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研究生： 王金龍
Bertinus Enrico Rahardjo
論文名稱： 考量參考價格與有限庫存之貝氏需求學習動態訂價
Bayesian Dynamic Pricing with Demand Learning affected by Reference Price and Limited Inventory
指導教授： 莊雅棠
Chuang, Ya-Tang
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業與資訊管理學系
Department of Industrial and Information Management
論文出版年： 2022
畢業學年度： 110
語文別： 中文
論文頁數： 46
中文關鍵詞： 動態定價 、貝氏需求學習 、參考價格
外文關鍵詞： Dynamic pricing, Bayesian demand learning, Reference price
相關次數： 點閱：108  下載：10 
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    • 本研究建立一個考量有限庫存(limited inventory)與參考價格(reference price) 的動態定價模型，參考價格為顧客所能得知的過往產品價格。由於顧客可得知參考價格，故顧客願付價格(willingness-to-pay) 會受到參考價格影響。需求受顧客願付價格影響但決策者在事前無任何願付價格資訊，雖事前無任何資訊但隨著期數增加決策者可透過定價來觀察銷售資訊來進行學習。本研究模型因考量雙向審查(two-sided censoring)、有限庫存與參考價格使模型複雜度提升求解十分困難，因此本研究致力於發展啟發式演算法以求取最佳價格。本研究提出兩種演算法，分別為短視近利策略法(myopic policy)與無後驗信念更新法(no posterior belief update)。透過數值分析在短期數的情況下本研究提出的兩種演算法表現皆為良好(與最佳解差距小於5%)，在長期數的則是無後驗信念更新法表現較優於短視近利策略法。

    We consider a dynamic pricing problem in which a seller sells a limited number of a single product under demand uncertainty. The customers’ willingness-to-pay (WtP) is unknown but can be learned over time through sales data. A Bayesian framework is adopted for demand learning. In particular, customers' purchase behavior is affected by not only the posted price but also the reference price. Reference prices are the customer's perspective of the past prices. The goal of this research is to develop heuristic algorithms to approximate the optimal price decision as the model is complicated to solve. The model complexity is caused by the two-sided censoring, limited inventory, and reference prices. We developed two approaches for this problem, which are myopic policy and no posterior belief update. Our numerical analysis shows that both heuristics show great results for a short-period problem, which has a performance gap of less than 5% compared to the optimal solution. For a problem which has limited inventory and a longer period, no update policy shows better results than a myopic policy.

    摘要 i Abstract ii Acknowledgments iii Table of Contents iv List of Tables vii List of Figures viii Chapter 1: Introduction 1 1.1 Introduction 1 1.2 Research Objective 5 1.3 Research Outline 5 Chapter 2: Literature Review 6 2.1 Dynamic Pricing 6 2.2 Dynamic Pricing with Demand Learning 7 2.3 Bayesian Dynamic Programming Approach 8 2.4 Dynamic Pricing with Reference Price Effect 9 2.5 Demand Censoring 10 Chapter 3: Mathematical Programming Model 12 3.1 Problem Description 12 3.2 Problem Formulation 14 3.2.1 Single Period Formulation 14 3.2.2 Optimal Decision of Single Period Problem 16 3.2.3 Dynamic of Demand Learning Process 17 3.2.4 Dynamic of Reference Price Process 17 3.2.5 Dynamic Programming Formulation 18 3.2.6 Unnormalized Prior Transformation 19 3.3 Problem Analysis 20 Chapter 4: Heuristic Algorithms 25 4.1 Heuristic I 25 4.2 Heuristic II 26 Chapter 5: Numerical Analysis 28 5.1 No Reference Price Model VS Reference Price Model 30 5.2 Heuristic Algorithm Approaches Performance 32 5.2.1 Heuristic Algorithm Approaches VS Optimal Solution - Three Period Problem 32 5.2.2 Heuristic Algorithm Approaches VS Optimal Solution - Four Period Problem 34 5.2.3 Heuristic Algorithm Approaches Comparison - Seven Period Problem36 5.3 Sensitivity Analysis 38 Chapter 5: Conclusion and Future Research 41 References 43 Appendices 46

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