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Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing

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  • Peng-Yeng Yin

    (Department of Information Management, National Chi Nan University, No. 1, University Rd., Puli, Nantou 54561, Taiwan
    Institute of Strategy and Development of Emerging Industry, National Chi Nan University, No. 1, University Rd., Puli, Nantou 54561, Taiwan)

  • Hsin-Min Chen

    (Institute of Strategy and Development of Emerging Industry, National Chi Nan University, No. 1, University Rd., Puli, Nantou 54561, Taiwan)

  • Yi-Lung Cheng

    (Department of Electrical Engineering, National Chi Nan University, No. 1, University Rd., Puli, Nantou 54561, Taiwan)

  • Ying-Chieh Wei

    (Institute of Strategy and Development of Emerging Industry, National Chi Nan University, No. 1, University Rd., Puli, Nantou 54561, Taiwan)

  • Ya-Lin Huang

    (Department of Information Management, National Chi Nan University, No. 1, University Rd., Puli, Nantou 54561, Taiwan)

  • Rong-Fuh Day

    (Department of Information Management, National Chi Nan University, No. 1, University Rd., Puli, Nantou 54561, Taiwan)

Abstract

It is estimated that 1 billion waste tires are generated every year across the globe, yet only 10% are being processed, and much rubber waste is yielded during manufacturing. These waste tires and rubber scraps are poisonous to the environment when processed via incineration and landfill. Rubber circular manufacturing is an effective solution that reduces not only rubber waste but also raw material costs. In this paper we propose a two-line flowshop model for the circular rubber manufacturing problem (CRMP), where the job sequence of two production lines is appropriately aligned to obtain the shortest makespan while guaranteeing that sufficient rubber waste yielded in the first line is ready to be reused for circular production in the second line. A genetic algorithm (GA) is developed, and the design of its genetic operations is customized to the CRMP context to achieve efficient and effective evolution. The experimental results with both real and synthetic datasets show that the GA significantly surpasses two heuristics in the literature by delivering the minimum makespan, which is 3.4 to 11.2% shorter than those obtained by the two competing methods.

Suggested Citation

  • Peng-Yeng Yin & Hsin-Min Chen & Yi-Lung Cheng & Ying-Chieh Wei & Ya-Lin Huang & Rong-Fuh Day, 2021. "Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2576-:d:507510
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    References listed on IDEAS

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    Cited by:

    1. Rohit Agrawal & Vishal A. Wankhede & Anil Kumar & Sunil Luthra & Abhijit Majumdar & Yigit Kazancoglu, 2022. "An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling," Operations Management Research, Springer, vol. 15(3), pages 609-626, December.

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