ORIGINAL_ARTICLE
Door Allocations to Origins and Destinations at Less-than-Truckload Trucking Terminals
For an LTL (Less-than Truckload) carrier, the allocation of doors at a consolidation facility to outbound trailers assigned to various destinations, and to inbound trailers in the continuous stream arriving from various origins, has a significant impact on its operations, and on the nightly man-hours needed for consolidation. In the past literature door allocations to destinations of outbound trailers are determined using deterministic mathematical models based on average volumes of shipments between origin-destination pairs. The online nature of allocation of doors to inbound trailers is either ignored or simple rules like FCFS (First Come First Served) are assumed that do not take advantage of the data on the trailer's actual contents readily available at the time of its arrival. In reality the actual shipment volume between any origin-destination pair varies significantly from day to day. Due to this wide variation destination door allocations that are optimal for the average volume tend to be far from optimum for most nights. Also, very simple on-line policies for door allocation to each inbound truck at the time of its arrival based on its actual contents can significantly reduce the man-hours needed to consolidate its contents. In this paper we develop a new model that uses such an on-line policy for door allocations to inbound trailers, and determines doors to allocate to destinations to minimize the expected man-hours for consolidating freight nightly taking the random variation in freight volumes into account. Computational results on data from an actual facility indicate that the man-hour requirement can be reduced by over 20% compared to current practice.
http://www.jise.ir/article_3948_24fced7e8935833da864ec4e16587021.pdf
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1
15
cross-docking
Less-than-truckload freight terminal
Man-hours for consolidation
Vincent F.
Yu
true
1
Department of Industrial management, National Taiwan University of Science and Technology, Taiwan
Department of Industrial management, National Taiwan University of Science and Technology, Taiwan
Department of Industrial management, National Taiwan University of Science and Technology, Taiwan
AUTHOR
Dushyant
Sharma
true
2
Equity Trading Lab, Morgan Stanley, 1585 Broadway Ave, New York, NY 10036
Equity Trading Lab, Morgan Stanley, 1585 Broadway Ave, New York, NY 10036
Equity Trading Lab, Morgan Stanley, 1585 Broadway Ave, New York, NY 10036
AUTHOR
Katta G.
Murty
true
3
Department of Industrial and Operations Engineering, University of Michigan, USA
Department of Industrial and Operations Engineering, University of Michigan, USA
Department of Industrial and Operations Engineering, University of Michigan, USA
AUTHOR
[1] Ahuja R.K., Magnanti T.L., Orlin J.B. (1993), Network Flows: Theory, Algorithms, and Applications.
1
Prentice Hall.
2
[2] Ahuja R.K., Orlin J.B., Tiwari A. (2000), A greedy genetic algorithm for the quadratic assignment
3
problem; Computers and Operations Research 27; 917-934.
4
[3] Bartholdi J.J., Gue K.R. (2000), Reducing labor costs in an LTL crossdocking terminal; Operations
5
Research 48; 823-832.
6
[4] Bermudez R., Cole M.H. (2000), A genetic algorithm approach to LTL breakbulk terminal door
7
assignment; In Proceedings of the 2000 Industrial Engineering Research Conference; Cleveland, Ohio.
8
[5] Bureau of Transportation Statistics, USA (2003), National transportation statistics.
9
[6] Davis L. (1991), Handbook of Genetic Algorithms; Van Nostrand, New York.
10
[7] Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization, and Machine Learning; Addison
11
[8] Gue K.R. (1999), The effects of trailer scheduling on the layout of freight terminals; Transportation
12
Science 33; 419-428.
13
[9] Holland J.H. (1975), Adaptations in Natural and Artificial Systems; The University of Michigan Press,
14
Ann Arbor.
15
[10] Peck K.E. (1983), Operational Analysis of Freight Terminals Handling Less than Container Load
16
Shipments; Ph.D. thesis, University of Illinois at Urbana-Champaign.
17
[11] Savage S., Scholtes S., Zweidler D. (2006), Probability management; ORMS Today 33(1); 20-28.
18
[12] Tsui L.Y., Chang C.-H. (1990), A microcomputer-based decision support tool for assigning dock doors
19
in freight yards; Computers and Industrial Engineering 19; 309-312
20
ORIGINAL_ARTICLE
Determining Optimal Number of Suppliers in a Multiple Sourcing Model under Stochastic Lead Times
Employing more than one supplier and splitting orders between them is a strategy employed in supply chains to lessen the lead-time risk in unstable environments. In this paper we present a multiple-sourcing inventory system with stochastic lead-times and constant demand controlled by a continuous review, reorder point-order quantity inventory policy. We consider the situation in which the order quantity is equally split between a number of identical suppliers. The aims of this research are to determine the optimal number of suppliers and analyze the percentage savings obtained in a multiple-sourcing system compared to sole-sourcing. The objective function is to minimize the expected total cost per unit time by obtaining the number of suppliers, the reorder point and order quantity as decision variables. Extensive numerical examples are used to examine the effects of different parameters on the percentage savings and the optimal number of suppliers.
http://www.jise.ir/article_3949_20eaf78927377cf8c0b30a41dca8f86d.pdf
2008-04-01T11:23:20
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27
Multiple-sourcing
Lead-time risk pooling
Stochastic lead times
Mohammad Reza
Akbari Jokar
true
1
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
Mohsen
Sheikh Sajadieh
sajadieh@aut.ac.ir
true
2
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
[1] Chiang C. (2001), Order splitting under periodic review inventory system; International Journal of
1
Production Economics 70; 67–76.
2
[2] Chiang, C., Benton, W.C. (1994), Sole sourcing versus dual sourcing under stochastic demands and
3
lead times; Naval Research Logistics 41; 609–624.
4
[3] Dullaert W., Maes B., Vernimmen B., Witlox F. (2005), An evolutionary algorithm for order splitting
5
with multiple transport alternatives; Expert Systems with Applications 28; 201–208.
6
[4] Hill R.M. (1996), Order splitting in continuous review (Q,r) inventory models; European Journal of
7
Operational Research 95; 53–61.
8
[5] Hong J.D., Hayya J.C. (1992), Just-in-time purchasing: single or multiple sourcing?; International
9
Journal of Production Economics 27; 175–181.
10
[6] Lau H.S., Zhao L.G. (1993), Optimal ordering policies with two suppliers when lead times and
11
demands are all stochastic; European Journal of Operational Research 68 (1); 120–133
12
[7] Minner S. (2003), Multiple-supplier inventory models in supply chain management: a review;
13
International Journal of Production Economics 81–82; 265–279.
14
[8] Ramasesh R.V., Ord, J.K., Hayya J.C., Pan A. (1991), Sole versus dual sourcing in stochastic leadtimes,
15
(s,Q) inventory models; Management Science 37 (4); 428–443.
16
[9] Ramasesh R.V., Ord, J.K., Hayya, J.C. (1993), Note: dual sourcing with non-identical suppliers; Naval
17
Research Logistics 40; 279–288.
18
[10] Ryu S.W., Lee K.K. (2003), A stochastic inventory model of dual sourced supply chain with lead-time
19
reduction; International Journal of Production Economics 81–82; 513–524.
20
[11] Sculli D., Wu S.Y. (1981), Stock control with two suppliers and normal lead times; Journal of the
21
Operational Research Society 32 (11); 1003–1009.
22
[12] Sedarage D., Fujiwara O., Luong H.T. (1999), Determining optimal order splitting and reorder levels
23
for n-supplier inventory systems; European Journal of Operational Research 116; 389–404.
24
[13] Thomas D.J., Tyworth J.E. (2006), Pooling lead-time risk by order splitting: A critical review;
25
Transportation Research Part E 42; 245–257.
26
[14] Tyworth J.E., Ruiz-Torres A. (2000), Transportation’s role in the sole versus dual-sourcing decisions;
27
International Journal of Physical Distribution and Logistics Management 30 (2); 128–144
28
ORIGINAL_ARTICLE
Modeling of a Probabilistic Re-Entrant Line Bounded by Limited Operation Utilization Time
This paper presents an analytical model based on mean value analysis (MVA) technique for a probabilistic re-entrant line. The objective is to develop a solution method to determine the total cycle time of a Reflow Screening (RS) operation in a semiconductor assembly plant. The uniqueness of this operation is that it has to be borrowed from another department in order to perform the production screening task. Since the operation is being shared, there is a time limit to utilize it in a day. Screening of lots that cannot be completed within the given time has to be continued in the following days. The contributions of this paper is the development of a lot clustering method and factoring the limited time sharing condition and thus develop an analytical model. Comparison results were made using available real historical data. The proposed model provided operation managers with the total cycle time computation method and determining the appropriate cluster size to be loaded into the operation.
http://www.jise.ir/article_3950_9efda8d8263280d19fcc6482b58c1351.pdf
2008-04-01T11:23:20
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40
Probabilistic re-entrant line
Mean value analysis
Lot clustering
Total cycle time
Suresh
Kumar
true
1
Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Kingdom of
Saudi Arabia.
Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Kingdom of
Saudi Arabia.
Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Kingdom of
Saudi Arabia.
AUTHOR
[1] Gross D., Harris C.M. (1998), Networks, Series and Cyclic Queue. Fundamentals of Queueing Theory;
1
John Wiley, Sons, New York, 189-192.
2
[2] Halachimi I, Adan I.J.B.F., Wal J.V.D., Heesterbeek J.A.P., Beek P.V.(2000), The design of robotic
3
diary barns using closed queueing networks; European Journal of Operational Research 124; 437-446.
4
[3] Kumar S. (2007), Performance analysis of a probabilistic re-entrant line in an environmental stress
5
testing operation; Doctoral Thesis; Multimedia University.
6
[4] Kumar S., Omar M.K. (2005a), Stochastic re-entrant line modeling for an environmental stress testing
7
in a semiconductor assembly industry; Applied Mathematics and Computation 173(1); 603-615.
8
[5] Kumar S., Omar M.K. (2005b), Performance measure in a probabilistic reflow screening line using
9
mean value analysis; The AIUB Journal of Science and Engineering 4(1); 53-58.
10
[6] Little J.D.C. (1961), A proof for the queueing formula: L=λW; Operations Research 9; 383-387.
11
[7] Muduli P.K., Yegulalp T.M. (1996), Modeling truck-shovel systems as multiple-chain closed queueing
12
networks; International Transactions in Operations Research 3(1); 89-98.
13
[8] Narahari Y., Khan L.M. (1995), Performance analysis of scheduling policies re-entrant manufacturing
14
systems; Computers & Operations Research 23; 37-51.
15
[9] Narahari Y., Khan L.M. (1996), Modeling re-entrant manufacturing systems with inspections; Journal
16
of Manufacturing Systems 15; 367-378.
17
[10] Narahari Y., Khan L.M. (1998), Asymptotic loss priority scheduling policies in closed re-entrant lines:
18
A computational study; European Journal of Operational Research 110; 585-596.
19
[11] Park Y., Kim S., Jun C.H. (2002), Mean value analysis of re-entrant line with batch machines and
20
multiclass jobs; Computers & Operations Research 29; 1009-1024.
21
[12] Park Y., Kim S., Jun C.H. (2006), Performance evaluation of re-entrant manufacturing system with
22
production loss using mean value analysis; Computers & Operations Research 33; 1308-1325.
23
[13] Reiser M., Lavenberg S.S (1980), Mean-value analysis of closed multichain queueing networks;
24
Journal of the Association for Computing Machinery 27(2); 313-322.
25
ORIGINAL_ARTICLE
Deriving the Exact Cost Function for a Two-Level Inventory System with Information Sharing
In this paper we consider a two-level inventory system with one warehouse and one retailer with information exchange. Transportation times are constant and retailer faces independent Poisson demand. The retailer applies continuous review (R,Q)-policy. The supplier starts with m initial batches (of size Q), and places an order to an outside source immediately after the retailer’s inventory position reaches R+s. In this system the lead time of the retailer is determined not only by the constant transportation time but also by the random delay incurred due to the availability of stock at the supplier. A recent paper has obtained the approximate value of the expected cost for this system by using the expected value of the retailer’s lead time and hence has pointed out that the optimal supplier policy is an open question. In this paper we tackle this open question and obtain the exact value of the expected system cost by using the idea of the one-for-one ordering policy and implicitly incorporating the distribution function of the random delay.
http://www.jise.ir/article_3951_1afa6b1b3d1014c48f0bc482cbb10110.pdf
2008-04-01T11:23:20
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50
Multi-echelon inventory
information sharing
Continuous review
Poisson demand
R.
haji
true
1
Industrial Engineering Dept, Sharif University of Technology, Tehran, Iran
Industrial Engineering Dept, Sharif University of Technology, Tehran, Iran
Industrial Engineering Dept, Sharif University of Technology, Tehran, Iran
AUTHOR
S.M.
Sajadifar
true
2
Industrial Engineering Dept, Sharif University of Technology, Tehran, Iran
Industrial Engineering Dept, Sharif University of Technology, Tehran, Iran
Industrial Engineering Dept, Sharif University of Technology, Tehran, Iran
AUTHOR
[1] Axsäter S. (1990a),. Simple solution procedures for a class of two-echelon inventory problem;
1
Operations Research 38 (1); 64-69.
2
[2] Axsäter S. (1990b), Evaluation of (R,Q)-policies for two-level inventory systems with Poisson demand.
3
Lulea University of Technology, Sweden.
4
[3] Axsäter S. (1993), Exact and approximate evaluation of batch-ordering policies for two-level
5
inventory systems; Operations Research 41 (4); 777-785.
6
[4] Axsäter S., Forsberg R., Zhang W. (1994), Approximating general multi-echelon inventory systems by
7
Poisson models; International Journal Production Economics 35; 201-206.
8
[5] Cachon G.P., Fisher M. (2000), Supply chain inventory management and the value of shared
9
information; Management Science 46; 1032-1048.
10
[6] Cheung K.L., Lee H.L. (1998), Coordinated replenishments in a supply chain with Vendor-Managed
11
Inventory programs; Wouking Paper.
12
[7] Deuermeyer B., Schwarz L.B. (1981), A model for the analysis of system seviece level in
13
warehouse/retailer distribution systems: The Identical Retailer Case. in: L. B. Schwarz (ed.), Studies in
14
Management Science , 16: Multi-level Production/Inventory Control Systems, 163-193.
15
[8] Forsberg R. (1995), Optimization of order-up-to-S policies for two-level inventory systems with
16
compound Poisson demand; European Journal of Operational Research 81; 143-153.
17
[9] Forsberg R. (1996), Exact evaluation of (R,Q)-policies for two-level inventory systems with Poisson
18
demand; European Journal of Operational Research 96; 130-138.
19
[10] Gavirneni S. (2002), Information flows in capacitated supply chains with fixed ordering costs;
20
Management Science 48(5); 644-651.
21
[11] Graves S.C. (1985), A multi-echelon onventory model for a repairable item with one-for-one
22
replenishment; Management Science 31; 1247-1256.
23
[12] Hadley G., Whitin T. M. (1963). Analysis of inventory systems. Prentice-Hall, Englewood Cliffs, NJ.
24
[13] Hsiao J.M., Shieh C.J. (2006), Evaluating the value of information sharing in a supply chain using an
25
ARIMA model; International Journal of Advanced Manufacturing Technology 27; 604-609.
26
[14] Lee H.L., Moinzadeh K. (1987a), Two-parameter approximations for multi-echelon repairable
27
inventory models with batch ordering policy; IIE Transactions 19; 140-149.
28
[15] Lee H.L., Moinzadeh K. (1987b), Operating characteristics of a two-echelon inventory system for
29
repairable and consumable items under batch ordering and shipment policy; Naval Research Logistics
30
Quarterly 34; 356-380.
31
[16] Lee H.L., Whang S. (1998), Information sharing in a supply chain. Working Paper.
32
[17] Milgrom P., Roberts J. (1990), The economics of modern manufacturing technology, strategy and
33
organization; American Economic Review 80; 511-528.
34
[18] Moinzadeh K. (2002), A multi-echelon inventory system with information exchange; Management
35
Science 48(3); 414-426.
36
[19] Moinzadeh K., Lee H.L. (1986), Batch size and stocking levels in multi-echelon repairable systems;
37
Management Science 32; 1567-1581.
38
[20] Seifbarghi M., Akbari M.R. (2006), Cost evaluation of a two-echelon inventory system with lost sales
39
and approximately Poisson demand; International Journal of Production Economics 102; 244-254.
40
[21] Sherbrooke C.C. (1968), METRIC: A multi-echelon technique for recoverable item control;
41
Operations Research 16; 122-141.
42
[22] Svoronos A., Zipkin P. (1988), Estimationg the performance of multi-level inventory systems;
43
Operations Research 36; 57-72.
44
ORIGINAL_ARTICLE
An EFQM Based Model to Assess an Enterprise Readiness for ERP Implementation
In today's competitive market, Enterprise Resource Planning (ERP) system is widely being used by industries. However, the results of the research efforts carried out in this field reveal that the rate of successful implementations for ERP projects is low and in most cases the planned goals are not achieved. Therefore it is necessary to assess maturity of an enterprise in terms of factors affecting a successful implementation of an ERP system. This paper proposes an EFQM based model to assess the readiness of an enterprise for effective and successful ERP implementation. First, the main factors affecting the implementation of an ERP system, called Critical Success Factors (CSF) are identified. Then relations between the factors defined in EFQM model and ERP CSFs are investigated by means of questionnaires by experts working in this field. The results identify those EFQM factors which are related to ERP CSFs. In addition, those ERP specific factors which are not considered in the EFQM model are identified. Consequently a model based on EFQM including ERP specific CSFs is developed. The proposed model is applied to assess the readiness of a company intending to implement an ERP system. Finally the results of the assessment are discussed and concluding remarks are presented.
http://www.jise.ir/article_3952_0f223eb3c22a4ac67f5bc85056c315b6.pdf
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74
Enterprise resource planning
Readiness assessment
Critical success factors
EFQM
Rasoul
Shafaei
true
1
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
AUTHOR
Nooraddin
Dabiri
true
2
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N.Toosi University of Technology, Tehran, Iran
AUTHOR
[1] Abdinnour-Helm S., Lengnick-Hall M.L., Lengnick-Hall C.A. (2003), Pre-implementation attitudes
1
and organizational readiness for implementing an enterprise resource planning system; European
2
Journal of Operational Research 146 (2); 258–273.
3
[2] Akkermans H., Helen K.V. (2002), Vicious and virtuous cycles in ERP implementation: a case study
4
of interrelations between critical success factors; European Journal of Information Systems 11; 35–46.
5
[3] Al-Mashari M., Al-Mudimigh A., Zairi M. (2003), Enterprise resource planning: A taxonomy of
6
critical factors; European Journal of Operational Research 146; 352–364.
7
[4] Baatz E. (1996), Ready or Not; CIO Magazine; June 15.
8
[5] Bancroft N.H., Seip H., Sprengel A. (1998), Implementing SAP R/3., second ed; Manning Publications
9
Co., Greenwich, MA.
10
[6] Banijamali S.M., Jafarnejad A., Haghparast M. (2005), A framework to assesses the Iranian
11
organizations readiness for ERP implementation; 3rd International Management Conference; Tehran,
12
[7] Bingi P., Sharma M., Godla J. (1999), Critical issues affecting an ERP implementation; Information
13
Systems Management; 7–14.
14
[8] Botta V.G., Millet P.A., Grabot B. (2005a), A survey on the recent research literature on ERP systems;
15
Computers in Industry 56; 510–522.
16
[9] Botta V.G., Millet P.A. (2005b), An investigation into the use of ERP systems in the service sector;
17
International Journal of Production Economics; Available on: www.sciencedirect.com.
18
[10] Botta V.G., Millet P.A. (2005c), A classification for better use of ERP systems; Computers in Industry
19
56; 573–587.
20
[11] Brown J. (2001), Is ERP a silver Bullet? APICS Online Available
21
http://www.apics.org/magazine/past_issues/2001_01/erp_silver/full/asp.
22
[12] Buchout S., Frey E., Nemec J. (1999), Making ERP Succeed: Turning Fear Into Promise, Strategy and
23
Business, 2nd Quarter, [Online] Available: http://www.strategy-business.com/technology/99208/.
24
[13] Chand D., et. al. (2005), A balanced scorecard based framework for assessing the strategic impacts of
25
ERP systems; Computers in Industry 56; 558-572.
26
[14] Dabiri N. (2007), The investigation of enterprise readiness for ERP system implementation; MSc
27
Thesis, Faculty of Industrial Engineering, K.N. Toosi University of Technology; Iran.
28
[15] Davenport T. (1998), Putting the Enterprise into the Enterprise System; Harvard Business Review,
29
Jul–Aug; 121–131.
30
[16] Davenport T. (2000), Mission critical: realizing the promise of enterprise systems; Harvard Business
31
School Press, Boston.
32
[17] EFQM web page. (2007), www.efqm.org.
33
[18] Ehie I.C., Madsen M. (2005), Identifying critical issues in enterprise resource planning (ERP)
34
implementation; Computers in Industry 56; 545–557.
35
[19] Gefen D. (2002), Nurturing clients’ trust to encourage engagement success during the customization of
36
ERP systems; Omega 30; 287–299.
37
[20] Gyampah K.A., Salam A.F. (2004), An extension of the technology acceptance model in an ERP
38
implementation environment; Information & Management 41; 731–745.
39
[21] Holland C., Light B. (1999), A critical success factors model for ERP implementation; IEEE Software
40
(May/June); 30–35.
41
[22] Hong K.K., Kim Y.G. (2002), The critical success factors for ERP implementation: an organizational
42
fit perspective; Information & Management 40; 25–40.
43
[23] Hutchins H. (1998), 7 key elements of a successful implementation and 8 mistakes you will make
44
anyway, APICS, 1998. International Conference Proceedings, Falls Church, VA; 356–358.
45
[24] Jones M.C., Cline M., Ryan S. (2004), Exploring knowledge sharing in ERP implementation: an
46
organizational culture framework, Decision Support Systems, Available on: www.sciencedirect.com.
47
[25] Joshi K., Lauer T.W. (1999), Transition and change during the implementation of computer based
48
manufacturing process planning system: an analysis using the equity implementation model; IEEE
49
Transactions on Engineering Management 46; 156–167.
50
[26] Kale V. (2006), Implementing SAP R/3: The Guide for Business and Technology Managers, Online
51
available:
52
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53
20Prest%20Chpt%201.ppt.
54
[27] King S.F., Burgess T.F. (2006), Beyond critical success factors: A dynamic model of enterprise system
55
innovation; International Journal of Information Management 26; 59–69.
56
[28] Kositanurit B., Ngwenyama O., Osei-Bryson K.M. (2006), An exploration of factors that impact
57
individual performance in an ERP environment: an analysis using multiple analytical techniques;
58
European Journal of Information Systems 15; 556–568.
59
[29] KPMG, 2002. Annual program management survey (2002), Report 203-587, UK: KPMG-LLP.
60
[30] Kumar V., Maheshwari B., Kumar U. (2002), ERP systems implementation: Best practices in
61
Canadian government organizations; Government Information Quarterly 19; 147–172.
62
[31] Kwon T.H., Zmud W.R. (1987), Unifying the fragmented models of information system
63
implementation; Critical issues in information system research, Wiley, New York.
64
[32] Lam W. (2005), Investigating success factors in enterprise application integration: a case-driven
65
analysis; European Journal of Information Systems 14; 175–187.
66
[33] Langenwalter G. (2000), Enterprise Resources Planning and Beyond: Integrating Your Entire
67
Organization, St. Lucie Press, Boca Raton, FL.
68
[34] Lea B-R., Mahesh C.G., Wen-Bin Y. (2005), A prototype multi-agent ERP system: an integrated
69
architecture and a conceptual framework; Technovation 25; 433–441.
70
[35] Liang H., Xue Y. (2004), Coping with ERP-related contextual issues in SMEs: a vendor’s perspective;
71
Journal of Strategic Information Systems 13; 399–415.
72
[36] Mabert V.A., Soni A., Venkataramanan M. (2001), Enterprise resource planning survey of US
73
manufacturing firms; Business Horizon May-June; 69–76.
74
[37] Mabert V.A., Soni A., Venkataramanan M. (2003), Enterprise resource planning: Managing the
75
implementation process; European Journal of Operational Research 146(2); 302–314.
76
[38] Mandal P., Gunasekaran A. (2002), Issues in implementing ERP: A case study; European Journal of
77
Operational Research 146; 274–283.
78
[39] Motwani J., Mirchandani D., Madan M., Gunasekaran A. (2002), Successful implementation of ERP
79
projects: Evidence from two case studies; International Journal of Production Economics 75; 83–96.
80
[40] Motwani J., Subramanian R., Gopalakrishna P. (2005), Critical factors for successful ERP
81
implementation: Exploratory findings from four case studies; Computers in Industry 56; 529–544.
82
[41] Nandhakumar J., Rossi M., Talvinen J. (2005), The dynamics of contextual forces of ERP
83
implementation; Journal of Strategic Information Systems 14, 221–242.
84
[42] Nash K.S. (2000), Companies don’t learn from previous IT snafus, Computer-World, 30.
85
[43] Ng J.K.C., Ip W.H., Lee T.C. (1999), A paradigm for ERP and BPR integration; International Journal
86
of Production Research 37 (9); 108-209.
87
[44] Nikolaou A.I. (2004), Quality of post-implementation review for enterprise resource planning systems;
88
International Journal of Account Information System (5), 25 – 49.
89
[45] Olhager J., Selldin E. (2003), Enterprise resource planning survey of Swedish manufacturing firms;
90
European Journal of Operational Research 146; 365–373.
91
[46] Oliver R. (1999), ERP is dead! Long live ERP!; Management Review 88(10); 12–13.
92
[47] Olson d. l. (2004), Managerial issues of Enterprise Resource Planning Systems; International ed,
93
McGraw Hill/Irwin, New York, NY.
94
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ORIGINAL_ARTICLE
Some Modifications to Calculate Regression Coefficients in Multiple Linear Regression
In a multiple linear regression model, there are instances where one has to update the regression parameters. In such models as new data become available, by adding one row to the design matrix, the least-squares estimates for the parameters must be updated to reflect the impact of the new data. We will modify two existing methods of calculating regression coefficients in multiple linear regression to make the computations more efficient. By resorting to an initial solution, we first employ the Sherman-Morrison formula to update the inverse of the transpose of the design matrix multiplied by the design matrix. We then modify the calculation of the product of the transpose of design matrix and the design matrix by the Cholesky decomposition method to solve the system. Finally, we compare these two modifications by several appropriate examples.
http://www.jise.ir/article_3953_4d220fa82390cf449a34d3bd61f83762.pdf
2008-04-01T11:23:20
2020-01-22T11:23:20
75
86
Regression
Inverse matrix
Cholesky decomposition
Sherman-Morrison -
Woodbury formula
S.M.
Sajadifar
true
1
Department of Industrial Engineering, University of Science and Culture, P. O. Box: 13145-871, Tehran
Department of Industrial Engineering, University of Science and Culture, P. O. Box: 13145-871, Tehran
Department of Industrial Engineering, University of Science and Culture, P. O. Box: 13145-871, Tehran
AUTHOR
M.
Allameh
true
2
Department of Mathematics, Azad University, P. O. Box: 81595-158, Khorasgan, Isfahan, Iran
Department of Mathematics, Azad University, P. O. Box: 81595-158, Khorasgan, Isfahan, Iran
Department of Mathematics, Azad University, P. O. Box: 81595-158, Khorasgan, Isfahan, Iran
AUTHOR
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