隨著零售業和電子商務的快速發展，如何在有限的展示空間內選擇並上架合適的商品以促進銷售，已成為店家關注的核心議題。除了上架策略，定價策略也是影響消費者購買行為的關鍵因素。本文旨在商品上架組合與定價的動態聯合最佳化問題，針對有限的上架容量及未知的消費者偏好進行動態決策，以期最大化店家多期的總期望利潤。本研究基於 Feng et al. (2023) 提出的時間樹模型（Temporal Tree Model），發展出價格相依樹模型（Price-Dependent Model），將商品價格納入消費者選擇行為模型，以此描述消費者在不同價格條件下的選擇偏好。在價格相依樹模型架構下，結合禁忌搜尋演算法進行商品組合探索，並以粒子群演算法求解對應定價策略，與傳統窮舉法相比，能於有限的函數評估資源下更快速地收斂至較佳解，顯著提升運算效率與解的品質。面對店家在消費者偏好未知情境下的決策問題，本研究採用湯普森抽樣（Thompson Sampling）演算法，藉由平衡探索與利用（exploration vs. exploitation）策略，有效學習消費者選擇傾向，並在降低探索成本的同時提升整體收益表現。數值模擬結果顯示，本方法相較於傳統最大似然估計（Maximum Likelihood Estimation, MLE）策略，在模型參數先驗分布符合均勻假設下，總體效能上具有顯著提升，其隨機實驗之中位數能降低 17 個百分點的相對遺憾損失。

With the rapid development of retail and e-commerce, determining how to select and display suitable products within limited shelf space to boost sales has become a core issue for retailers. In addition to assortment strategies, pricing strategies are also crucial factors influencing consumer purchasing behavior. This paper addresses the dynamic joint optimization problem of product assortment and pricing, aiming to maximize the total expected profit over multiple periods under limited capacity and unknown consumer preferences. Building on the Temporal Tree Model proposed by Feng et al. (2023), this study develops the Price-Dependent Tree Model, which incorporates product prices into the consumer choice model to describe preferences under varying price conditions. Within this framework, Tabu Search is employed to explore product assortments, and Particle Swarm Optimization is applied to determine pricing strategies. Compared to traditional exhaustive methods, this approach achieves faster convergence and higher solution quality with limited function evaluations. For decision-making under unknown preferences, Thompson Sampling is used to balance exploration and exploitation, effectively learning consumer choices and improving overall revenue. Numerical simulations show that, under the assumption of a uniform prior distribution of model parameters, this approach significantly outperforms Maximum Likelihood Estimation (MLE), reducing the median relative regret by approximately 17 percentage points in random experiments.

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