(s, Q)存貨控制缺貨後補模式之多目標最佳化分析

Multi-Objective Optimization Analysis of the (s, Q) Backorder Inventory Control Models

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

《台灣管理學刊》 10卷1期 (2010/02) Pp. 19-50

https://doi.org/10.6295/TAMJ.2010.1001.02

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，並將結果與傳統存貨控制求解方式及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，並與傳統存貨控制的求解法不相土下，但傳統的方式一次只能求取一組解，而混合式微粒群演算法基於多點並行的搜尋方式，可以一次求解多組非凌越解並提供多種決策的選擇，同特此演算法要調整的參數較少。

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

並列摘要

Inventory management is an important work to the enterprise. Traditional inventory models only involve single objective which relates to several cost concepts and/or service requirements. Even in its multi-objective formulation, most models have been solved by aggregation methods. Such solutions obtained are unsatisfactory because decision makers try to act through a surrogate variable with incomplete information. So inventory management could be regard as a multi-objective optimization (MOP) problem. This work analyzes Agrell's inventory control problem and applies hybrid Multi-Objective Particle Swann 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 algorithm is compared with the traditional inventory control approach (such as the simultaneous and sequential approach) and Strength Pareto Evolutionary Algorithm (SPEA). The comparative results show that the HMOPSO surpasses the SPEA on the three performance indexes, and is competitive with than traditional approaches. However, HMOPSO can find lots of non-dominated solution in a single run and traditional approaches just search for one in a single run.

並列關鍵字

Inventory Management ； Multi-Objective Optimization ； Particle Swann Optimization ； Backorder Model

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