Robust Strategy Analysis of Capacity Rationing in Advance Selling About Perishable Products Under Yield Uncertainty

SUN Caihong, LI Zhen, YU Hui

Journal of Systems Science and Mathematical Sciences ›› 2023, Vol. 43 ›› Issue (4) : 898-913.

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Journal of Systems Science and Mathematical Sciences ›› 2023, Vol. 43 ›› Issue (4) : 898-913. DOI: 10.12341/jssms22428

Robust Strategy Analysis of Capacity Rationing in Advance Selling About Perishable Products Under Yield Uncertainty

  • SUN Caihong1, LI Zhen2,3, YU Hui4
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Abstract

Capacity rationing in advance selling, a kind of complex selling model based on joint connection between supply and demand, is favored by enterprises to launch perishable products. To study the advance-selling strategy under the circumstance of yield uncertainty, a two-period robust newsvendor model is constructed, in which the manufacturer introduces capacity rationing in advance selling into regular selling. With the support of the robust decision, the model is studied on the basis of partial information of perishable products’ stochastic proportional yield. It is found that capacity rationing in advance selling can not only relieve the contradiction between supply and demand, caused by the yield-uncertainty risk, but also reduce the impact of this risk on expected profits. The threshold condition of whether to select capacity rationing in advance selling is determined by the coefficient of variation of stochastic proportional yield, two–period sales price and demand impact factor about perishable products. At the same time, the robust optimal advance-selling quantity and its influence factors are shown and the relevant strategy suggestions are offered.

Key words

Capacity rationing in advance selling / perishable products / yield uncertainty / robust analysis

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SUN Caihong , LI Zhen , YU Hui. Robust Strategy Analysis of Capacity Rationing in Advance Selling About Perishable Products Under Yield Uncertainty. Journal of Systems Science and Mathematical Sciences, 2023, 43(4): 898-913 https://doi.org/10.12341/jssms22428

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