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  • 學位論文

多種需求模式的庫存政策

Inventory policies with multiple demand patterns

指導教授 : 陳平舜

摘要


具有多種需求模式的庫存策略可以與單個產品或多個產品相關。一個項目的動態條件可以創建不同的需求模式,並且需要一個集成的庫存策略。 在多個項目的情況下,它們之間的關係需要一個集成的庫存。 因此,開發庫存模型以處理多種需求模式是必不可少的。 本論文討論了與多種需求模式相關的三個子主題。 首先,具有多種需求模式的順序關係的單項庫存策略討論了市場生命週期短和劣化的產品。 本分題以智能手機行業來代表科技產品的快速增長。 市場生命週期是通過在增長、成熟和衰退階段之後不斷變化的需求模式來描述的。 銷售期的結束時間成為獲得最大利潤的決策變量,然後轉換為訂貨量。 該訂單僅在期初發出一次。 該模型使用非線性規劃,搜索解決方案使用非線性廣義減少梯度。 通過考慮需求模式的變化來獲得更準確的結果對於避免庫存系統中的短缺和不受控制的庫存至關重要。 其次,具有多關係需求模式的單項庫存政策討論了製藥行業的庫存政策。 長期治療或療法可能會反複使用藥物。 它也發生在化療藥物中。 患者進行的治療包括幾個週期,因此當前對藥物的需求會受到先前患者需求的影響。 物料需求計劃(MRP)系統是一種可以結合依賴和獨立需求率以適應以前和新患者需求率的方法。 仿真模型用於查看 MRP 系統與流行的再訂貨點系統相比的優勢。 結果表明 MRP 系統優於再訂貨點系統。考慮當前需求與先前需求之間的依賴關係對庫存策略的性能有很大影響。 第三,具有多個獨立需求模式的多項目庫存策略討論了具有共同訂貨成本的聯合補貨。 這些項目可能有不同的需求模式,必須由集成的庫存策略處理。 具有多個庫存單位的零售業是這個子主題的一個很好的例子。 一個訂單中每個項目的訂貨數量是獲得最小總成本的決策變量。 此問題使用非線性規劃建模並使用 CPLEX 尋找解決方案。 仿真模型用於比較有和沒有優化模型的聯合補貨。所提議的程序給出了更好的總體成本。 這三項研究顯示了在特定情況下具有多種需求模式的庫存模型的優勢,並有望補充庫存模型中的知識,並為管理和未來研究提供見解。

關鍵字

庫存政策

並列摘要


Inventory policies with multiple demand patterns can be related to a single product or multi-product. Dynamic conditions on an item can create different demand patterns and require an integrated inventory policy. In multi-item situations, the relationships between them require an integrated inventory. Thus, the inventory models development to handle multiple demand patterns is essential. This dissertation discusses three subtopics related to multiple demand patterns. First, a single-item inventory policy with sequential relationships with multiple demand patterns discusses products with short market life cycles and deterioration. This subtopic uses the smartphone industry to represent the rapid growth of technological products. The market life cycle is described by changing demand patterns following growth, maturity, and declining phases. The end of the sales period becomes a decision variable to obtain maximum profit, then converted as an order quantity. This order is made only once at the beginning of the period. This model uses non-linear programming, and the search for solutions uses a non-linear generalized reduced gradient. More accurate results by considering changes in demand patterns are essential to avoid shortages and uncontrolled inventory in the inventory system. Second, a single-item inventory policy with multi-relational demand patterns discusses the inventory policy in pharmaceutical industries. Long-term treatment or therapy may use drugs repeatedly. It also occurs for chemotherapy drugs. The treatment performed by the patient consists of several cycles so that the current demand for drugs is affected by the demand from previous patients. The material requirements planning (MRP) system is a method that can combine dependent and independent demand rates to accommodate previous and new patients' demand rates. The simulation model is used to see the advantages of the MRP system compared to the popular reorder point system. The results show that the MRP system outperforms the reorder point system. Considering the dependencies between the current and previous demands brings a significant improvement in the performance of the inventory policies. Third, the multi-item inventory policy with multiple independent demand patterns discusses a joint replenishment with a common ordering cost. These items may have different demand patterns and must be handled by an integrated inventory policy. A retail industry with multiple stock-keeping units is a good example of this subtopic. The order quantity for each item in one order is the decision variable to obtain the minimum total cost. This problem is modeled using non-linear programming and finding solutions using CPLEX. Simulation models are used to compare with and without the joint replenishment optimization model. The proposed procedure gives better total costs. These three studies show the advantages of the inventory model with multiple demand patterns in certain situations and are expected to complement the knowledge in the inventory model and provide insight for management and future studies.

並列關鍵字

inventory policy

參考文獻


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