ORIGINAL_ARTICLE
Efficiency Analysis of Public Universities in Iran Using DEA Approach: Importance of Stakeholder’s Perspective
Our primary aim, in this paper, is to propose and investigate, for the first time, the application of Data Envelopment Analysis (DEA) technique in assessing Iranian public universities. We provide an analysis on the importance of the stakeholder’s perspective on the structure of DEA and the variations of efficiency results. For illustrations, we perform efficiency analysis on a sample of public universities in Iran from three different perspectives of importance, i.e., teaching quality, research productivity, and cost efficiency by using available data.
http://www.jise.ir/article_4050_eeedcf89ac4e6c65b48bfbc8197548b7.pdf
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197
Data Envelopment Analysis
Efficiency
Academic performance
M.A.
S.Monfared
true
1
School of Engineering,Alzahra University,Tehran, Iran
School of Engineering,Alzahra University,Tehran, Iran
School of Engineering,Alzahra University,Tehran, Iran
AUTHOR
Mahsa
Safi
true
2
School of Engineering,Alzahra University,Tehran, Iran
School of Engineering,Alzahra University,Tehran, Iran
School of Engineering,Alzahra University,Tehran, Iran
AUTHOR
[1] Abbott M., Doucouliagos C., (2003),The efficiency of Australian Universities: A Data Envelopment
1
Analysis;Economics of Education review 22;89-97.
2
[2] Ahn T., ArnoldV., CharnesA., CooperW. W. (1989), DEA and ratio efficiency analyses forpublic
3
institutions of higher learning in Texas;Research in Governmental andNonprofit Accounting 5; 165–
4
[3] Ahn T., Charnes A., Cooper W.W. (1988),Some statistical and DEA evaluations of relative
5
efficiencies of public and private institutions of higher learning;Socio-Economic Planning Sciences22;
6
259–69.
7
[4] Avkiran NK. (2001), Investigating technical and scale efficiencies of Australian universities through
8
data envelopmentanalysis; Socio-Economic Planning Sciences35; 57–80.
9
[5] Beasley J.E. (1995),Determining teaching and research efficiencies; Journal of the Operational
10
Research Society46(4); 441–52.
11
[6] Celik O., AlaattinEcer. (2009), Efficiency in accounting education: evidence from Turkish
12
Universities; Critical Perspectives on Accounting20;614–634.
13
[7] Charnes A, Cooper W. W., Rhodes E. (1979), Short Communication: Measuring Efficiency of
14
Decision Making Units; European Journal of Operational Research 3; 339.
15
[8] Cook W.D., Liang L., Zhu J.(2009), Measuring Performance of Two-Stage Network Structures by
16
DEA: A Review and Future Perspective; Omega doi:10.1016/j.omega2009.12.001.
17
[9] Cook W.D., SeifordL.M. (2009), Data Envelopment Analysis (DEA)-Thirty Years on; EJOR 192; 1-
18
[10] Cooper W.W.,SeifordL. M., Karou Tone (2006), Introduction to Data Envelopment Analysis and its
19
uses with DEA-Solver Software and References;Springer; New York.
20
[11] Dyson.R.G.,AllenR.,CamanhoA.S, PondinovskiV.V.,SaricoC.S.,ShaleE.A.(2001), Pitfall and protocols
21
in DEA; European Journal of Operational Research 132; 245-259.
22
[12] Giannoulis, C., Alessio Ishizaka.(2010), A Web-based decision support system with ELECTRE III for
23
a personalized ranking of British universities; Decision Support Systems48; 488–497.
24
[13] Glass, J. Colin, Gillian McCallion, Donal G. McKillop, SyamarlahRasaratnam, Karl S. Stringer.
25
(2006), Implications of variant efficiency measures for policy evaluations in UK higher education;
26
Socio-Economic Planning Sciences 40;119–142.
27
[14] Glass J.C., McKillop DG, O’Rourke G. (1998), A cost indirect evaluation of productivity change in
28
UK universities; Journal of Productivity Analysis10; 153–75.
29
[15] Johnes G.(1996), Multiproduct cost functions and the funding of tuition in UK universities; Applied
30
Economics Letters 3; 557–61.
31
[16] Johnes J.(2006), Data envelopment analysis and its application to the measurement ofefficiency in
32
higher education; Economics of Education Review 25 (3); 273–288.
33
[17] Johnes J., Johnes G. (1995), Research funding and performance in U.K. university departments of
34
economics: a frontier analysis; Economics of Education Review 14; 301–14.
35
[18] Johnes J., YuL. (2008), Measuring the research performance of Chinese higher educationinstitutions
36
using data envelopment analysis; China Economic Review 19 (4); 679–696.
37
[19] Kao H. (2008), Hsi-Tai Hung, Efficiency analysis of university departments: An empirical study;
38
Omega 36; 653 – 664.
39
[20] Katharaki and Katharakis (2010), A comparative assessment of Greek universities’ efficiency using
40
quantitative analysis; International Journal of Educational Research 49; 115–128.
41
[21] Kong.W.,Fu.T. (2012), Assessing the performance of business colleges in Taiwan using data
42
envelopment analysis and student based value-added performance indicators; Omega 40; 541–549.
43
[22] Liu. J. Lu.L,Lu.W,Lin.B. (2012), A Survey of DEA Applications; Omega; Accepted Manuscript.
44
[23] LukmanR,Krajnc D. G. (2010), University ranking using research, educational and environmental
45
indicators, Journal of Cleaner Production 18; 619–628.
46
[24] Mehregan M.R. (2009), Quantitative Models for Organizational Performance Evaluation, University of
47
Tehran Publishing House, (in Farsi).
48
[25] Meng W., Zhang D.,Qi L., Liu W. (2008), Two-level DEA Approaches in research Evaluation;
49
Omega; 950-957.
50
[26] Monfared M.A.S., Gharneh N.S.,MirkhaniS.N. (2006), Ranking Analysis and Modeling of State Run
51
Universities; ScientiaIranica13(1); 91-104.
52
[27] Monfared M.A.S.,Mahdavi F. (2004), Measurement of College Quality Using 3 Different MCDM
53
Methods, Proceeding of 3rd International Conference on Industrial Engineering, Amirkabir University
54
of Technology; 63-83, (in Farsi).
55
[28] Muñiz M.A. (2002), Separating managerial inefficiency and external conditions in data envelopment
56
analysis; European Journal of Operational Research; 143:625–43.
57
[29] Sarrico C. S., Dyson R. G. (2003), Restricting virtual weights in data envelopment analysis; European
58
Journal of Operational Research159(1)1; 17–34.
59
[30] Seiford L.M. (1996), Data envelopment analysis: the evolution of the state of the art (1978–1995);
60
Journal of Productivity Analysis;7:99–137.
61
[31] Sinuany-Stern Z. (1994), Mehrez A., Barboy A., Academic departments efficiency via DEA;
62
Computers and Operations Research;21:543–56.
63
[32] Tam M. (2001), Measuring Quality and Performance in Higher Education; Quality in Higher
64
Education7(1).
65
[33] The league table of UK universities, The Complete University Guide,
66
http://www.thecompleteuniversityguide.co.uk/single.htm?ipg=6310#HowtheLeagueTableworks, 2009.
67
ORIGINAL_ARTICLE
A Multi-Stage Single-Machine Replacement Strategy Using Stochastic Dynamic Programming
In this paper, the single machine replacement problem is being modeled into the frameworks of stochastic dynamic programming and control threshold policy, where some properties of the optimal values of the control thresholds are derived. Using these properties and by minimizing a cost function, the optimal values of two control thresholds for the time between productions of two successive nonconforming products is determined. If this time exceeds the first threshold, the production continues. If it is less than the second one, inspection, repair, or replacement occur. However, if it falls within the control thresholds, then the process of sampling continues. At the end, the application of the proposed methodology is demonstrated using a numerical illustration.
http://www.jise.ir/article_4051_a3d15550157ca3ddd1fcdc8e2826c517.pdf
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207
Machine Replacement Policy
Control Threshold Policy
Exponential Distribution
Stochastic dynamic programming
Mohammad Saber
Fallah Nezhad
true
1
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
AUTHOR
Seyed Taghi
Akhavan Niaki
niaki@sharif.edu
true
2
Industrial Engineering Department, Sharif university of Technology, P.O. Box 11155-9414, Tehran, Iran
Industrial Engineering Department, Sharif university of Technology, P.O. Box 11155-9414, Tehran, Iran
Industrial Engineering Department, Sharif university of Technology, P.O. Box 11155-9414, Tehran, Iran
AUTHOR
Ahmad
Sadegheih
true
3
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
Industrial Engineering Department, Yazd University, Yazd, Iran
AUTHOR
[1] Fallahnezhad M.S., Niaki S.T.A., Eshragh-Jahromi A. (2007), A one-stage two-machines replacement
1
strategy based on the Bayesian inference method; Journal of Industrial and Systems Engineering 1; 235-
2
[2] Fallahnezhad M.S., Niaki S.T.A. (2010), A multi-stage two-machines replacement strategy using mixture
3
models, Bayesian inference and stochastic dynamic programming; Communications in Statistics-Theory
4
and Methods 40; 702-725.
5
[3] Fallahnezhad M.S., Niaki S.T.A. (2011), A new machine replacement policy based on number of
6
defective items and Markov chains; Iranian Journal of Operations Research 2; 17-28.
7
[4] Grosfeld-Nir A. (2007), Control limits for two-state partially observable Markov decision processes;
8
European Journal of Operational Research 182; 300–304.
9
[5] Iravani S., Duenyas I. (2002), Integrated Maintenance and Production Control of a Deteriorating
10
Production System; IIE Transactions 34; 423-435.
11
[6] Niaki S.T.A., Fallahnezhad M.S. (2007), A decision making framework in production processes using
12
Bayesian inference and stochastic dynamic programming; Journal of Applied Science 7; 3618-3627
13
[7] Singh, M., Song J.-S., Yano C., Moreno-Beltran A. (2004), Production and repair decisions with timeconsuming repair and a deadline; Working Paper.
14
ORIGINAL_ARTICLE
Model and Solution Approach for Multi objective-multi commodity Capacitated Arc Routing Problem with Fuzzy Demand
The capacitated arc routing problem (CARP) is one of the most important routing problems with many applications in real world situations. In some real applications such as urban waste collection and etc., decision makers have to consider more than one objective and investigate the problem under uncertain situations where required edges have demand for more than one type of commodity. So, in this research, a new fuzzy chance constrained programming model based on credibility measure for CARP with two objectives: minimizing the number of vehicle and minimizing the total travel cost is formulated. In this model each required edge has demand for more than one type of commodity and also all demands for each commodity are supposed to be triangular fuzzy numbers. Then we develop a multi-objective genetic algorithm using the Pareto ranking technique and hybrid it with stochastic simulation to design an intelligent algorithm to solve the fuzzy chance constrained model. In order to improve the quality of final solutions, we also propose a new heuristic method to generate a good initial solution in initial population of genetic algorithm. Some data sets with fuzzy demand generated randomly are used to evaluate and investigate key characteristics of the new proposed model and solution approach.
http://www.jise.ir/article_4052_082cd7de52e7eadf2033de3712f33389.pdf
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229
Multi objective capacitated arc routing problem
Fuzzy chance constrained
programming
Stochastic simulation
Genetic algorithm
Pareto ranking
heuristic
Alireza
Eydi
true
1
Department of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Engineering, University of Kurdistan, Sanandaj, Iran
Department of Engineering, University of Kurdistan, Sanandaj, Iran
AUTHOR
Leila
Javazi
true
2
University of Kurdistan, Sanandaj, Iran
University of Kurdistan, Sanandaj, Iran
University of Kurdistan, Sanandaj, Iran
AUTHOR
[1] Beullens P., Muyldermans L., CattrysseD.,VanOudheusden D. (2003), A guided local search heuristic
1
for the capacitated arc routing problem; European Journal of Operational Research 147(3); 629–643.
2
[2] Christiansen C.H., Lysgaard J., Wøhlk S. (2009), A Branch-and-Price Algorithm for the Capacitated
3
Arc Routing Problem with Stochastic Demands; Operations Research Letters 37; 392-398.
4
[3] Coello C.A.C. (1999), A Comprehensive Survey of Evolutionary-Based Multiobjective Optimization
5
Techniques; Knowledge and Information Systems 1(3); 269–308.
6
[4] DeArmon J.S. (1981),A comparison of heuristics for the capacitated chinese postman problem;
7
Dissertation, University of Maryland.
8
[5] Deb K., Pratap A., Agarwal S., Meyarivan T. (2002), A fast and elitist multiobjective genetic
9
algorithm: NSGA-II; IEEE Transactions on Evolutionary Computation 6(2); 182–197.
10
[6] Dijkgraaf E., Gradus R. (2007), Fair competition in the refuse collection market; Applied Economic
11
Letters 14(10); 701–704.
12
[7] Dijkstra E.W. (1959), A note on two problems in connection with graphs; NumerischeMathematik
13
1(1); 269–271.
14
[8] Dror M., Stern H.I. (1979), Routing Electric Meter Readers; Computers and Operations Research
15
6(4); 209–223.
16
[9] Dror M., Levy L. (1986), A vehicle routing improvement algorithm comparison of a greedy and a
17
matching implementation for inventory routing; Computer and Operation Research 13(1); 33–45.
18
[10] Eglese R.W., Li L. (1992), Efficient Routing for Winter Gritting; Journal of Operational Research
19
Society 43(11); 1031–1034.
20
[11] Fleury G., Lacomme,P., Prins C. (2004), Evolutionary algorithms for stochastic arc routing
21
problems,.In: Raidl G.R., Rothlauf F., Smith G.D., Squillero G., Cagnoni S., Branke J., Corne D.W.,
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Drechsler R., Jin Y., Johnson C.G. (Eds.), Applications of Evolutionary Computing, Springer-Verlag:
23
Berlin; 501-512.
24
[12] Fleury G., Lacomme P., Prins C., Sevaux M. (2005), A memetic algorithm for a bi-objective and
25
stochastic CARP; Multi Objective Combinatorial Optimization, The 6th Metaheuristics International
26
Conference; 22-26.
27
[13] Garcia-Najera A., Bullinaria J.A. (2009), Bi-objective optimization for the vehicle routing problem
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with time windows: using route similarity to enhance performance, In: Ehrgott M., Fonseca C.,
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Gandibleux X., Hao J.K., Sevaux M., editors; Proceedings of fifth international conference on
30
evolutionary multi- criterion optimization, Lecture Notes in Computer Science 5467; 275–89.
31
[14] Garcia-Najera A.,Bullinaria J.A. (2011), An improved multi-objective evolutionary algorithm for the
32
vehicle routing problem with time windows; Computers and Operation Research 38(1); 287-300.
33
[15] Gen M., Cheng R.W. (2000), Genetic algorithms and engineering optimization; John wiley&Sons;
34
[16] Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning; Addison
35
[17] Goldberg D.E., Deb K. (1991), A comparative analysis of selection schemes used in genetic
36
algorithms; In: Foundations of genetic algorithms, Morgan Kaufmann publisher; 69–93.
37
[18] Golden B.L., Wong R.T. (1981), Capacitated arc routing problems; Networks 11; 305–315.
38
[19] Golden B.L., DeArmon J.S., Baker E.K. (1983), Computational experiments with algorithms for a
39
class of routing problems; Computers and Operations Research 10; 47–59.
40
[20] Grandinetti1 L., Guerriero F., Lagana D., Pisacane O. (2010), An approximate e-constraint method for
41
the Multi-objective Undirected Capacitated Arc Routing Problem; Lecture Notes in Computer
42
Science6049; 214-255.
43
[21] Greistorfer P. (2003), A Tabu Scatter Search Metaheuristic for the Arc Routing Problem; Computers
44
and Industrial Engineering 44(2); 249–266.
45
[22] Han H.S., Yu J.J., Park C.G., Lee J. G. (2004), Development of inspection gauge system for gas
46
pipeline; Korean Society Mechanical Engineering International Journal 18(3); 370–378.
47
[23] Hertz A., Laporte G., Mittaz M. (2000), Atabu search heuristic for the capacitated arc routing problem;
48
Operations Research 48(1); 129–135.
49
[24] Hertz A., Mittaz M. (2001), A variable neighborhood descent algorithm for the undirected capacitated
50
arc routing problem; Transportation Science 35(4); 425–434.
51
[25] Kaufmann A., Gupta M.M. (1985), Introduction to Fuzzy Arithmetic, Theory and Applications; Van
52
Nostrand Reinhold, New York.
53
[26] Labelle A., Langevin A., Campbell J.F. (2002), Sector design for snow removal and disposal in urban
54
areas; Socio-Economic Planning Sciences 36(3); 183–202.
55
[27] Lacomme P., Prins C., Ramdane-CherifW. (2004), Competitive memetic algorithms for arc routing
56
problems; Annals of Operation Research 131; 159–185.
57
[28] Lacomme P., Prins C., Sevaux M. (2006), A genetic algorithm for a biobjective capacitated arc routing
58
problem; Computers and Operations Research 33(12); 3473–3493.
59
[29] Liu B. (2006), A survey of credibility theory; Fuzzy Optimization and Decision Making 5(4); 387-408.
60
[30] Mei Y., Tang K., Yao X. (2010), Capacitated arc routing problem in uncertain environments; IEEE
61
world congress on Computational Intelligence; Spain, 1400-1407.
62
[31] Mei Y., Tang K., Yao X. (2011), Decomposition-Based Memetic Algorithm for Multi-Objective
63
Capacitated Arc Routing Problem; IEEE Transactions on Evolutionary Computation15(2);151-16.
64
[32] Mitra K. (2009), Multiobjective optimization of an industrial grinding operation under uncertainty;
65
Chemical Engineering Science 64; 5043-5056.
66
[33] Muyldermans L., Pang G. (2010), A guided local search procedure for the multi-compartment
67
capacitated arc routing problem, Computers and Operations Research 37; 1662–1673.
68
[34] Potvin J.Y., Bengio S. (1996), The vehicle routing problem with time windows, part II: Genetic
69
search;Informs Journal of Computing 8(2); 165–172.
70
[35] Santos L., Coutinho-Rodrigues J.R., Current J.R. (2009), An improved heuristic for the capacitated arc
71
routing problem; Computers and OperationsResearch 36(9); 2632–2637.
72
[36] Tang K., Mei Y., Yao X. (2009), Memetic Algorithm with Extended Neighborhood Search for
73
Capacitated Arc Routing Problems; IEEE Transactions on Evolutionary Computation 13(5); 1151-
74
[37] Tobin G.A., Brinkmann R. (2002), The effectiveness of street sweepers in removing pollutants from
75
road surfaces in Florida; Journal of Environmental Science and Health (Part A) 37(9); 1687–1700.
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[38] Ulusoy G. (1985), The fleet size and mix problem for capacitated arc routing; European Journal of
77
Operational Research 22; 329–37.
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[39] Van Veldhuizen D.A. (1999), Multiobjective Evolutionary Algorithms: Classifications, Analyses, and
79
New Innovations; Ph.D. thesis, AFIT/DS/ENG/99-01, Air Force Institute of Technology, Wright-
80
Patterson AFB, Ohio.
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[40] Van Veldhuizen D.A., Lamont G.B. (2000), Multiobjective evolutionary algorithms: Analyzing the
82
state-of-the-art; Evolutionary Computation 8(2); 125-147.
83
[41] Wøhlk S. (2005), Contributins to arc routing; PhD thesis, University of Southern Denmark.
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[42] Zadeh L.A. (1965), Fuzzy Sets; Information and Control 8; 338-353.
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[43] Zadeh L.A. (1978), Fuzzy sets as a basis for a theory of possibility; Fuzzy sets and Systems 1; 3-28.
86
ORIGINAL_ARTICLE
A Queueing-Inventory System with Repair Center for Defective Items and One-for-One Ordering Policy
In this paper we consider a system consisting of a supplier with a single processing unit, a repair center, and a retailer with Poisson demand. We assume that the retailer applies one-for-one ordering policy with backorders for his inventory control. The retailer’s orders form a queue in the supplier processing unit. We also assume that a certain fraction of the products produced by the supplier are defective and they must be repaired in the repair center before going to the retailer. Further, we assume that the processing time of each unit at the supplier and the service time of each defective item in the repair center are exponentially distributed random variables with known means. The purpose of this paper is to obtain the optimal value of the inventory position of the retailer which minimizes the total cost of the system. To achieve this purpose we consider two cases, Case (1) the ratio of the arrival rate to service rate, at the supplier and at the repair center are not equal and Case (2) these ratios are equal. For both cases, we first derive the long run probability distribution of the number of outstanding orders of the retailer. Then we obtain the average on hand inventory and backorders of the retailer, and derive the long run unit total cost of the system. We also investigate the convexity of this total system cost function and obtain the optimal value of the inventory position of the retailer and present a numerical example.
http://www.jise.ir/article_4053_de3c2c29b0b3aa14140e67566c83bbdc.pdf
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239
One-for-one ordering
Defectives
Inventory control
Poisson demand
Babak
Haji
true
1
Department of Industrial and Systems Engineering, University of Southern California, Los Angeles,
California, USA
Department of Industrial and Systems Engineering, University of Southern California, Los Angeles,
California, USA
Department of Industrial and Systems Engineering, University of Southern California, Los Angeles,
California, USA
AUTHOR
Alireza
Haji
ahaji@sharif.edu
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] Berman O., Kim E. (2001), Dynamic order replenishment policy in internet-based supply chains;
1
Mathematics Methods of Operations Research. 53; 371–390.
2
[2] Hadley G., Whitin T.M. (1963), Analysis of Inventory Systems; Prentice Hall Inc.
3
[3] Haji R., Saffari M., Haji A. (2011), Queueing Inventory System in a Two-level Supply Chain with Onefor-
4
One Ordering Policy; Journal of Industrial and System Engineering, 5 (1); 52-62.
5
[4] He Q.M., Jewkes E.M., Buzacott J. (2002), Optimal and near-optimal inventory policies for a make to
6
order inventory-production system; European Journal of Operational Research 141; 113-132.
7
[5] Liu L., Liu X., Yao D.D. (2004), Analysis and Optimization of a Multistage Inventory-Queue System;
8
Management Science 50 (3); 365-380.
9
[6] Olsson R. J., Hill R.M. (2007), A two-echelon base-stock inventory model with Poisson demand and the
10
sequential processing of orders at the upper echelon; European Journal of Operational Research 177;
11
310–324.
12
[7] Ross S. M. (2010), Introduction to Probability Modes; Academic Press 10th Edition.
13
[9] Saffari M., Haji R., Hassanzadeh F. (2011), A queueing system with inventory and mixed exponentially
14
distributed lead times; International Journal of Advance ManufacturingTechnology 53; 1231-1237
15
[8] Schwarz M., Daduna H. (2006), Queueing systems with inventory management with random lead times
16
and with backordering; Mathematical Methods of Operations Research 64; 383–414.
17
[9] Schwarz M., Sauer C., Daduna H., Kulik R., Szekli R. (2006), M/M/1 queueing systems with inventory;
18
Queueing System 54; 55–78.
19
[10] Wang Y., Cohen M.A., Zheng Y.S. (2000), A two echelon repairable inventory system with stockingcenter-dependent depot replenishment lead times; Management Science 46 (11); 1441-1453.
20
[11] Zhao N., Lian Z. (2011), Aqueuing-inventory system with two classes of customers; International
21
Journal of Production Economics 129; 225–231.
22
ORIGINAL_ARTICLE
A Note on Runway Capacity Definition and Safety
The following is a discussion on the different aspects of the term capacity as well as a brief look at the commonly used capacity definitions in industrial and operations engineering in general and in Civil Aviation in particular. We maintain that quality factors (including more specifically here, safety) must be explicitly considered in runway capacity definition, and accordingly, we provide a revision on previous perspectives.
http://www.jise.ir/article_4054_e8f904c95f9fc73bf11249ea1b4adfaf.pdf
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244
Capacity
Air Transportation
Runway
safety
Babak
Ghalebsaz Jeddi
true
1
Dept. of Industrial Engineering, Sharif University of Technology, Zip 14588, Tehran, Iran
Dept. of Industrial Engineering, Sharif University of Technology, Zip 14588, Tehran, Iran
Dept. of Industrial Engineering, Sharif University of Technology, Zip 14588, Tehran, Iran
AUTHOR
[1] AdamE., Ebert R.(1989) Production and Operations Management, Prentice Hall
1
[2] Bell G.E., (1949), Operational research into air traffic control. Journal of Royal Aeronautical Society 53;
2
[3] De Neufville R., Odoni A.(2003),Airport Systems Planning, Design and Management;McGraw Hill
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Companies Inc.
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[4] HeizerJ.H., RenderB. (2002),Operations Management; Prentice Hall.
5
[5] Hockaday S.L.M., Chatziioanou A. (1986), An analytical method for aircraft collision risk estimation;
6
Transportation Research Part B 20B(5); 415-428.
7
[6] Hockaday S.L.M., Kanafani A.K.(1974), Developments in airport capacity
8
analysis;TransportationResearch 8; 171-180
9
[7] Jeddi B.G., Donohue G.L., Shortle J.F. (2009), A statistical analysis of aircraft landing process;Journal
10
of Industrial and Systems Engineering 3(3); 152-169.
11
[8] Jeddi B.G.,Shortle J.F.(2007),Throughput, Risk and Economic Optimality of Runway Landing
12
Operations;7th Air Traffic Management R&D Seminar, Barcelona, Spain.
13
[9] Jeddi B.G., Shortle J.F., Sherry L.(2006), Statistical separation standards for the aircraft–approach
14
process; 25th Digital Avionics System Conference (DASC), Portland, Oregon, USA; pp. 2A1-1 to 2A1-
15
[10] Newell G.F. (1979), Airport capacity and delays;Transportation Science 13(3); 201-241.
16
[11] ACI, Airports Console International, Annual Traffic data,
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www.aci.aero/cda/aci_common/display/main/aci_content07_c.jsp?zn=aci&cp=1-5-54_666_2__; 2010.
18