臺灣博碩士論文加值系統

隨著國防安全議題的日趨受到重視，軍方武器系統若於演練操作或實戰時期能夠有效運作將是非常重要，而負責維持系統設備正常運作的備份件則是其關鍵所在，若備份件存量過少，便無法適時對失效的系統進行維修、更換，因而耽誤生產作業；若備份件存量過多時，則會導致庫存成本過高，故如何提供一有效的備份件補給體係實是一重要的研究課題。

然而，過去備份件的相關文獻並無在多階補給架構與多階系統結構，以及有限維修能力之下，針對不符從卜瓦松失效分配行為的零件進行最佳化存貨配置的研究。故本研究提出一個較佳化求解模型，首先乃藉商用軟體OPUS10取得一起始解；再者利用eM-Plant模擬平台建構一具考量多種需求分配之能力的評估程式；最後結合邊際配置法進行較佳化作業，並發展4個方法針對邊際演算法進行修正，以強化求解效率。而在X系統的個案中，不僅說明零件失效行為並不完全符從卜瓦松分配模式，更在備份件存貨配置上證明確實存在高達10%的成本節省空間。

The spare-part stocking problem is very important for weapon system. Keeping a higher inventory level would increase weapon’s availability, yet at the price of incurring higher costs. Most related literature assumed that the failure of a spare part follows a Poisson distribution. This would reduce the problem complexity; yet may not well model a particular real world problem. This research aims to solve the spare-part stocking problem in a scenario where the failures of spare parts are not limited to Poisson distribution. The proposed method involves three major steps. First, we use OPUS10, proprietary software, to yield an initial solution. Second, we use simulation technique to evaluate the performance of a stocking solution. Third, we develop some heuristics to efficiently and effectively improve the solution until a satisfactory one is obtained. Experiment results show that the proposed method as compared to OPUS10 may save about 10% in inventory cost.

圖目錄 Ⅰ
表目錄 Ⅱ

第一章 緒論 01
1.1 研究動機 01
1.2 論文章節安排 03

第二章 文獻探討 04
2.1 需求限制卜瓦松 04
2.2 需求不限卜瓦松 05
2.3 文獻探討整理 06

第三章 研究問題與情境 08
3.1 補給架構屬性 08
3.2 系統零件屬性 08
3.3 失效維修程序 09

第四章 研究模型 11
4.1 模組1：尋找起始解 11
4.1.1 OPUS10 11
4.1.2決定起始解 13
4.2 模組2：評估解的績效 14
4.3 模組3：更新解 14

第五章 邊際演算化的修正 16
5.1 重要零件 16
5.2 減少站點考量 17
5.3 場站分群 18
5.2 動態刪除 20
5.3 解空間縮減小結 20

第六章 實例驗證一 22
6.1 案例一情境 22
6.1.1 補給體系架構 22
6.1.2 系統零件結構 23
6.1.3 真實需求分配 23
6.2 案例一求解過程 25
6.2.1 起始解 25
6.2.2 重要零件 27
6.2.3 刪除冗站與場站分群 27
6.2.4 動態刪除與邊際結果 28
6.2.5 案例一結果比較 29

第七章 實例驗證二 31
7.1 案例二情境 31
7.1.1 補給體系架構 31
7.2 案例二求解過程 32
7.2.1 起始解 32
7.2.2 重要零件 34
7.2.3 刪除冗站與場站分群 34
7.2.4 動態刪除與邊際結果 35
7.2.5 案例二結果比較 36

第八章 結論 39

參考文獻 40

附錄一 42

附錄二 43

附錄三 45

[1] Sherbrooke, C.C., “METRIC: Multi-Echelon Technique for Recoverable
ItemControl”, Operations Research 16, 122-141, 1968.

[2] Muckstadt, J, "A model for a multi-item, multi-indenture inventory system",
Management Science 20, 472-481, 1973.

[3] Slay, F.M., “VARY-METRIC: An Approach to Modeling Multi-Echelon Resupply when the Demand Process is Poisson with Gamma Prior”, Report AF301-3, LogisticManagement Institute, Washington, D.C, 1984.

[4] Graves, S., “A multi-echelon inventory model for a repairable item with
one-for-one replenishment, Management Science 31, 1247–1256, 1985.

[5] Sherbrooke, C.C., "VARI-METRIC: improved approximations for multi-indenture, multi-echelon availability models", Operations Research 34, 311-319, 1986.

[6] Gross, D., D.R. Miller and R.M. Soland, “A closed queueing network model for multi-echelon repairable item provisioning”, IIE Transactions 15(4), 344-352, 1983.

[7] Albright, S.C. and A. Soni, “Markovian multi-echelon repairable inventory
system”, Naval Research Logistics Quarterly 35, 49-61, 1988.

[8] Gupta, A. and S.C. Albright, “Steady-state approximations for multi-echelon multiindentured repairable-item inventory system”, European Journal of Operational Research 97(3), 340-353, 1992.

[9] Albright, S.C. and A. Gupta, “Steady-state approximation of a multi-echelon multiindentured repairable-item inventory system with a single repair facility”, Naval Research Logistics 40(4), 479-493, 1993.

[10] Diaz, A. and M.C. Fu, “Models for multi-echelon repairable item inventory
systems with limited repair capacity”, European Journal of Operational Research 97, 480-492, 1997.

[11] Aronis, K.P., Magou, I., Dekker, R., Tagaras, G., “Inventory Control of Spare Parts Using a Bayesian Approach: A Case Study,” European Journal of Operational Research 154, 730-739, 2004.

[12] Kabir, A.B.M.Z. and H.A. Farrash, “Simulation of an integrated age replacement and spare provisioning policy using SLAM”, Reliability Engineering and System Safety 52, 129-138, 1996.

[13] Sarker, R. and A. Haque, “Optimization of maintenance and spare provisioning policy using simulation”, Applied Mathematical Modelling 24, 751-760, 2000.

[14] Marseguerra, M., Zio, E., Podofillini, L., “Muliobjective spare part allocation by means of genetic algorithms an Monte Carlo simulation”, Reliability Engineering and System Safety 87, 325-335, 2005.

[15] Ahmed, M.A., Alkhamis, T.M., Hasan, M., “Optimizing discrete stochastic systems using simulated annealing an simulation”, Computers and Industrial Engineering 32, 823-836, 1997.

[16] Alkhamis, T.M. and M.A. Mohamed, “Simulation-base optimization for repairable systems using particle swarm algorithm”, 2005 Winter Simulation Conference, 2005.

[17] Sleptchenko, A., Integral Inventory Control in Spare Parts Networks with Capacity Restrictions, Twente University Press, 2002, the Netherlands.

[18] Sherbrooke, C.C., Optimal Inventory Modeling of Systems-Multi-Echelon Techniques, Kluwer Academic Publishers, 2004, 2nd ed..