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An optimal variable pricing model for container line revenue management systems

Author

Listed:
  • Ming Yin

    (Shanghai Maritime University)

  • Zheng Wan

    (Shanghai Maritime University)

  • Kap Hwan Kim

    (Pusan National University)

  • Shi Yuan Zheng

    (Shanghai Maritime University)

Abstract

This paper discusses a variable pricing method, which is a special technique of revenue management, in the context of the competitive market of the liner shipping industry. Most papers on competitive variable pricing are based on two fundamental assumptions: (i) the length of a price-adjusting period is short enough so that in each period at most one customer can arrive; and (ii) the real-time inventory levels of all firms constitute public information. This paper relaxes both assumptions so that each interval between two consecutive freight rate changes allows more than one shipper to arrive and that a line knows only its competitors’ initial carrying capacity at the beginning of the sales process. A multi-iteration of genetic algorithm is proposed and numerically tested.

Suggested Citation

  • Ming Yin & Zheng Wan & Kap Hwan Kim & Shi Yuan Zheng, 2019. "An optimal variable pricing model for container line revenue management systems," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(2), pages 173-191, June.
    • Handle: RePEc:pal:marecl:v:21:y:2019:i:2:d:10.1057_s41278-017-0082-8
    DOI: 10.1057/s41278-017-0082-8
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    References listed on IDEAS

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    Cited by:

    1. Zhong, Huiling & Zhang, Fa & Gu, Yimiao, 2021. "A Stackelberg game based two-stage framework to make decisions of freight rate for container shipping lines in the emerging blockchain-based market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    2. Wang, Yadong & Gu, Yuyun & Wang, Tingsong & Zhang, Jun, 2022. "A risk-averse approach for joint contract selection and slot allocation in liner container shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).

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