本論文研究單一零售商，針對其販售有限銷售時間商品於顧客，所面臨的商品定價決策問題。此類問題大多探討在商品銷售期間有限下，零售商尋求最佳定價策略以達到商品銷售營收最大化。本論文則是發展簡易的啟發式演算法來解決零售商所面臨的定價問題，再進一步描述由本研究發展的定價方法求得的定價和收益現象。在研究中，先介紹離散時間定價策略以及宣告折扣策略定價模型，再參考宣告折扣策略定價模型，保留宣告折扣策略定價簡單易懂且容易執行的優點，並消除宣告折扣策略定價無法反應真實銷售情況的缺點，發展以最後一期價格和折扣率為決策變數的啟發式演算法。最後再以數值案例分別探討在保留價格分配參數不隨時間變動以及保留價格分配參數隨時間變動的情況下，離散時間定價、宣告折扣策略定價模型和啟發式演算法三種定價方法的求解結果，衡量與比較三種不同定價決策之績效並解釋其管理意涵。 This thesis studies pricing policies when selling seasonal goods in retail stores. We consider a retailer who sells a single good during a short sales season, and the cost of the good is a sunk cost. Moreover, the seller orders the good at the beginning of the planning horizon and that reorders are not allowed. We allow a nonhomogeneous Poisson arrival process to describe the arrival of customers to the store, and each customer has an individual reservation price based on which he makes his own purchasing decision. In this research, we first introduce periodic pricing review policy that is the optimal and announced discount policy that consists of an initial price and a fixed discount per period. Although announced discount policy has the advantage that reduces all the management costs associated with changing prices, retailers loses the flexibility to react to market conditions. Therefore, we develop a heuristic pricing policy that consists of the price of the period and a discount rate for eliminating the negative effect resulted from announced discount policy. Finally, we perform two numerical studies to compare the performance of the proposed heuristic to those obtained under the other two pricing policies. We show that our heuristic pricing policy provides the near optimal expected revenue that is better than that obtained by announced discount policy. Briefly, the purpose of this research is to propose a near optimal pricing policy that is simpler to implement in practice for seasonal goods.
