應用多目標微粒群演算法求解(s, Q)存貨控制最佳化模式

Application of Multi-objective Particle Swarm Algorithm to Inventory Control Optimization Models

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

《東吳經濟商學學報》 69期 (2010/06) Pp. 47-81

https://doi.org/10.29735/SJEB.201006.0003

摘要

存貨管理的目的是如何運用最少的成本維持高度的服務水準，並降低缺貨的可能性以滿足顧客對產品的需求，基本上，存貨管理為一個多目標最佳化問題。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。此外，為了避免多目標存貨控制模式出現多餘的目標，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差異。最後，將不同存貨模式在缺貨後補與銷售損失的狀況下求解的結果進行比較。文中發現在銷售損失的狀況下，廠商會特別擔心因缺貨而造成的銷售損失，因此會更注重庫存的管理，而讓平均安全因子提高，但其批量大小會少於缺貨後補模式。

關鍵字

存貨管理 ； 多目標最佳化 ； 微粒群演算法 ； 缺貨後補模式 ； 銷售損失模式

並列摘要

The goal of inventory management is how to maintain high level of service quality by using least cost and how to reduce the possibility of shortage in order to satisfy the requirements of customers at the meantime. So inventory management could be regard as a multi-objective optimization problem (MOOP). This work extends Agrell's (1995) 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. Next, in order to avoid the redundancies 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, backorder model is compared to lost sales model. On the views of decision variables, the average safety factor in lost sales model is grater than those in backorder model, but lot size is smaller than backorder model.

並列關鍵字

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

參考文獻

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