English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62805/95882 (66%)
Visitors : 3940558      Online Users : 1074
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/33265


    Title: 保護期間內為隨機需求的週期性檢查存貨模型之研究
    Other Titles: A study of periodic review inventory models with stochastic demand during the protection interval
    Authors: 林裕仁;Lin, Yu-jen
    Contributors: 淡江大學管理科學研究所博士班
    歐陽良裕;Ouyang, Liang-yu
    Date: 2005
    Issue Date: 2010-01-11 03:48:50 (UTC+8)
    Abstract: 以往有關存貨問題的相關學術典籍或研究文獻幾乎注重在連續性檢查訂購策略的探討。然而,就存貨訂購策略的擬訂而言,較常為管理階層所引用的除了連續性檢查策略外,還有週期性檢查存貨訂購策略,但後者卻少有學者涉入鑽研。為了提供管理階層擬訂較為完整的存貨策略,本論文試圖對週期性檢查之存貨系統的最適訂購策略做深入探討。我們在可控制前置時間的存貨模型中,當存貨系統發生缺貨情形時,為降低銷售損失,考慮對缺貨數量採價格折扣,以吸引更多顧客願意等待欠撥;或是投資資金經由對員工的訓練、作業程序的改變,或採用新的設備佈置,以降低銷售損失率。

    本論文係在週期性檢查存貨系統的訂購策略下,提出保護期間內為隨機需求的存貨模型,在第二章建構可控制前置時間且缺貨發生時,缺貨數量含有欠撥價格折扣的存貨模型。第三章則討論當縮短前置時間可同時降低訂購成本時,缺貨數量含有欠撥價格折扣的存貨模型。在第四和第五章中,我們考慮投資資金以降低銷售損失率。第四章所建構的模型是以檢查週期長度,銷售損失率及前置時間為決策變數;而第五章則以檢查週期長度,銷售損失率及目標水準為決策變數。對各章所建立的存貨模型,我們均進一步討論兩種情況:一為保護期間內需求量的機率分配服從常態的情形,另一為保護期間內需求量的機率分配為未知,而僅已知其平均數與標準差的情形,並運用分配不拘大中取小準則來求得最適解。最後第六章為結論,對前述各章所得的結果作一總結,並提出未來的研究方向。
    In most of the early literature dealing with the inventory problems, the research conceptions are mostly concentrated on the continuous review ordering policy. However, viewing the domain of the periodic review inventory policies, it is found that existing literature discussing the problem is not substantial. In order to provide the decision-maker with some perfect managerial strategies of inventory systems, in this thesis, we attempt to investigate the periodic review inventory systems so as to look for their corresponding optimal ordering strategies. In the inventory systems with controllable lead time, when unsatisfied demands occur, in order to reduce lost sales, we consider the supplier could offer a backorder price discount, so that more customers may prefer their demands to be backorders. Or the supplier may invest more capital to reduce lost sales rate through efforts such as staff training, procedural changes, or specialized equipment acquisition.

    This thesis mainly focuses on the ordering strategies of periodic review inventory models. Under the policies, we propose the inventory models with stochastic demands during the protection interval. In Chapter 2, when unsatisfied demand occurs, we formulate the stockout quantity including backorder price discounts models with controllable lead time. And then, in Chapter 3, we discuss the stockout quantity including backorder price discounts, when the reduction of lead time may accompany the reduction of ordering cost. In Chapters 4 and 5, we consider investing more capital to reduce lost sales rate. In Chapter 4, we formulate the models, including decision variables of periodic review, lost sales rate, and lead time. And in Chapter 5, we formulate the models including decision variables of periodic review, lost sales rate, and target level. For each chapter, we discuss two cases in our formulate inventory models. The first is the case where the demand during protection interval follows a normal distribution. In the second case, where the distributional form of protection interval demand is unknown but merely the mean and standard deviation are known, we apply the minimax distribution free approach to solve the optimal solution. Finally, concluding remarks are made in Chapter 6, and future research directions are proposed.
    Appears in Collections:[管理科學學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat
    0KbUnknown270View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback