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Order Acceptance and Scheduling Problem with Carbon Emission Reduction and Electricity Tariffs on a Single Machine

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  • Shih-Hsin Chen

    (Department of Information Management, Cheng Shiu University, Kaohsiung 833, Taiwan
    Current address: No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City 333, Taiwan.
    These authors contributed equally to this work.)

  • Yeong-Cheng Liou

    (Department of Healthcare Administration and Medical Informatics, Research Center of Nonlinear Analysis and Optimization, Kaohsiung Medical University, Kaohsiung 807, Taiwan
    These authors contributed equally to this work.)

  • Yi-Hui Chen

    (Department of Information Management, Chang Gung University, Taoyuan City 333, Taiwan
    These authors contributed equally to this work.)

  • Kun-Ching Wang

    (Department of Information Technology & Communication, Shih Chien University, Kaohsiung City 845, Taiwan
    These authors contributed equally to this work.)

Abstract

Order acceptance and scheduling (OAS) problems are realistic for enterprises. They have to select the appropriate orders according to their capacity limitations and profit consideration, and then complete these orders by their due dates or no later than their deadlines. OAS problems have attracted significant attention in supply chain management. However, there is an issue that has not been studied well. To our best knowledge, no prior research examines the carbon emission cost and the time-of-use electricity cost in the OAS problems. The carbon emission during the on-peak hours is lower than the one in mid-peak and off-peak hours. However, the electricity cost during the on-peak hours is higher than the one during mid-peak and off-peak hours when time-of-use electricity (TOU) tariff is used. There is a trade-off between sustainable scheduling and the electricity cost. To calculate the objective value, a carbon tax and carbon dioxide emission factor are included when we evaluate the carbon emission cost. The objective function is to maximize the total revenue of the accepted orders and then subtract the carbon emission cost and the electricity cost under different time intervals on a single machine with sequence-dependent setup times and release date. This research proposes a mixed-integer linear programming model (MILP) and a relaxation method of MILP model to solve this problem. It is of importance because the OAS problems are practical in industry. This paper could attract the attention of academic researchers as well as the practitioners.

Suggested Citation

  • Shih-Hsin Chen & Yeong-Cheng Liou & Yi-Hui Chen & Kun-Ching Wang, 2019. "Order Acceptance and Scheduling Problem with Carbon Emission Reduction and Electricity Tariffs on a Single Machine," Sustainability, MDPI, vol. 11(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5432-:d:272423
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    References listed on IDEAS

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    Cited by:

    1. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    2. Dar-Li Yang & Wen-Hung Kuo, 2019. "Minimizing Makespan in A Two-Machine Flowshop Problem with Processing Time Linearly Dependent on Job Waiting Time," Sustainability, MDPI, vol. 11(24), pages 1-18, December.
    3. R. Micale & C. M. La Fata & M. Enea & G. La Scalia, 2021. "Regenerative scheduling problem in engineer to order manufacturing: an economic assessment," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1913-1925, October.
    4. Fei Zou & Yanju Zhou & Caihua Yuan, 2020. "The Impact of Retailers’ Low-Carbon Investment on the Supply Chain under Carbon Tax and Carbon Trading Policies," Sustainability, MDPI, vol. 12(9), pages 1-27, April.
    5. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2021. "Job Scheduling under Time-of-Use Energy Tariffs for Sustainable Manufacturing: A Survey," LIDAM Discussion Papers CORE 2021019, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).

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