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  • 學位論文

供應商產能有限及價格折扣下多產品多供應商最佳化採購決策

Optimal Procurement Policies for Multi-Product Multi-Supplier with Capacity Constraint and Price Discount

指導教授 : 丁慶榮
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摘要


隨著採購成本成為多數企業總成本的很大百分比時,採購機能已成為以供應鏈為導向之工業經濟中重要的議題;因此,供應商的選擇及訂購數量的決定對採購部門而言是相當重要的決策。 本研究提出一非線性混合型整數規劃模式來進行供應商的遴選及數量分配,模式中包含存貨成本、具有價格折扣的採購成本、配送成本、固定成本及壞料成本,同時也加入了產能限制、前置時間及品質等限制。問題模式中因含有價格折扣,使非線性的目標函數為不連續性;然這類問題具有複雜且不易求解的特性,故本研究利用粒子群最佳化(PSO)演算法求解問題。PSO初始解一般為隨機產生,因此,本研究提出一啟發式演算法來改善PSO的效率。啟發式演算法中以配送成本、採購成本、存貨成本及壞料成本計算供應商的平均單位成本,同時根據產能限制或料件需求來分配料件給平均單位成本最小的供應商,每一次分配後即更新料件的剩餘需求量及各供應商的平均單位成本,重覆執行分配直到所有料件皆分配完畢,分配的結果將成為PSO演算法初始解中的其中一組解。 研究中以三個簡例測試問題,結果顯示PSO演算法中含有演算法初始解之結果其效率與品質皆優於PSO初始解為完全隨機的結果值;最後,將PSO的供應商選擇及數量分配的結果以LINGO進行修正,PSO找到之最佳解與LINGO之最佳解的誤差在0.01%之下。參數敏感度分析中,供應商產能、配送成本、採購成本及固定成本改變時,不但會影響總成本,同時也會影響供應商的選擇及數量分配。

並列摘要


The purchasing function is taking on increasing importance in today’s supply-chain oriented industrial economy, as purchases from suppliers account for a large percentage of the total costs for many firms. The selection of suppliers and the determination of order quantities to be placed with those suppliers are important decisions in purchasing department. In this research, a mixed integer non-linear programming model is presented to select suppliers and determine the order quantities. The model considers the cost of purchasing which takes into account quantity discount, the cost of transportation, the fixed cost for establishing suppliers, the cost for holding inventory, and the cost of receiving poor quality parts. The capacity constraints for suppliers, quality and lead time requirements for the parts are also considered in the model. Since the purchasing cost, which is a decreasing step function of order quantities, introduces discontinuities to the non-linear objective function, it is not easy to employ traditional optimization methods. Thus, a heuristic algorithm, called particle swarm optimizer (PSO), is used to find the (near) optimal solution. PSO usually generates initial solutions randomly. To improve the PSO solution quality, a heuristic is proposed to find an initial solution based on the average unit cost including transportation, purchasing, inventory, and poor quality part cost. The solution yielded by the heuristic is used as one of PSO initial solutions. Three example problems are tested in this research. The results show that PSO with the initial solution heuristic provides better solutions than those with PSO algorithm only. For each problem, we use LINGO to solve the quantity allocation problem based on the selected suppliers provided by PSO algorithm. Compared with the optimal solutions found by LINGO, the error yielded by PSO with initial solution heuristic are within 0.01%. From the sensitivity analysis of the parameters, capacity constraint, transportation, purchasing, and fixed cost are important in making supplier selection and quantity allocation decision.

參考文獻


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被引用紀錄


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洪歆雅(2007)。整合類神經網路與粒子群演算法為輔之模糊神經網路於供應商選擇之應用〔碩士論文,國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841%2fNTUT.2007.00122
卓峻瑋(2012)。以擾亂式的同化作用改善帝國競爭演算法〔碩士論文,元智大學〕。華藝線上圖書館。https://doi.org/10.6838%2fYZU.2012.00091

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