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Parallel Machine Scheduling with Due Date-to-Deadline Window, Order Sharing and Time Value of Money

Author

Listed:
  • Yinfeng Xu

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, P. R. China)

  • Rongteng Zhi

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, P. R. China)

  • Feifeng Zheng

    (Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, P. R. China)

  • Ming Liu

    (School of Economics & Management, Tongji University, Shanghai 200092, P. R. China)

Abstract

Motivated by a variety of applications in sharing economy, we study an identical parallel machine scheduling problem with due date-to-deadline window by jointly considering machine sharing and the time value of money. A factory owns a set of parallel identical machines and processes a set of production orders within a finite time period. In the sharing setting, the factory may also rent external machines to handle a part of orders by paying some extra cost. The factory aims to determine the sharing policy of the production orders and the scheduling rule of machines, to maximize its total future value of profits by satisfying the orders. To the best of our knowledge, there are no previous results for this problem. In this work, a mathematical programming model is derived, and a problem-specific genetic algorithm and a heuristic are proposed to solve large-scale instances. Numerical experiments using randomly generated instances are carried out to evaluate the effectiveness and efficiency of the proposed solution methods.

Suggested Citation

  • Yinfeng Xu & Rongteng Zhi & Feifeng Zheng & Ming Liu, 2022. "Parallel Machine Scheduling with Due Date-to-Deadline Window, Order Sharing and Time Value of Money," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 39(02), pages 1-32, April.
    • Handle: RePEc:wsi:apjorx:v:39:y:2022:i:02:n:s021759592150024x
    DOI: 10.1142/S021759592150024X
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