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A multi-period ordering and clearance pricing model considering the competition between new and out-of-season products

Author

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  • Xiang Li

    (Nankai University)

  • Guohua Sun

    (Shandong University of Finance and Economics)

  • Yongjian Li

    (Nankai University)

Abstract

The joint management of pricing and inventory for perishable products has become an important problem for retailers. This paper investigates a multi-period ordering and clearance pricing model under consideration of the competition between new and out-of-season products. In each period, the ordering quantity of the new product and the clearance price of the out-of-season product are determined as decision variables before the demand is realized, and the unsold new product becomes the out-of-season one of the next period. We establish a finite-horizon Markov decision process model to formulate this problem and analyze its properties. A traditional dynamic program (DP) approach with two-dimensional search is provided. In addition, a myopic policy is derived in which only the profit of the current period is considered. Finally, we apply genetic algorithm (GA) to this problem and design a GA-based heuristic approach, showing by comparison among different algorithms that the GA-based heuristic approach is more performance sound than the myopic policy and much less time consuming than the DP approach.

Suggested Citation

  • Xiang Li & Guohua Sun & Yongjian Li, 2016. "A multi-period ordering and clearance pricing model considering the competition between new and out-of-season products," Annals of Operations Research, Springer, vol. 242(2), pages 207-221, July.
    • Handle: RePEc:spr:annopr:v:242:y:2016:i:2:d:10.1007_s10479-013-1498-x
    DOI: 10.1007/s10479-013-1498-x
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    Cited by:

    1. Mahmoud Dehghan Nayeri & Amir-Nader Haghbin & Abdolkarim Mohammadi-Balani & Karim Bayat, 2020. "A multi-objective mean–variance mathematical programming approach to combined phase-out and clearance pricing strategy for seasonal products: case study of a Jeans retailer," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(3), pages 210-217, June.
    2. Mou, Shandong & Robb, David J. & DeHoratius, Nicole, 2018. "Retail store operations: Literature review and research directions," European Journal of Operational Research, Elsevier, vol. 265(2), pages 399-422.
    3. Liu, Jiaguo & Zhao, Huida & Li, Jian & Yue, Xiaohang, 2021. "Operational strategy of customized bus considering customers’ variety seeking behavior and service level," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. Zhou, Shenghan & Zhou, Yuliang & Zuo, Xiaorong & Xiao, Yiyong & Cheng, Yang, 2018. "Modeling and solving the constrained multi-items lot-sizing problem with time-varying setup cost," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 202-207.
    5. Kannan Govindan, 2016. "Evolutionary algorithms for supply chain management," Annals of Operations Research, Springer, vol. 242(2), pages 195-206, July.

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