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研究生:顧宗杰
研究生(外文):TSUNG-CHIEH KU
論文名稱:生產標準產品與迴流加工客製化產品生產系統等候線模式之分析
論文名稱(外文):Queueing Model Analysis on a Single-Station Production SystemProducing Standard and Custom Products
指導教授:張國華張國華引用關係
學位類別:碩士
校院名稱:中原大學
系所名稱:工業工程研究所
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:2008
畢業學年度:96
語文別:英文
論文頁數:75
中文關鍵詞:Make-to-orderMake-to-stock基本庫存量庫存等候線系統等候線系統
外文關鍵詞:Make-to-orderMake-to-stockBase-stock policyInventory-queueQueueing system
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隨著全球性市場競爭越來越激烈,生產製造的環節在全球供應鏈中仍然扮演
相當重要的角色,不斷推陳出新的產品使的產品生命週期逐漸變短,過長的生產
時間可能使產品在市場中失去競爭力,因此生產系統必須要擁有好的生產力,才
能快速滿足顧客的需求,產品才會具有競爭力。最普遍用來評估生產系統的指標
為品質、產量與成本,多數的生產管理著重在於生產量與降低成本的問題,往往
忽略顧客需求,以大量生產的方式來降低成本。隨著顧客需求開始變的越來越難
以掌握,製造業開始面對到許多生產以外的問題,要如何滿足這些不確定性的顧
客需求變的相當重要。
本研究將中,我們探討一個單站生產系統,同時考慮客製化產品可由庫存區
中的標準產品稍做加工而成,該工作站同時負責生產標準產品亦同時負責客製化
產品所需之後續加工。完成的標準產品將會存放於標準產品庫存區中,而標準產
品的生產方式是以基本庫存量(Base-stock policy)來執行生產。為了要滿足更多的
一般需求,因此庫存區中的標準產品將優先供給到達系統的一般需求。在此系統
中,由於只有一個工作站,工作站在進行客製化加工具有較高的優先權於生產標
準產品。本研究將透過一般需求滿足率(Fill rate)與特殊需求回應時間
(On-Time-Delivery-Rate)來評估系統的服務品質,我們將討論基本庫存量對一般
需求滿足率與特殊需求回應時間之影響,進一步要求在達到某服務品質標準之
下,考慮其成本結構下之最佳庫存量。
Due to the intense competition in the global market, if manufacturing system
has great ability to produce efficiently and economically, it can provide services to
the customers with shorter response time, and the products will have higher competitiveness
in the global market. Therefore the appropriate management decisions for
production are getting more and more important.
In our study, we consider an single-station production system without the reservation
mechanism that produces standard products for ordinary demands and custom
products for specific demands. Custom products are made by alternating the existing
standard ones from inventory with additional works and base-stock control policy
is applied to control the production of the standard product. In this system, the
production of the custom products for specific demands has higher priority than the
production of the standard product. The fill rate of the ordinary demand and the
on-time-delivery-rate of the specific demand are considered as the measures of the
qualities of service. By assuming an Markovian system, qualities of service under
base-stock policy are derived; furthermore, the optimal base-stock level can be obtained
numerically under the requirements on the qualities of services.
Abstract
List of Tables iii
List of Figures vi
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Organizations of The Thesis . . . . . . . . . . . . . . . . . . . . . . . 5
2 Preliminary and Literature Review 6
2.1 Inventory-Queueing System . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Response Time in Queueing System . . . . . . . . . . . . . . . . . . . 7
2.3 Hybrid Production System . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 Matrix-Geometric Method . . . . . . . . . . . . . . . . . . . . . . . . 11
3 MTO/MTS Hybrid Production Systems 15
3.1 QueueingModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Quasi-Birth-Death Process . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3 Matrix-Geometric Method . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4 On-Time-Delivery-Rate . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4 Numerical Analysis 29
4.1 Model Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Optimization on S under cost structure . . . . . . . . . . . . . . . . . 33
5 Conclusion 37
Bibliography 38
Appendices 43
A Submatrices of the generator matrix Q 43
B Various response time of specific demand D in some given state 46
C Simulated results of performance indexes for 500 replications 51
D Comparison the numerical results with the simulated results on various
base-stock levels 55
E The numerical results of performance indexes on various base-stock
levels 64

List of Tables
3.1 Various response time of D in state (3,3) . . . . . . . . . . . . . . . . 22
3.2 Various response time of D in state (3,5) . . . . . . . . . . . . . . . . 23
3.3 Approximates of the OTDR on various base-stock levels with different
cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1 Comparison results on various base-stock levels for case of 1 = 0.2
and 2 = 0.15 (1 = 0.02, 2 = 0.01, μ1 = 0.1, μ2 = 0.2 and t = 2) . . 30
4.2 Comparison results on various base-stock levels for case of 1 = 0.2
and 2 = 0.5 (1 = 0.02, 2 = 0.03333, μ1 = 0.1, μ2 = 0.2 and t = 2) . 31
4.3 Comparison results on various base-stock levels for case of 1 = 0.8
and 2 = 0.85 (1 = 0.08, 2 = 0.03333, μ1 = 0.1, μ2 = 0.2 and t = 2) 31
4.4 Comparison results on various base-stock levels for case of 1 = 0.8
and 2 = 0.5 (1 = 0.08, 2 = 0.05666, μ1 = 0.1, μ2 = 0.2 and t = 2) . 31
4.5 Comparison results of OTDR with required lead time t = 0.2 on various
base-stock levels (1 = 5, 2 = 4, μ1 = 10 and μ2 =20) . . . . . . . . 32
4.6 Comparison results of OTDR with required lead time t = 0.2 on various
base-stock levels (1 = 8, 2 = 2, μ1 = 10 and μ2 =20) . . . . . . . . 33
4.7 Pf and e on various base-stock levels for case of 1 + 2 1 (1 = 5,
2 = 2, μ1 = 10 and μ2 =20) . . . . . . . . . . . . . . . . . . . . . . 34
4.8 Pf and e on various base-stock levels for case of 1 + 2 1 (1 = 9,
2 = 4, μ1 = 10 and μ2 =20) . . . . . . . . . . . . . . . . . . . . . . 34
4.9 Fill rates and OTDR on various base-stock levels (1 = 5, 2 = 2,
μ1 = 10 and μ2 =20) . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.10 TC’s on various base-stock levels (1 = 5, 2 = 2, μ1 = 10, μ2 = 20,
C1 = $20, C2 = $30, C3 = $0.7 and C4=$1) . . . . . . . . . . . . . . 36
B.1 Various response time of D in state (3,3) . . . . . . . . . . . . . . . . 47
B.2 Various response time of D in state (3,4) . . . . . . . . . . . . . . . . 47
B.3 Various response time of D in state (3,5) . . . . . . . . . . . . . . . . 48
B.4 Various response time of D in state (4,3) . . . . . . . . . . . . . . . . 48
B.5 Various response time of D in state (4,4) . . . . . . . . . . . . . . . . 49
B.6 Various response time of D in state (4,5) . . . . . . . . . . . . . . . . 50
C.1 Simulated Pf results for 500 replications . . . . . . . . . . . . . . . . 52
C.2 Simulated LF results for 500 replications . . . . . . . . . . . . . . . . 52
C.3 Simulated LPO results for 500 replications . . . . . . . . . . . . . . . 53
C.4 Simulated LSD results for 500 replications . . . . . . . . . . . . . . . 53
C.5 Simulated WSD results for 500 replications . . . . . . . . . . . . . . . 54
C.6 Simulated OTDR results for 500 replications . . . . . . . . . . . . . . 54
D.1 Comparison results on various base-stock levels for case of 1 = 0.2
and 2 = 0.15 (1 = 0.02, 2 = 0.01, μ1 = 0.1 and μ2 = 0.2) . . . . . . 56
D.2 Comparison results on various base-stock levels for case of 1 = 0.2
and 2 = 0.5 (1 = 0.02, 2 = 0.03333, μ1 = 0.1 and μ2 = 0.2) . . . . 57
D.3 Comparison results on various base-stock levels for case of 1 = 0.2
and 2 = 0.85 (1 = 0.02, 2 = 0.05666, μ1 = 0.1 and μ2 = 0.2) . . . . 58
D.4 Comparison results on various base-stock levels for case of 1 = 0.8
and 2 = 0.15 (1 = 0.08, 2 = 0.01, μ1 = 0.1 and μ2 = 0.2) . . . . . . 59
D.5 Comparison results on various base-stock levels for case of 1 = 0.8
and 2 = 0.5 (1 = 0.08, 2 = 0.03333, μ1 = 0.1 and μ2 = 0.2) . . . . 60
D.6 Comparison results on various base-stock levels for case of 1 = 0.8
and 2 = 0.85 (1 = 0.08, 2 = 0.05666, μ1 = 0.1 and μ2 = 0.2) . . . . 61
D.7 Comparison results of OTDR with required lead time t = 0.2 on various
base-stock levels (1 = 5, 2 = 4, μ1 = 10 and μ2 =20) . . . . . . . . 62
D.8 Comparison results of OTDR with required lead time t = 0.2 on various
base-stock levels (1 = 8, 2 = 2, μ1 = 10 and μ2 =20) . . . . . . . . 63
E.1 Pf and e on various base-stock levels for case of 1 + 2 1 (1 = 5,
2 = 2, μ1 = 10 and μ2 =20) . . . . . . . . . . . . . . . . . . . . . . 65
E.2 Pf and e on various base-stock levels for case of 1 + 2 1 (1 = 9,
2 = 4, μ1 = 10 and μ2 =20) . . . . . . . . . . . . . . . . . . . . . . 66
E.3 Fill rates and OTDR on various base-stock levels (1 = 5, 2 = 2,
μ1 = 10 and μ2 =20) . . . . . . . . . . . . . . . . . . . . . . . . . . . 67


List of Figures
1.1 The single-station hybrid production system . . . . . . . . . . . . . . 4
3.1 The MTO/MTS hybrid system . . . . . . . . . . . . . . . . . . . . . 16
3.2 The transition rates diagram . . . . . . . . . . . . . . . . . . . . . . . 17
3.3 Approximates of the OTDR on various base-stock levels with different
cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1 TCs on various base-stock levels(1 = 5, 2 = 2, μ1 = 10, μ2 = 20,
C1 = $20, C2 = $30 C3 = $0.7 and C4 =$1 ) . . . . . . . . . . . . . . 35
[1] A. Arreola-Risa and G. A. DeCroix, ”Make-to-order versus Make-to-stock in a
Production-Inventory System with General Production Times,” IIE Trans. Vol.
30, no. 8, pp. 705-713, 1998.
[2] Adan I. and Ven der Wal J., ”Combining Make to Order and Make to Stock,”
Operations Research Spektrum, Vol. 20, no. 2, pp. 73-81, 1998.
[3] Buzacott, J. A., Price, S.M., Shanthikumar, J. G., ”Service Level in Multistage
MRP and Base Stock Controlled Production Systems,” New Directions for Operations
Research in a Manufacturing Systems”, pp. 445-463, 1992.
[4] Bolch, G., Greiner, S., Meer, H. D., T, K. S., Queueing Networks and Markov
Chains Modeling and Performance Evaluation with Computer Science Applications,
Second Edition, A John Wiley and Sons, Inc., New Jersey, 200
[5] Carr, S. and Duenyas, I., ”Optimal Admission Control and Sequencing in a Make
to Stock/Make to Order Production System,” Operational Research, Vol. 48, no.
5, pp. 709-720, 2000.
[6] Duri, C., Frein, Y., Mascolo, M. D., ”Performance Evaluation and Design of
Base Stock Systems,” European Journal of Operational Research, No. 127, pp.
172-188, 2000.
[7] Deshpande V., Cohen M. A., and Donohue K., ”A Threshold Inventory Rationing
Policyfor Service Differentiated Demand Classes,” Management Science, Vol. 49,
No. 6, pp. 683-703, 2003.
[8] Ettl, M., G. E. Feigin, G. Y. Lin, D. D., ”A Supply Network Model with Base-
Stock Control and Service Requirements,” Operational Research, Vol. 48, pp.
216-232, 2000.
[9] Federguruen A. and Z. Katalan,”Impact of Adding a Make-to-zorder Item to
a Make-to-Stock Production System,” Management Science, Vol. 45, no. 7, pp.
980-994, 1999.
[10] Gupta, D., Selvaraju, N., ”Performance Evaluation and Stock Allocation in Capacitated
Serial Supply Systems,” Manufacturing and Service Operations Management,
Vol. 8, No. 2, pp. 169-191, 2006.
[11] Kuo-Hwa Chang, Yang-Shu Lu and Yen-Chen Shih,”Optimal Base-Stock Level of
a MTO/MTS Hybrid Production System ,” Europesn Conference on Operational
Research, Prague, 2007.
[12] Kuo-Hwa Chang, Yen-Chen Shih, Yi-Teng Shiu and Yang-Shu Lu, ”Performance
Analysis of a Two-Station Make-to-Order/Make-to-Stock Hybrid Production
System,” proceedings of the 2nd Asia-Pacific Symposium on Queueing Theory
and Network Application, Kobe, Japan, 211-218, 2007.
[13] Kuo-Hwa Chang and Yang-Shu Lu, ”An MTS/MTO Production System for
Standard Products and Custom Products,” working paper, Hualian, Taiwan,
2007.
[14] Kuo-Hwa Chang, Yi-Teng Shiu, Yi-Der Chiou and Yang-Shu Lu, ”Inventory-
Queue Systems Consisting MTS and MTO Production,” Proceedings of The 8th
Asia Pacific industrial Engineering and Management, Kaohsiung, Taiwan, 2007.
[15] Liu L., Liu X. and Yao D. D., ”Analysis and Optimization of a Multistage
Inventory-Queue System,” Management Science, 50(3). pp. 365-380, 2004.
[16] Lee, Y. J., Zipkin, P. H., Processing Networks with Inventories: Sequential Refinement
Systems,” Operations Research, No. 43, pp. 1025-1036, 1995.
[17] Nahmias S.and Demmy W. S., ”Operating Characteristics of an Invemtory System,”
Management Science, Vol. 27, pp. 1236-1245, 1981.
[18] Nguyen V., ”A Multiclass Hybrid Production Center in Heavy Traffic,” Operations
Research, Vol. 46, Suppl. 3, pp. S13-S25, 1998.
[19] Nelson, R., Probability, Stochastic Processes, and Queueing Theory: The Mathematics
of Computer Performance Modeling, Springer-Verlag, New York, 1995.
[20] Neuts, M. F., Matrix Geometric Solutions in Stochastic Model, An Algorithmic
Approach The John Hopkins University Press: Baltimore, 1981.
[21] Rajagopalan S., ”Make to Order or Make to Stock: Model and Application,”
Management Science, Vol. 48, no. 2, pp. 241-256, 2002.
[22] Svoronos, A., Zipkin, P., ”Evaluation of One-For-One Replenishment Policies for
Multi-Echelon Inventory Systems,” Management Science, Vol. 37, No. 1, 1991.
[23] Soman C., Donk D., and Gaalman G., ”Combined Make-to-Order and Maketo-
Stock in a Food Production System,” International Journal of Production
Economics vol. 90, pp. 223-235, 2004.
[24] Youssef, K. H., Van Delft, C., and Dallery, Y. ”Efficient Scheduling Rules in
a Combined Make-to-Stock and Make-to-Order Manufacturing System,” Annals
of Operations Research, 126(1-4), 103-134, 2004.
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