以隨機顧客行為分析易腐性產品之動態定價策略

Abstract

在超市系統中，管理易腐產品是一項挑戰。產品的劣化是庫存損失的主要原因，一旦產品超過有效期限，其價值便會喪失並最終成為廢棄物。這種情況在處理易腐產品時可能導致多種問題。本研究主要探討如何透過定價策略來解決此議題。考慮到產品的同質性，本研究的目標是以折扣率與最低價格作為決策變數，以最大化利潤。此外，由於易腐產品需要冷藏儲存，本研究亦納入庫存持有成本以及因儲存與產品劣化所產生的碳排放成本。 本研究建立了一個純隨機模型，以充分捕捉顧客行為的本質，並提出一種折扣定價策略，以最大化預期利潤。由於所提隨機模型的複雜性，最佳解需透過電腦模擬來近似求得。本研究針對多種情境進行測試，並針對三個主要參數（顧客的購買意願、顧客到達率（λ）、與批次產品數量）進行不同設定的實驗。我們測試了顧客購買意願的隨機與固定變數，以及同質與非同質的顧客到達模式。模擬結果顯示在不同情境下的最佳設定，並分析了當顧客到達率與批次數量增加時，所選折扣率的影響。

Managing perishable products can be challenging in supermarket systems. Deterioration is a major cause of inventory loss; once products pass their expiration date, they lose value and become waste. This situation can lead to multiple problems when dealing with perishable goods. This study primarily discusses how to address this issue through pricing strategies. By considering homogeneous products, the aim is to maximize profit using discount rates and minimum prices as decision variables. Since perishable products require cold storage, we also factor in holding costs and emission costs, both from inventory holding and deteriorated items. We developed a purely stochastic model to adequately capture the essence of customers behavior and proposed a discounted pricing strategy to maximize the expected profit. Due to the complexity of the proposed stochastic model, the optimal solution is approximated by computer simulation. Multiple scenarios were examined with different settings for three parameters: customers' willingness to buy, customer arrival rates (λ), and batch products. We tested both stochastic and constant variables for customers' willingness to buy, as well as homogeneous and non-homogeneous customer arrivals. The simulation results indicate the best settings for each scenario and the impact of the chosen discount rate as customer arrival rates and the number of batches increase.