基于需求学习的数字化鲜活品拍卖平台批量优化策略研究

doi:10.16381/j.cnki.issn1003-207x.2022.1410

Abstract

Abstract:

Due to the characteristics of large fluctuations in supply and demand， large transaction volume and strong timeliness in the fresh product market， the transaction prices of the auction items under different auction batches and sequences are quite different， and it is difficult for the auction platform to achieve the optimal revenue. In practice， the auction platform adopts a solidified auction plan decision based on the auctioneer's personal experience， which is difficult to adapt to complex situations where demand information is unknown and dynamically changing. From the perspective of the auction platform， this article explores how to dynamically adjust the auction batch to improve the long-term total revenue of the auction platform. Firstly， the influence of the game between bidders on the equilibrium bidding is analyzed， and the equilibrium bidding strategy of the bidders under the given auction batch is obtained； secondly， based on the price distribution and demand distribution of specific bidders， the single-stage revenue function of the auction platform is established； finally， a multi-stage revenue optimization model based on Bayesian-Markov decision process is built while dynamically learning demand signal. The online rolling horizon optimization method is applied to solve the model. Through a large number of numerical experiments， the management inspirations are obtained as follows：（1） Based on the continuous updating and learning of demand information， the auction revenue can be effectively improved by dynamically setting the appropriate auction batch， especially in the period of oversupply and seasonal fluctuation of demand；（2） When the gap between the initial belief and the true distribution is the same， the benefit of information learning under underestimated demand is higher than that of information learning under overestimated demand. Therefore， when it is difficult to predict the demand， the decision made by auction platform under the underestimated demand is better than the overestimated demand；（3） The auction platform can dynamically configure the optimal total auction stage， the optimal reserve price， and the optimal auction batch simultaneously to increase total revenue of auction platform.

Key words: multi-item sequential auction, auction variable decision, demand learning, Bayesian-Markov decision process

CLC Number:

F713.359

Xiangtianrui Kong, Yanhong Long, Kaize Yu, Zelong Yi, Pengyu Yan. Optimal Lot-sizing for Digital Perishable Auction Platform with Demand Learning[J]. Chinese Journal of Management Science, 2025, 33(6): 222-232.

Figures/Tables 8

符号	说明
t	拍卖场次，t=1,2,…,T
$N t$	t场次拍卖开始前商品剩余量
$v ¯$	起拍价
$v ̲$	保留价
$v j$	竞拍者 $j$ 对商品的估价
$F (∙)$	竞拍者的估价累积分布
$f (∙)$	竞拍者的估价概率分布
$k t$	即t场次拍卖的参拍人数，服从参泊松分布
$λ t$	t场次拍卖开始前拍卖平台对竞拍者到达人数所服从泊松分布参数的估计
$b j t$	竞拍者 $j$ 在 $t$ 场次的意愿竞价
$b (x t, k t)$	$t$ 场次在 $k t$ 个竞价中第 $x t$ 大的意愿竞价
$v (x t, k t)$	$t$ 场次在 $k t$ 个竞价中第 $x t$ 大的估价
决策变量
$x t$	t场次供应的拍卖批量

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Optimal Lot-sizing for Digital Perishable Auction Platform with Demand Learning

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