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研究生:洪宛瑜
研究生(外文):Wan-Yu Hong
論文名稱:應用基因演算法於數量折扣的補貨問題
論文名稱(外文):Using genetic algorithm for replenishment problem with quantity discounts
指導教授:康鶴耀
指導教授(外文):He-Yau Kang
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:43
中文關鍵詞:存貨數量折扣混合整數規劃基因演算法觸控面板
外文關鍵詞:InventoryQuantity discountsMixed integer programmingGenetic algorithmsTouch Panel
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企業為了滿足顧客需求,提高競爭優勢,並達到企業追求總成本極小化目標。供應鏈最佳化問題愈來愈受到重視,供應鏈規劃之最佳化問題包含供應商評選和採購、需求的規劃和滿足、生產排程、配銷和售後服務等方面。而製造業在求解供應鏈規劃之最佳化問題時,一般較著重需求與供給的適當配合,以達到資源運用與分配之有效性與時效性,並使總成本極小化或利潤最大化。物料配置為供應鏈規劃系統的主要功能之一,其目的在將各種可行的供給配置給需求,但是在傳統上,物料配置的結果往往只能得到可行解,而無法得到最佳解。供應鏈規劃最佳化問題求解最常用有這三種方法:啟發式演算法、線性規劃、基因演算法。使用啟發式演算法來求解,很難在資源限制及成本極小化之間取得平衡點;使用線性規劃來求解,萬一變數或限制條件過多,很難得到合理解的規劃;基因演算法,是藉由基因組合交換的方式,不斷的產生新的解,來改善系統的效能。
因此,本研究以基因演算法為基礎,建構供應鏈物料配置決策模式,建立一最小化成本為目標的規劃模式,做為廠商在物料配置時的參考。本研究為探討以最小成本為目標下之最佳存貨模式及數量折扣問題。首先制定出一套混合整數規劃數學模式,並利用基因演算法搜尋近似解的特性,找出各種可行的供需之間的組合,而使得總成本為最小。本研究以台灣某家觸控面板製造商為例,並考慮其重要成本因素如採購、持有、訂購成本,於案例研究中以參數組合交叉比較,求出在最小成本下之參數組合。

The company in order to satisfy customer demands and improve competitive advantage and achieve business objectives to pursue the total cost minimization. The optimization problems of supply chain become more and more important. Supply chain planning optimization problems contain supplier selection and procurement, demand planning and fulfillment, production scheduling, distribution and after-sales service and so on. And manufacturing supply chain planning in solving optimization problems are generally more focused on the right with the demand and supply, and allocation of resources to achieve the effectiveness and timeliness, and minimize the total cost or profit maximization. Material is configured to supply chain planning system one of the main functions, the purpose of supply in the various possible configurations to demand. But in traditionally, the material configuration results are often only got a feasible solution, and can not find the optimal solution. Supply chain planning optimization problem solving the most common are these three methods: heuristics, linear programming, and genetic algorithms. Using the heuristic algorithm to solve will be difficult to resource constraints and cost minimization strike a balance between; use linear programming to solve, if too many variables or constraints will be difficult to get together to understand the planning; genetic algorithm , is the combination of genes by way of exchange, constantly generate new solutions to improve system performance.
This study is to find the best inventory model with quantity discounts under the objective of attaining lowest cost. A mixed integer programming model is constructed first; genetic algorithm is applied next to find possible combinations between supply and demand with near-optimal solutions. A case study of a touch panel manufacturer in Taiwan is used as an example, and cost factors, including purchasing cost, holding cost and ordering cost are considered. The parameters are cross compared to find the combination with the minimum cost.

摘要 i
ABSTRACT ii
誌謝 iv
目 錄 v
表目錄 1
圖目錄 2
第一章 緒論 3
1.1研究背景與動機 3
1.2研究目的 3
1.3研究流程 4
第二章 文獻探討 6
2.1 存貨管理 6
2.1.1存貨管理的重要性 6
2.1.2存貨的種類 7
2.1.3存貨的相關成本 8
2.1.4批量訂購方法 8
2.2基因演算法 14
2.2.1基因演算法的簡介及特性 14
2.2.2基因演算法演算流程 14
2.2.3基因演算法主要特性 19
2.3 觸控面板 20
2.3.1 觸控面板應用 20
2.3.2 觸控面板技術 20
2.3.3 觸控面板發展 22
第三章 研究方法與步驟 24
3.1 混合整數規劃數學模式 24
3.1.1符號與假設 24
3.1.2模式建構 26
3.2 基因演算法的參數設定 27
第四章 案例研究 28
4.1 輸入基本參數 28
4.2 實驗結果及分析 30
4.3 結果與討論 35
第五章 結論與未來研究方向 39
5.1 結論 39
5.2 未來研究方向 40
參考文獻 41


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