduling of such production systems can be challenging as it must incorporate the latest data and be able to re-plan quickly. In this research, a multi-fidelity model for simultaneous scheduling problem of machines and vehicles at flexible manufacturing system has been proposed. In order to improve the computational efficiency, we extend the framework, called multi-fidelity optimization with ordinal transformation and optimal sampling, with combining with the K-means method. The proposed framework enables the benefits of both fast and inexpensive low-fidelity models with accurate but more expensive high-fidelity models. Results show that this approach can significantly decrease computational cost compared with other algorithms in the literature.

Keywords: Internet of Things; simultaneous scheduling problem; flexible manufacturing system; multi-fidelity scheduling algorithm (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0217595918500057

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