ORIGINAL_ARTICLE
A Model for Runway Landing Flow and Capacity with Risk and Cost Benefit Factors
As the demand for the civil aviation has been growing for decades and the system becoming increasingly complex, the use of systems engineering and operations research tools have shown to be of further use in managing this system. In this study, we apply such tools in managing landing operations on runways (as the bottleneck and highly valuable resources of air transportation networks) to handle its optimal and safe usage. We consider a uniform aircraft fleet mix landing on a runway with two major landing risks of wake-vortex encounter and simultaneous runway occupancy. Here, we empirically estimate minimum safe wake-vortex separation thresholds, extend go-around procedure to avoid wake-vortex encounter, and enforce the go-arounds assumed to be risk free. We introduce cost-benefit factors to study implications of enforced go-arounds, and develop models to adjust the average separation to maximize the net economic outcome. This also estimates the runway’s true landing capacity, and provides a ground for quantifying effect of separation variance on optimal throughput. An estimation of the economic effect of wake-vortex phenomenon is also presented. Illustrations are provided through real world data.
https://www.jise.ir/article_4055_45d1edffd19bc033fcb1a6aac82d0607.pdf
2012-04-01
1
19
Aircraft separation
Landing safety
safety
Wake-vortex
Landing capacity
Goaround
procedure
Variance reduction
Cost and benefit analysis
Babak
Ghalebsaz Jeddi
1
Dept. of Industrial Engineering, Sharif University of Technology, Tehran, Zip 14588,Iran
AUTHOR
John F.
Shortle
2
Dept. of Systems Engineering and Operations Research, George Mason University, Fairfax, VA22030, USA
AUTHOR
[1] Airports Console International (2010), Annual Traffic data;http://www.aci.aero/Data-Centre/Annual-
1
Traffic-Data/Movements/2010-final
2
[2] Boesel J., Bodoh D. (2004), Simulating Airspace Redesign for Arrivals to Detroit Wayne County
3
Airport;Proceedings of Winter Simulation Conference; 1318-1325.
4
[3] Federal Aviation Administration (1993),FAA Order 7110.65, Para 2,1,19 and Para 3,9,6 (September ).
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[4] Gerz T., Holzäpfel F., Darracq D. (2002), Commercial aircraft wake vortices;Progress in Aerospace
6
Sciences 38; 181-208.
7
[5] Gilbo E.P. Sept. (1993), Airport Capacity: representation, estimation, optimization;IEEE Transactions
8
on Control Systems Technology 1(3); 144-154.
9
[6] Hockaday S.L.M., Chatziioanou A. (1986), An analytical method for aircraft collision risk
10
estimation;Transportation Research Part B 20B(5); 415-428.
11
[7] Hockaday S.L.M., Kanafani A.K. (1974), Developments in airport capacity analysis;Transportation
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Research 8; 171-180
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[8] Jeddi B.G., Shortle J.F., Sherry L. (2006), Statistical separation standards for the aircraft–approach
14
process;Proceedings of 25th Digital Avionics System Conference, Portland, Oregon, USA; 2A11-13.
15
[9] Jeddi, B.G., Shortle J.F. (2007), Throughput, Risk and Economic Optimality of Runway Landing
16
Operations;7th Air Traffic Management R&D Seminar, Barcelona, Spain.
17
[10] Jeddi, B.G., Donohue G.L., Shortle J.F. (2009), A statistical analysis of aircraft landing process;Journal
18
of Industrial and Systems Engineering 3(3); 152-169.
19
[11] Jeddi, B.G., (2012), A Note on Runway Capacity Definition, Journal of Industrial and Systems
20
Engineering, 5(4); pp
21
[12] Lee D., Kostiuk, P.F., Hemm, R.V., Wingrove III, E.R., Shapiro, G. (1997), Estimating the effects of the
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terminal area productivity program;Logistics Management Institute, McLean, Virginia, NASA
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Contractor Report 201682.
24
[13] Newell G.F. (1979), Airport capacity and delays;Transportation Science 13(3); 201-241.
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[14] Nolan M.S. (2011), Fundamentals of Air Traffic Control 5th Ed.; Delmar, Cengage Learning.
26
[15] Reich P. (1964), An Analysis of Planned Aircraft Proximity and Its Relation to Collision Risk, with
27
Special Reference to the North Atlantic Region 1965-1971; Royal Aircraft Establishment Technical
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Report 64042, Famborough. England.
29
[16] Robins R.E., Delisi D.P. (2002), NWRA AVOSS wake-vortex prediction algorithm version
30
3.1.1;NASDA/CR-2002-211746.
31
ORIGINAL_ARTICLE
A Comparison of Four Multi-Objective Meta-Heuristics for a Capacitated Location-Routing Problem
In this paper, we study an integrated logistic system where the optimal location of depots and vehicles routing are considered simultaneously. This paper presents a new mathematical model for a multi-objective capacitated location-routing problem with a new set of objectives consisting of the summation of economic costs, summation of social risks and demand satisfaction score. A new multi-objective adaptative simulated annealing (MOASA) is proposed to obtain the Pareto solution set of the presented model according to the previous studies. We also apply three multi-objective meta-heuristic algorithms, namely MOSA, MOTS and MOAMP, on the simulated data in order to compare the proposed procedure performance. The computational results show that our proposed MOASA outperforms the three foregoing algorithms.
https://www.jise.ir/article_4056_fe2ace436cf9be17c5252d9557e94e61.pdf
2012-04-01
20
33
Location-routing problem
Demand satisfaction score
Multi-objective metaheuristic
algorithms
Pareto solution set
Farshid
Samaei
1
Department of Industrial Engineering, Shahed University, Tehran, Iran
AUTHOR
Mahdi
Bashiri
2
Department of Industrial Engineering, Shahed University, Tehran, Iran
AUTHOR
Reza
Tavakkoli-Moghaddam
3
Departmentof Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
[1] Albareda-Sambola M., Diaz J., Fernandez E. (2005),A compact model and tight bounds for a
1
combined location-routing problem. Computers and Operations Research 32; 407–428.
2
[2] Barreto S., Ferreira C., Paixão J., Santos B. (2007),Using clustering analysis in a capacitated locationrouting
3
problem. European Journal of Operational Research, 179; 968-977.
4
[3] Belenguer J., Benavent E., Prins C., Prodhon C., Wolfler Calvo R. (2011), A Branch-and-Cut method
5
for the Capacitated Location-Routing Problem. Computers and Operations Research 38; 931-941.
6
[4] Bouhafs L., Hajjam A., Koukam A. (2006),A combination of simulated annealing and ant colony
7
system for the capacitated location-routing problem. Lecture Notes in Computer Science 4251; 409–
8
[5] Caballero R., Gonzalez M., Flor M., Molina J., Paralera C. (2007), Solving a multiobjective location
9
routing problem with a metaheuristic based on tabu search. Application to a real case in Andalusia.
10
European Journal of Operational Research 177; 1751–1763.
11
[6] Deb, K. (2001),Multiobjective optimization using evolutionary algorithms. Wiley, Chichester, UK.
12
[7] Duhamel C., Lacomme P., Prins C., Prodhon C. (2010),A GRASP×ELS algorithm for the capacitated
13
location-routing problem. Computers and Operations Research, 37; 1912-1923.
14
[8] Gover F., Kochenberger G. (2003),Handbook of metahuristics. New York: Kluwer Academic
15
Publishers.
16
[9] Lin C., Kwok R. (2006), Multi-objective metaheuristics for a location-routing problem with multiple
17
use of vehicles on real data and simulated data. European Journal of Operational Research 175; 1833–
18
[10] Lin C., Chow C., Chen A. (2002), A location-routing-loading problem for bill delivery services.
19
Computers and Industrial Engineering 43; 5–25.
20
[11] Min H., Jayaraman V., Srivastava R. (1998), Combined location-routing problems: a synthesis and
21
future research directions. European Journal of Operational Research 108; 1–15.
22
[12] Nagy G., Salhi S. (2007), Location-routing: Issues, models and methods. European Journal of
23
Operational Research 177; 649–672.
24
[13] Perl J., Daskin M. (1984), A unified warehouse location-routing methodology. Journal of Business
25
Logistics 5; 92–111.
26
[14] Prins C., Prodhon C., Calvo R. W. (2006b),Solving the capacitated location-routing problem by a
27
GRASP complemented by a learning process and a path relinking.4OR 4; 221–238.
28
[15] Prins C., Prodhon C., Wolfler Calvo R. (2006a), A memetic algorithm with population management
29
(MA|PM) for the capacitated location-routing problem. Lecture Notes in Computer Science 3906; 183–
30
[16] Prins C., Prodhon C., Ruiz A., Soriano P., Wolfler Calvo R. (2007),Solving the capacitated locationrouting
31
problem by a cooperative lagrangean relaxationgranular tabu search heuristic. Transportation
32
Science 41(4); 470–483.
33
[17] Prodhon C. (2011), A hybrid evolutionary algorithm for the periodic location-routing problem.
34
European Journal of Operational Research 210; 204-212.
35
[18] Schott J. (1995),Fault tolerant design using single and multicriteria genetic algorithms
36
optimization.Department of Aeronautics and Astronautics. Cambridge: Master’s thesis, Massachusetts
37
Institute of Technology.
38
[19] Tavakkoli-Moghaddam R., Makui A., Mazloomi Z. (2010), A new integrated mathematical model for
39
a bi-objective multi-depot location-routing problem solved by a multi-objective scatter search algorithm. Journal of Manufacturing Systems 29; 111–119.
40
[20] Tuzun D., Burke L. (1999), A two-phase tabu search algorithm to the location routing problem.
41
European Journal of Operational Research 116; 87–89.
42
[21] Ulungu E. L., Teghem J., Fortemps P. H., Tuyttens D. (1999), MOSA method: a tool for solving
43
multiobjective combinatorial optimization problems. Journal of Multicriteria Decision Analysis 8;
44
[22] Van Veldhuizen D. (1999),Multiobjective evolutionary algorithms: Classification, analyses and new
45
innovations.Dayton, Ohio: Doctoral dissertation, Air Force Institute of Technology.
46
[23] Wu T., Low C., Bai J. (2002), Heuristic solutions to multi-depot location-routing problems. Computers
47
and Operations Research 29; 1393–1415.
48
[24] Yu V. F., Shih-Wei Lin, Wenyih Lee, Ching-Jung Ting. (2010), A simulated annealing heuristic for
49
the capacitated location routing problem. Computers & Industrial Engineering; 288–299.
50
[25] Zitzler E. (1999),Evolutionary algorithms for multiobjective optimization: Methods and
51
applications.Zurich: PhD thesis, Swiss Federal Institute of Technology Zurich.
52
[26] Zitzler E., Deb K., Thiele L. (2000), Comparison of multiobjective evolutionary algorithms: Empirical
53
results. Evolutionary Computation 8(2); 173-195.
54
ORIGINAL_ARTICLE
A Fuzzy Random Minimum Cost Network Flow Programming Problem
In this paper, a fuzzy random minimum cost flow problem is presented. In this problem, cost parameters and decision variables are fuzzy random variables and fuzzy numbers respectively. The object of the problem is to find optimal flows of a capacitated network. Then, two algorithms are developed to solve the problem based on Er-expected value of fuzzy random variables and chance-constrained programming. Furthermore, the results of two algorithms will be compared. An illustrative example is also provided to clarify the concept.
https://www.jise.ir/article_4057_ea9d514e9bf31829c10b874267b35c8b.pdf
2012-04-01
34
47
network flow programming
fuzzy random variable
Er-expected value
chanceconstrained
programming
Javad
Nematian
1
Department of Industrial Engineering, University of Tabriz, Iran
AUTHOR
Kourosh
Eshghi
eshghi@sharif.edu
2
Department of Systems Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
[1] Ahuja R. K., Magnanti T. L., Orlin J. B. (1993), Network Flows; Prentice-Hall, EnglewoodCliffs, NJ.
1
[2] Bellman R., Zadeh L. A. (1970), Decision-making in a fuzzy environment; Management Science 17;
2
[3] Buckley J.J., Feuring T. (2000), Evolutionary algorithm solution to fuzzy problems: Fuzzy linear
3
programming; Fuzzy Sets and Systems 109; 35-53.
4
[4] Charnes A., Cooper w. w. (1959), Chance-constrained programming; Management science 6; 73-79.
5
[5] Diamond P., Korner R. (1997), Extended fuzzy linear models and least squares estimates; Computers
6
and Mathematics with Applications 33; 15–32.
7
[6] Dubois D., Prade H. (1979), Fuzzy real algebra: Some results; Fuzzy Sets and System 2; 327-348.
8
[7] Dubois D., Prade H. (1980), Fuzzy Sets and Systems: Theory and Application; Academic Press; New
9
[8] Dubois D., Prade H. (1988), Fuzzy numbers: An overview. In Analysis of fuzzy information; CRC
10
Press 2; pp 3-29.
11
[9] Eshghi K., Nematian J. (2008), Special classes of mathematical programming models with fuzzy
12
random variables; Journal of Intelligent & Fuzzy Systems 19; 131-140.
13
[10] Hukuhara M. (1967), Integration des applications measurable dont la valeur est un compact convexe;
14
Funkcialaj Ekvacioj 10; 205–223.
15
[11] Katagiri H., Ishii H. (2000), Linear programming problem with fuzzy random constraint;
16
Mathematica Japonica 52; 123-129.
17
[12] Kwakernaak H. (1978), Fuzzy random variable-1: Definitions and theorems; Information Sciences 15;
18
[13] Lin C. J., Wen U. P. (2004), A labeling algorothm for the fuzzy assignment problem; Fuzzy Sets and
19
Systems 142; 373-391.
20
[14] Liu Y-K., Liu B. (2003), Fuzzy random variables: A scalar expected value operator; Fuzzy
21
Optimization and Decision Making 2; 143-160.
22
[15] Puri M. L., Ralescu D. A. (1986), Fuzzy random variables; Journal of Mathematical Analysis and
23
Application 114; 409-422.
24
[16] Okada S., Soper T. (2000), A shortest path problem on a network with fuzzy arc lengths; Fuzzy Sets
25
and Systems 109; 129-140.
26
[17] Shih H. S., Stanley Lee E. (1999), Fuzzy multi-level minimum cost flow problems; Fuzzy Sets and
27
Systems 107; 159-176.
28
[18] Wang G.-Y., Zang Yue (1992), The theory of fuzzy stochastic processes; Fuzzy Sets and Systems 51;
29
[19] Wang G-Y., Zhong Q. (1993), Linear programming with fuzzy random variable coefficients; Fuzzy
30
sets and Systems 57; 295-311.
31
[20] Wang J. R. (1999), A fuzzy set approach to activity scheduling for product development; Journal of
32
the Operational Research Society 50;1217–1228.
33
[21] Williams H. P. (1999), Model building in mathematical programming, Fourth edition;John Wiley &
34
[22] Zadeh L. A. (1973), The concept of linguistic variable and its application to approximate reasoning;
35
Memorandum ERL- M 411; Berkeley.
36
[23] Zimmermann, H.J. (1978), Fuzzy programming and linear programming with several objective
37
function; Fuzzy Sets and Systems 1; 45-55.
38
ORIGINAL_ARTICLE
The P-Center Problem under Uncertainty
Facility location decisions play a prominent role in strategic planning of many firms, companies and governmental organizations. Since in many real-world facility location problems, the data are subject to uncertainty, in this paper, we consider the P-center problem under uncertainty of demands. Using Bertsimas and Sim approach, we develop a robust model of the problem as an integer programming model. Furthermore, we develop a tabu search algorithm for solving the problem. Finally we use design of experiments (DOE) to adjust the parameters of tabu search algorithm. The numerical results of algorithm are presented accordingly.
https://www.jise.ir/article_4058_f386294adb4353323a811ed9f2e06648.pdf
2012-04-01
48
57
Facility location
robust optimization
P-Center
Tabu Search
Majid
Taghavi
1
Dept. of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
Hassan
Shavandi
shavandi@sharif.edu
2
Dept. of Industrial Engineering, Sharif University of Technology, Tehran, Iran
AUTHOR
[1] Arostegui M., Kadipasaoglu N., Khumawala M. (2006), An empirical comparison of Tabu Search,
1
Simulated Annealing, and Genetic Algorithms for facilities location problems; International Journal of
2
Production Economics103; 742-754.
3
[2] Ben-Tal A., Nemirovski A. (1998), Robust convex optimization, Math. Operations Research 23; 769-
4
[3] Bertsimas D., Sim M. (2003), Robust discrete optimization and networkflows;Mathematical
5
Programming Series B; 98, 49–71.
6
[4] El-Ghaoui, Lebret H. (1997), Robust solutions to least-square problems to uncertain data matrices;SIAM
7
Journal on Matrix Analysis and Applications 18; 1035-1064.
8
[5] Glover F. (1989), Tabu Search Part I;ORSA Journal on Computing 1; 190-206.
9
[6] Glover, F (1990), Tabu Search Part II;ORSA Journal on Computing 2; 4-32.
10
[7] Jia H. Fernando, Dessouki G. (2007), A modeling framework for facility location of medical services for
11
large-scale emergencies;IIE Transactions 39; 41–55.
12
[8] Soyster A.L. (1973), Convex programming with set-inclusive constraints and applications to inexact
13
linear programming; Operations Research 21; 1154-1157.
14