多目標銷售損失存貨模式之決策問題與解法探討

Discussion of Decision Problems and Algorithms in Multi-Objective Lost Sales Inventory Models

許晉雄(Chin-Hsiung Hsu) ； 鄒慶士(Ching-Shih Tsou)

《東海管理評論》 11卷1期 (2010/07) Pp. 95-132

https://doi.org/10.29724/TMR.201007.0004

摘要

存貨管理對於企業來說是極為重要的工作，其目的是如何運用最少的成本維持高度的服務水準，降低缺貨的可能性以滿足顧客對產品的需求。如何在這些衝突目標間做出權衡取捨，便是多目標存貨控制所面臨的一大挑戰。本研究將Agrell (1995)提出的缺貨後補下三目標(s, Q)存貨控制模式延伸至銷售損失的情況下，運用加入區域搜尋與群集機制的混合式多目標微粒群最佳化來求解不同模式的存貨控制問題，並將結果與強健柏拉圖進化式演算法比較，發現混合式多目標微粒群最佳化的非凌越解在三項績效衡量指標上明顯的勝過強健柏拉圖進化式演算法。此外，為了避免多目標存貨控制模式出現多餘(redundant)的目標，本研究將三個目標之存貨控制模式轉換為兩個雙目標之存貨控制模式，分別命名為缺貨次數與缺貨數量存貨模式，並進行求解與比較不同模型之間的差異。

關鍵字

存貨管理 ； 多目標最佳化 ； 微粒群演算法 ； 銷售損失

並列摘要

Inventory management is an important work to the enterprise. Inventory control involves tradeoff between conflicting objectives such as cost minimization and inventory availability in order to attain the goal of customer satisfaction. So inventory management could be regard as a multi-objective optimization problem (MOP). This work extends Agrell's inventory control problem from backorder to lost sales, and applies hybrid Multi-Objective Particle Swarm Optimization (HMOPSO), which incorporates a local search and clustering method, to an inventory planning problem. The way of multi-objective analysis can determine lot size and safety factor simultaneously under the objectives of minimizing the expected total relevant cost and some measurements about stockout. HMOPSO is compared with Strength Pareto Evolutionary Algorithm (SPEA). The comparative results show that the HMOPSO surpasses the SPEA on the three performance indexes. However, HMOPSO can find lots of non-dominated solution in a single run and traditional approaches just search for one in a single run. Otherwise, in order to avoid the redundance in objective functions, we reorient Agrell's model to two multi-objective inventory control models emerge redundant objective, base on Agrell's objective, we construct two bi-objective inventory models, named the stockout occasions model (N-model) and the number of items stocked out model (B-model). Finally, we find the non-dominated solutions of each model and make some comparisons among these two inventory models.

並列關鍵字

Inventory Management ； Multi-Objective Optimization ； Particle Swarm Optimization ； Lost Sales

參考文獻

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