ORIGINAL_ARTICLE
Comparing the Accessibility of Rescue Centers in the Districts of Tehran Municipality after Catastrophic Earthquakes
In this research, the centers involved in relief operations in Tehran traffic area districts have been compared and the supply-demand ratio has been specified through a relation presented separately for each district. The supply has been evaluated based on three parameters: 1) No of relief centers, 2) congestion of the main road (the ratio of the length of the main road to the area of the district), and 3) the ratio of the length of the main road to those of the secondary roads. The demand has been considered based on the number of casualties waiting for relief per unit area of each district.
After delimiting the districts in the case study and extracting the network’s populations and roads information layers in the GIS, two road classes were specified: 1) main roads (capable of working under disaster conditions) and 2) secondary roads (capable of obstructing relief operations in needy areas). Next, the parameters were co equalized with their corresponding maximum values and scaled in the 0-100 range. The final results have been shown separately for each district (totally 32 in number) as the accessibility index. Accordingly, districts with indices smaller than 2 are considered as weakly accessible and those with indices more than 15 as properly accessible; districts with indices 2-8.5 have average accessibility.
http://www.ijte.ir/article_55928_55434e9c2a0f40c97f7e9e170f2da753.pdf
2019-01-01
191
205
10.22119/ijte.2018.55928
Earthquake
accessibility
relief
Transportation
district
Maghsood
Pouryari
mpooryari@yahoo.com
1
PhD. Candidate, School of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Nemat
Hasani
hassani@pwut.ac.ir
2
Assistant Professor, School of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
AUTHOR
Ahmadreza
Mahboobi Ardakani
ar_mahboubi@yahoo.com
3
Associate Professor, School of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
AUTHOR
Kiarash
Naser Asadi
nasserasadi@gmail.com
4
Assistant Professor, School of Civil Engineering, Zanjan University, Zanjan, Iran
AUTHOR
-Alinezhad, H., Yaghubi, S., Hoseini Motlagh, S. M. Allahyari, M. and Saghafi, N. (2017)" An Improved Particle Swarm Optimization for a Class of Capacitated Vehicle Routing", International Journal of Transportation Engineering (IJTE) , Vol. 5, No. 20, pp. 331-347.
1
-Babaeia, M., Shariat Mohaymany, A. and Nikoo, N. (2017)" Emergency transportation network designproblem: Identification and evaluation of disaster response routes", International Journal of Disaster Risk Reduction, http://dx.doi.org/10.1016/j.ijdrr.2017.07.003
2
-Balijepalli, N. C. and ppong, O. (2014) "Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas", Journal of Transport Geography, https://doi.org/10.1016/j.jtrangeo.2014.06.025
3
-California Department of Transportation (1994) "Post-eearthquake investigation team(PEQIT)", report: Northridge earthquake, 17 January.
4
-EM-DAT (2015) "The OFDA/CRED International Disaster Database", http://www.emdat.net.
5
-EunSu, Lee (2014) "Designing service coverage and measuring accessibility and serviceability of rural and small urban ambulance systems", Systems, Vol. 2, pp.34-53 ISSN 2079-8954, doi:10.3390/systems2010034.
6
-Japan International Cooperation Agency – JICA- (2000) "The study on seismic microzoning of the greater Tehran area, Center for earthquake and environmental studies of Tehran(Cest)",Tehran Municipality.
7
-Luis E.de la Torre, Irina S.Dolinskaya,Karen R.Smilowitz.(2011) "Disaster Relief Routing: Integrating Research and Practice", Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2011.06.001
8
- Mohamadi, A. and Yaghubi, S. (2017) "A bi-objective stochastic model for emergency medical services network design with backup services for disasters under disruptions: An earthquake case study", http://dx.doi.org/10.1016/j.ijdrr.2017.05.003.
9
-Nagurney,A.&Qiang,Q.(2007) "A transportation network efficiency measure that captures flows,behavior,and costs with applications to network component importance identification and vulnerability".Proceedings of the POMS 18th Annual Conference,Dallas, Texas,USA,MAY.
10
-Scott, D. M., Novak, D. C., Aultman-hall, L. and Guo, F. (2006) "Network robustness index: A new methods for identifying critical links and evaluating the performance of transportation networks", Journal of Transport Geography,Vol. 14, pp.215-227.
11
- Giovinazzi, S. and Nicholson, A. (2010) "Transport network reliability in seismic risk analysis and management",14 ECEE
12
-Statistical Center of Iran(2016) "Tehran Statistical Yearbook", 2016.
13
-Tavakkoli-Moghaddam, R., Raziei, Z. and Tabrizian, S. (2016) "Solving a bi-objective multi-product vehicle routing problem with heterogeneous fleets under an uncertainty condition", International Journal of Transportation Engineering, Vol. 3, No. 3, pp. 207-225.
14
-Taylor, M. A., Sekhar, S. V. and Este, G. M. (2006) "Application of accessibility based methods for vulnerability analysis of strategic road networks", Networks and Spatial Economics, Vol. 6, pp.267-291.
15
-Tehran Comprehensive Transportation and Traffic Studies Company (2015) "Selected data of Tehran transportation", Tehran: Tehran Municipality.
16
-Tehran Comprehensive Transportation & Traffic Studies Company (2008) “Models of presenting the comprehensive studies of Tehran traffic and transportation”, Report 905-3-6.
17
-Tehran Disaster Mitigation and Management Organization (2010) "Determining site magnification coefficients, extracting fragility function, and evaluating fatalities due to earthquake for Tehran buildings", final report (chapter 3, 4).
18
- Tehran Disaster Mitigation and Management Organization (2015) "Rapid evaluation system of Tehran earthquake fatalities and damage", deputy prevention and risk reduction.
19
- Wakabayashi, H. and Iida,Y. (1992) "Upper and lower bounds of terminal reliability of road networks: An efficient method with Boolean algebra", Journal of Natural Disaster Science, Vol. 14, pp. 29-44.
20
ORIGINAL_ARTICLE
Car Use Behavioral Study for Commuting Trips: Parents’ Work Trips and Children’s School Trips
Negative consequences of car use along with deficiencies for investment in environmental friendly modes, have driven authorities to search for soft measure in order to encourage people for modal shift. The main objective of this study is to propose a model which describes car use behavior on parents’ work trips and how this relates to mode choice regarding their children’s school trips. A questionnaire survey was carried out in 24 randomly selected primary schools in Tehran, Iran (n=4000). A mean structural analysis based on the sample (returned questionnaires =1876) demonstrates that parents who accompany their children on school trips have a stronger intention to use car for work trips than parents who do not accompany their children on school trips. A structural equation model based on the theory of planned behavior suggests that using car on school trips along with car use behavior for work trips, significantly increases the car use behavior for work trips. Findings suggest that individuals as parents, and also parents who accompany their children on school trips need to be considered in planning and policy setting for modal shift providing soft measures sensitive to this issue, since parents are highly influenced by their parental roles involved with children’s requirements in school trips.
http://www.ijte.ir/article_55927_acef8f6e12b7f8826361b3348c5f17f7.pdf
2019-01-01
207
216
10.22119/ijte.2018.55927
Car use behavior
mode choice
Theory of Planned Behavior
commuting trips
escorting children to school
Shideh
Ehteshamrad
shideh_ehtesham@yahoo.com
1
Ph.D., Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Mahmood
Saffarzadeh
saffar_m@modares.ac.ir
2
Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
LEAD_AUTHOR
Amirreza
Mamdoohi
armamdoohi@yahoo.com
3
Assistant Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran
AUTHOR
Sorousha
Saffarzadeh
saffarzadeh.ma@gmail.com
4
Civil and Environmental Engineering, Amirkabir University, Tehran, Iran
AUTHOR
Fernando Monterio
Figueira
5
Professor, Department of Civil Engineering, University of Lisbon, Portugal
AUTHOR
-Ajzen, I. (1991) “The theory of planned behavior”, Organizational Behavior and Human Decision Processes”, Vol. 50, No. 2, pp. 179–211.
1
-Bamberg, S., Rolle, D. and Weber, C. (2003) "Does habitual car use not lead to more resistance to change of travel mode?", Transportation, Vol. 30, pp. 97–108.
2
-Bamberg, S. and Schmidt, P. (2003) "Incentives, morality, or habit? Predicting students’ car use for university routes with the models of Ajzen, Schwartz, and Triandis", Environment and Behavior, Vol. 35, pp. 264-283.
3
-Broujerdian, A., Dehqani, S. and Fetanat, M. (2015) "Estimation model of two-lane rural roads safety index according to characteristics of the road and drivers’ behavior", International Journal of Transportation Engineering, Vol. 3, No. 1, pp. 17-29.
4
-Byrne, B. (2001) "Structural equation modeling with AMOS: Basic concepts, applications,and programming”, Taylore and Franncis.
5
-Chakrabarti, S. (2017) "How can public transit get people out of their cars? An analysis of transit mode choice for commute trips in Los Angeles", Transport Policy, Vol. 54, pp. 80-89.
6
-Ching-Fu, C. and Chao, W. (2011) "Habitual or reasoned? Using the theory of planned behavior, technology acceptance model, and habit to examine switching intentions toward public transit", Transportation Research Part F, Vol. 14,pp. 128–137.
7
-De Witte, A., Hollevoet, J., Dobruszkes, F. and Hubert F. (2013) "Linking modal choice to motility: A comprehensive review", Transportation Research Part A: Policy and Planning, Vol. 49, pp. 329–341.
8
-Donald, I., Cooper, S. and Conchie, S. (2014) "An extended theory of planned behaviour model of the psychological", Journal of Environmental Psychology, Vol. 40, pp. 39-48.
9
-Ehteshamrad, Sh. (2017) "Analysis of joint mode choice behaviour of parents for work and children for school trips", Ph.D. dessertation, Tarbiat Modares University.
10
- Kaewkluengklom, R., Satiennam, W., Jaensirisak, S. and Satiennam, T. (2017) "Influence of psychological factors on mode choice behaviour: Case study of BRT in Khon Kaen City, Thailand", Journal of Transportation Research Procedia, Vol. 25, pp. 5072-5082.
11
-Kline, R. (2005) "Principles and practice of structural equation modeling (2nd ed.), New York", The Guilford Press.
12
-Klöckner, C. and Blöbaum, A. (2010) "A comprehensive action determination model – towards a broader understanding of ecological behaviour using the example of travel mode choice", Journal of Environmental Psychology, Vol. 30, No. 4, pp. 574–586.
13
-Klockner, A. and Matthies, E. (2009) "Structural modeling of car use on the way to the university in different settings: Interplay of norms, habits, situational restraints, and perceived behavioral control", Journal of Applied Social Psychology, Vol. 39, No. 8, pp. 1807–1834. -Lo, S. H., van Breukelen, G. J. P., Peters, G. J. Y. and Kok, G. (2016) "Commuting travel mode choice among office workers: Comparing an extended theory of planned behavior model between regions and organizational sectors", Journal of Travel Behaviour and Society, Vol. 4, pp. 1-10.
14
-Mackett, R. L. (2013) "Children’s travel behaviour and its health implications", Transport Policy, Vol. 26,pp. 66-72.
15
-Mamdoohi, A., Seyedabrishami, S. and Baghestani A. (2014) "Final analytical comparison of aggregate and disaggregate mode choice models transferability", International
16
Journal of Transportation Engineering, Vol. 2, No. 2, pp. 145-154.
17
-McMillan, T. (2005) "Urban form and a child's trip to school: the current literature and a framework for future research", J. Plann. Lit, Vol. 19, No. 4,p. 440–456.
18
-Melonia, I., Sanjusta, B., Sottilea, E. and Cherchib, E. (2013) "Propensity for voluntary travel behavior changes: An experimental Analysis", Social and Behavioral Sciences, Vol. 87, pp. 31 – 43.
19
-Nelson, M., Foley, E., O'Gorman, D. J., Moyna, N. M. and Woods, C. B. (2008) "Active commuting to school: How far is too far?", International Journal of Behavioral Nutrition and Physical Activity, Vol. 5, No. 1.
20
-Park H., Noland R. and Lachapelle, U. (2013) "Active school trips: associations with caregiver walking frequency", Transport Policy, Vol. 29, pp. 23-28.
21
-Schumacke, R. and Lomax, R. (2012) "A beginner's guide to structural equation modeling: (3rd ed.)", Routledge.
22
-Schwartz, S. and Howard, J. (1981) "A normative decision-making model of altruism”, In J. P. Rushton & R. M. Sorrentino (Eds.)" Altruism and helping behavior, pp. 89–211.
23
ORIGINAL_ARTICLE
Modelling and Solving the Capacitated Location-Routing Problem with Simultaneous Pickup and Delivery Demands
In this work, the capacitated location-routing problem with simultaneous pickup and delivery (CLRP-SPD) is considered. This problem is a more realistic case of the capacitated location-routing problem (CLRP) and belongs to the reverse logistics of the supply chain. The problem has many real-life applications of which some have been addressed in the literature such as management of liquid petroleum gas tanks, laundry service of hotels and drink distribution. The CLRP-SPD is composed of two well-known problems; facility location problem and vehicle routing problem. In CLRP-SPD, a set of customers with given delivery and pickup demands should be supplied by a fleet of vehicles that start and end their tours at a single depot. Moreover, the depots and vehicles have a predefined capacity and the objective function is minimizing the route distances, fixed costs of establishing the depot(s) and employing the vehicles. The node-based MIP formulation of the CLRP-SPD is proposed based on the literature of the problem. To solve the model, a greedy clustering method (GCM) is developed which includes four phases; clustering the customers, establishing the proper depot(s), assigning the clusters to depot(s) and constructing the vehicle tours by ant colony system (ACS). The numerical experiments on two sets of test problems with different sizes on the number of customers and candidate depots show the efficiency of the heuristic method with the proposed method in the literature. Finally, performance of the heuristic method to the similar methods in the literature is evaluated by several standard test problems of the CLRP.
http://www.ijte.ir/article_63633_ee541b6e2a5d69de6ebca1ce92d53840.pdf
2019-01-01
217
235
10.22119/ijte.2018.67919.1306
Capacitated location-routing problem
simultaneous pickup and delivery
greedy clustering method
ant colony system
Ali
Nadizadeh
alinadizadeh1@gmail.com
1
Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran
LEAD_AUTHOR
Hasan
Hosseini Nasab
hhnn@gmail.com
2
Professor, Department of Industrial Engineering, Faculty of Engineering, Yazd University, Yazd, Iran
AUTHOR
-Angelelli, E. and Mansini, R. (2002) "The vehicle routing problem with time windows and simultaneous pick-up and delivery. In: Lecture Notes in Economics and Mathematical Systems", Springer, Germany, pp. 249–267.
1
-Barreto, S. (2003) "http://sweet.us.pt/_iscf143".
2
-Barreto, S., Ferreira, C., Paixao, J. and Sousa Santos, B. (2007) "Using clustering analysis in a capacitated location-routing problem", European Journal of Operational Research, Vol. 179, pp. 968-977.
3
-Belenguer, J. M., Benavent, E., Prins, C., Prodhon, C. and Wolfler-Calvo, R. (2011) "A Branch-and-cut method for the Capacitated Location-Routing Problem", Computers and Operations Research, Vol. 38 pp. 931-941.
4
-Bouhafs, L., Hajjam, A. and Koukam, A. (2010) "A hybrid heuristic approach to solve the capacitated vehicle routing problem", Journal of Artificial Intelligence: Theory and Application, Vol. 1, No. 1, pp. 31-34.
5
-Catay, B. (2010) "A new saving-based ant algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery", Expert Systems with Applications, Vol. 37, pp. 6809–6817.
6
-Derbel, H., Jarboui, B., Hanafi, S. and Chabchoub, H. (2012) "Genetic algorithm with iterated local search for solving a location-routing problem", Expert Systems with Applications, Vol. 39, pp. 2865-2871.
7
-Dorigo, M. and Gambardella, L. M. (1996) "A study of some properties of ant-Q", PPSN, springer-Verlag, Berlin, pp. 656-665.
8
-Drexl, M. and Schneider, M. (2015) "A survey of variants and extensions of the location-routing problem", European Journal of Operational Research, Vol. 241, No. 2, pp. 283-308.
9
-Duhamel, C., Lacomme, P., Prins, C. and Prodhon C. (2010) "A GRASP×ELS approach for the capacitated location-routing problem", Computers & Operations Research, Vol. 37, pp. 1912-1923.
10
-Escobar, J. (2014) "Heuristic algorithms for the capacitated location-routing problem and the multi-depot vehicle routing problem", 4OR, Vol. 12, No. 1, pp. 99-100.
11
-Ghatreh Samani, M. and Hosseini-Motlagh, S.-M. (2017) "A hybrid algorithm for a two-echelon location- routing problem with simultaneous pickup and delivery under fuzzy demand", International Journal of Transportation Engineering, Vol. 5, No. 1, pp. 59-85.
12
-Huang, S.-H. (2015) "Solving the multi-compartment capacitated location routing problem with pickup–delivery routes and stochastic demands", Computers & Industrial Engineering, Vol. 87, pp. 104-113.
13
-Karaoglan, I., Altiparmak, F., Kara, I. and Dengiz, B. (2011) "A branch and cut algorithm for the location-routing problem with simultaneous pickup and delivery", European Journal of Operational Research, Vol. 211, pp. 318-332.
14
-Karaoglan, I., Altiparmak, F., Kara, I. and Dengiz, B. (2012) "The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach", Omega, Vol. 40, No. 4, pp. 465-477.
15
-Laporte, G. (1988) "Location-routing problems. Vehicle Routing: Methods and Studies", B. L. In: Golden, Assad, A.A. (Eds.). North-Holland, Amsterdam, pp. 163–198.
16
-Lopes, R. B., Ferreira, C. and Santos, B. S. (2016) "A simple and effective evolutionary algorithm for the capacitated location–routing problem", Computers & Operations Research, Vol. 70, pp. 155-162.
17
-Manzour-al-Ajdad, S. M. H., Torabi, S. A. and Salhi, S. (2012) "A hierarchical algorithm for the planar single-facility location routing problem", Computers & Operations Research, Vol. 39, pp. 461-470.
18
-Marinakis, Y. and Marinaki, M. (2008) "A Particle Swarm Optimization Algorithm with Path Relinking for the Location Routing Problem", Journal of Mathematical Modelling and Algorithm, Vol. 7, pp. 59–78.
19
-Min, H., Jayaraman, V. and Srivastava, R. (1998) "Combined location-routing problems: A synthesis and future research directions", European Journal of Operational Research, Vol. 108, pp. 1–15.
20
-Nadizadeh, A. (2017) "The fuzzy multi-depot vehicle routing problem with simultaneous pickup and delivery: Formulation and a heuristic algorithm", International Journal of Industiral Engineering & Producion Research, Vol. 28, No. 3, pp. 325-345.
21
-Nadizadeh, A. and Kafash, B. (2017) "Fuzzy capacitated location-routing problem with simultaneous pickup and delivery demands", Transportation Letters, DOI: 10.1080/19427867.2016.1270798.
22
-Nadizadeh, A. and Hosseini Nasab, H. H. (2014) "Solving the dynamic capacitated location-routing problem with fuzzy demands by hybrid heuristic algorithm", European Journal of Operational Research, Vol. 238, No. 2, pp. 458-470.
23
-Nadizadeh, A., Sadegheih, A. and Sabzevari Zadeh, A. (2017) "A hybrid heuristic algorithm to solve capacitated location-routing problem with fuzzy demands", International Journal of Industrial Mathematics, Vol. 9, No. 1, pp. 1-20.
24
-Nadizadeh, A., Sahraeian, R., Sabzevari Zadeh, A. and Homayouni, S. M. (2011) "Using greedy clustering method to solve capacitated location-routing problem", African Journal of Business Management, Vol. 5, No. 17, pp. 7499-7506.
25
-Nagy, G. and Salhi, S. (2007) "Location-routing: Issues, models and methods", European Journal of Operational Research, Vol. 177, pp. 649-672.
26
-Prins, C., Prodhon, C. and Wolfler Calvo, R. (2006) "Solving the capacitated location-routing problem by a GRASP complemented by a learning process and a path relinking", Operational Research Quarterly, Vol. 4, pp. 221-238.
27
-Prodhon, C. (2008) "http://prodhonc.free.fe/homepage".
28
-Prodhon, C. and Prins, C. (2014) "A survey of recent research on location-routing problems", European Journal of Operational Research, Vol. 238, No. 1, pp. 1-17.
29
-Rahmani, Y., Cherif-Khettaf, W. R. and Oulamara, A. (2015) "A local search approach for the two–echelon multi-products location–routing problem with pickup and delivery", IFAC-Papers On Line, Vol. 48, No. 3, pp. 193-199.
30
-Rahmani, Y., Ramdane Cherif-Khettaf, W. and Oulamara, A. (2016) "The two-echelon multi-products location-routing problem with pickup and delivery: formulation and heuristic approaches", International Journal of Production Research, Vol. 54, No. 4, pp. 999-1019.
31
-Rath, S. and Gutjahr, W. J. (2014) "A math-heuristic for the warehouse location–routing problem in disaster relief", Computers & Operations Research, Vol. 42, pp. 25–39.
32
-Salhi, S. and Nagy, G. (1999) "Consistency and robustness in location routing", Studies in Locational Analysis, Vol. 13, pp. 3-19.
33
-Salhi, S. and Rand, G. K. (1989) "The effect of ignoring routes when locating depots", European Journal of Operational Research, Vol. 39, pp. 150–156.
34
-Tavakkoli-Moghaddam, R., Razie, Z. and Tabrizian, S. (2016) "Solving a Bi-Objective Multi-Product Vehicle Routing Problem with Heterogeneous Fleets under an Uncertainty Condition", International Journal of Transportation Engineering, Vol. 3, No. 3, pp. 207-225.
35
-Wang, X. and Li, X. (2017) "Carbon reduction in the location routing problem with heterogeneous fleet, simultaneous pickup-delivery and time windows", Procedia Computer Science, Vol. 112, pp. 1131-1140.
36
-Webb, M. H. J. (1968) "Cost functions in the location of depots for multiple delivery journeys", Operational Research Quarterly, Vol. 19, No. 3, pp. 311-320.
37
-Yang, J. and Sun, H. (2015) "Battery swap station location-routing problem with capacitated electric vehicles", Computers & Operations Research, Vol. 55, pp. 217-232.
38
-Yu, V. F. and Lin, S.-W. (2014) "Multi-start simulated annealing heuristic for the location routing problem with simultaneous pickup and delivery", Applied Soft Computing, Vol. 24, pp. 284-290.
39
-Yu, V. F. and Lin, S.-Y. (2016) "Solving the location-routing problem with simultaneous pickup and delivery by simulated annealing", International Journal of Production Research, Vol. 54, No. 2, pp. 526-549.
40
-Zarandi, M., Hemmati, A., Davari S. and Turksen, I. (2013) "Capacitated location-routing problem with time windows under uncertainty", Knowledge-Based Systems, Vol. 37, pp. 480–489.
41
-Zarandi, M. H. F., Hemmati, A. and Davari, S. (2011) "The multi-depot capacitated location-routing problem with fuzzy travel times", Expert Systems with Applications, Vol. 38, No. 8, pp. 10075-10084.
42
-Zare Mehrjerdi, Y. and Nadizadeh, A. (2013) "Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands." European Journal of Operational Research, Vol. 229, No. 1, pp. 75-84.
43
-Zare Mehrjerdi, Y. and Nadizadeh, A. (2016) "Heuristic Method to Solve Capacitated Location-Routing Problem with Fuzzy Demands", International Journal of Industiral Engineering & Producion Research, Vol. 27, No. 1, pp. 1-19.
44
*Zhao, J. and Verter, V. (2014) "A bi-objective model for the used oil location-routing problem", Computers & Operations Research, Vol. 62, pp. 157-168.
45
ORIGINAL_ARTICLE
Evolutionary Approach for Energy Minimizing Vehicle Routing Problem with Time Windows and Customers’ Priority
A new model and solution for the energy minimizing vehicle routing problem with time windows (EVRPTW) and customers’ priority is presented in this paper. In this paper unlike prior attempts to minimize cost by minimizing overall traveling distance, the model also incorporates energy minimizing which meets the latest requirements of green logistics. This paper includes the vehicles load as an additional indicator of the cost in addition to the distance traveled cost. Moreover, this paper tries to maximize the customers' satisfaction using their preference and considers the customers' priority for servicing. Every customer is assigned to a group (e.g., very important, important, casual and unimportant) and the customers’ preference is represented as a convex fuzzy number with respect to the satisfaction for service time. The detailed mathematical formulation of proposed model is provided and it is interpreted as multi-objective optimization where, the energy consumed and the total number of vehicles are minimized and the total satisfaction rate of customers is maximized. In general, the relationship between these defined objectives is unknown until the problem is solved in a proper multi-objective manner. Thus, a multi-objective evolutionary algorithm is proposed and its performance on several completely random instances is compared with Non-dominated Sorting Genetic Algorithm II (NSGA II) and CPLEX Solver. The hypervolume indicator is used to evaluate the two Pareto set approximations found by NSGA-II and the proposed approach. The performance proposed evolutionary is further demonstrated through several computational experiments and the results indicate the good quality of the method.
http://www.ijte.ir/article_55929_94f268c282ebd00baeb96a949d6588f1.pdf
2019-01-01
237
264
10.22119/ijte.2018.55929
vehicle routing problem
energy consumption
Customers' priority
multi-objective
Evolutionary Algorithm
Seyed Farid
Ghannadpour
ghannadpour@iust.ac.ir
1
Assistant Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
LEAD_AUTHOR
-Alinaghian, M. and Naderipour, M. (2016) "A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: A case study", Computers & Industrial Engineering, Vol. 99, pp.210–222.
1
-Bektaş, T. and Laporte, G. (2011) 'the pollution-routing problem", Transportation Research Part B: methodological, Vol. 45, No. 8, pp.1232-1250.
2
-Chiang, T. C. and Hsu, W. H. (2014) "A knowledge-based evolutionary algorithm for the multiobjective vehicle routing problem with time windows", Computers & Operations Research, Vol. 45, pp.25-37.
3
-Deb, K. (2002) "A fast and elitist multiobjective genetic algorithm: NSGA-II", IEEE Transaction on Evolutionary Computation, Vol. 6, No. 2, pp.182-197
4
-Derrac, J., Garcia, S., Molina, D., Herrera, F. (2011) "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms", Swarm and Evolutionary Computation, Vol. 1, No. 1, pp.3-18.
5
-Dondo, R., Cerda, J. (2007) "A cluster-based optimization approach for the multi-depot heterogeneous fleet vehicle routing problem with time windows", European Journal of Operational Research, Vol. 176, No. 3, pp. 1478-1507.
6
-Erbao, C., Mingyong, L. (2010) "The open vehicle routing problem with fuzzy demands", Expert Systems with Applications, Vol. 37, No. 3, pp.2405-2411.
7
-Euchi, J., Yassine, A., Chabchoub, H. (2015) "The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach", Swarm and Evolutionary Computation, Vol. 21, pp.41-53.
8
-Feng, H.M., Liao, K.L. (2014) "Hybrid evolutionary fuzzy learning scheme in the applications of traveling salesman problems", Information Sciences, Vol. 270, pp.204-225.
9
-Fernández, E., Roca-Riu, M., Speranza, M.G. (2018) "The Shared Customer Collaboration Vehicle Routing Problem", European Journal of Operational Research, Vol. 265, No. 3, pp. 1078-1093.
10
-Garcia-Najera, A., Bullinaria, J. A. and Gutiérrez-Andrade, M. A. (2015) "An evolutionary approach for multi-objective vehicle routing problems with backhauls", Computers & Industrial Engineering, Vol. 81, pp.90-108.
11
-Gaur, D. R., Mudgal, A. and Singh, R. R. (2013) "Routing vehicles to minimize fuel consumption", Operations Research Letters, Vol. 41, pp.576-580.
12
-Ghannadpour, S. F., Noori, S. and Tavakkoli-Moghaddam, R. (2014) "A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority", Journal of Combinatorial Optimization, Vol. 28, No. 2, pp.414-446.
13
-Ghoseiri, K. and Ghannadpour, S. F. (2010) "Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm", Applied Soft Computing, Vol. 10, No. 4, pp.1096-1107.
14
-Hosseini-Nasab, H. and Lotfalian, P. (2017) "Green routing for truck systems with classification of path types", Journal of Cleaner Production, No. 146, pp.228-233.
15
-Kara, I., Kara, B.Y. and Yetis, M. K. (2007) "Energy minimizing vehicle routing problem", Lecture Notes in Computer Science, Vol. 146, pp.62-71.
16
-Li, F., Golden, B. and Wasil, E. (2005) "Very large scale vehicle routing: new test problems algorithms and results", Computers and Operations Research, Vol. 32, No. 5, pp.1165-1179.
17
-Li, K., Kwong, S. and Deb, K. (2015) "A dual-population paradigm for evolutionary multiobjective optimization", Information Sciences, Vol. 309, pp.50-72.
18
-Lin, C., Choy, K. L., Ho, G. T. S., Chung, S.H. and Lam, H.Y. (2014) "Survey of green vehicle routing problem: past and future trends", Expert Systems with Applications, Vol. 41, No. 4, pp.1118 – 1138.
19
-Lqbal, S., Kaykobad, M. and Rahman, M. S. "Solving the multi-objective vehicle routing problem with soft time windows with the help of bees", Swarm and Evolutionary Computation, Vol. 24, pp.50–64.
20
-Montoya, A., Gueret, C., Mendoza, J. E. and Villegas, J. G. (2016) "A multi-space sampling heuristic for the green vehicle routing problem", Transportation Research Part C: Emerging Technologies, Vol. 70, pp.113-128.
21
-Nikkhah Qamsari, A. S. A., Hosseini Motlagh, S. M. and Jokar, A. (2017) "A two-phase hybrid heuristic method for a multi-depot inventory-routing problem", International Journal of Transportation Engineering, Vol. 4, No. 3, pp. 287-304
22
-Ombuki, B., Ross, B. and Hanshar, F. (2006) "Multi-objective genetic algorithm for vehicle routing problem with time windows", Applied Intelligence, Vol. 24, No. 1, pp.17-30.
23
-Pepin, A. S., Desaulniers, G., Herts, A. and Huisman, D. (2009) "A comparison of five heuristics for the multiple depot vehicle scheduling problem", Journal of Scheduling, Vol. 39, pp.17-30.
24
-Qian, J. and Eglese, R. (2016) "Fuel emissions optimization in vehicle routing problems with time-varying speeds", European Journal of Operational Research, Vol. 248, No. 3, pp.840-848.
25
-Shim, V. A., Tan, K. C. and Tang, H. (2015) "adaptive memetic computing for evolutionary multiobjective optimization", IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 610-621.
26
-Sivaram Kumar, V., Thansekhar, M. R., Sarvanan, R. and Miruna Joe Amali, S. (2014) "Solving multi – objective vehicle routing problem with time windows by FAGA", Procedia Engineering, Vol. 97, pp.2176-2185.
27
-Solomon, M. M. (1987) "Algorithms for the vehicle routing and scheduling problems with time window constraints", Operations Research, Vol. 35, pp.254–265.
28
-Tan, K. C., Cheong, C.Y. and Goh, C. K. (2007) "Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation", European Journal of Operational Research, Vol. 177, No. 2, pp.813-139.
29
-Tan, K. C., Chew, Y. H. and Lee, L. H. (2006) "A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows", Computational Optimization and Applications, Vol. 34, pp.115-151.
30
-Tanga, J., Pan, Z.H., Fung, R.Y.K. and Lau, H. (2009) "Vehicle routing problem with fuzzy time windows", Fuzzy Sets and Systems, Vol. 160, pp.683–695.
31
-Tavares, G., Zsigraiova, Z., Semiao, V. and Carvalho, M. G. (2008) "A case study of fuel savings through optimisation of MSW transportation routes", Management of Environmental Quality: An International Journal, Vol. 19, No. 4, pp. 444 – 454.
32
-Xiao, Y., Zhao, Q., Kaku, I. and Xu, Y. (2012) "Development of fuel consumption optimization model for the capacitated vehicle routing problem", Computers & Operations Research, Vol. 39, No. 7, pp.1419-1431.
33
-Yu, V., Jewpanaya, P. and Perwira Redi, A. A. N. (2016) "Open vehicle routing problem with cross-docking", Computers & Industrial Engineering, Vol. 94, pp.6-17.
34
-Yu, V. F., Perwira Redi, A. A. N., Hidayat, Y. A., and Wibowo, O. J. (2017) "A simulated annealing heuristic for the hybrid vehicle routingproblem", Applied Soft Computing, Vol. 53, pp.119-132.
35
-Zhang, S., Lee, C. K. M., Choy, K. L., Ho, W. and Ip, W.H. (2014) "Design and development of a hybrid artificial bee colony algorithm for the environmental vehicle routing problem", Transportation Research Part D: Transport and Environment, Vol. 31, pp.85-99.
36
-Zhang, X., Tian, Y., Cheng, R. and Jin, Y. (2015) "An efficient approach to nondominated sorting for evolutionary multiobjective optimization", IEEE Transaction on Evolutionary Computation, Vol. 19, No. 2, pp. 201-213.
37
-Zhou, Y. and Wang, J. (2015) "A local search-based multiobjective optimization algorithm for multiobjective vehicle routing problem with time windows", IEEE Systems Journal, Vol. 9, No. 3, pp.1100-1113.
38
-Zhu, Q., Lin, Q., Du, Z., Liang, Z., Wang, W., Zhu, Z., Chen, J., Huang, P. and Ming, Z. (2016) "A novel adaptive hybrid crossover operator for multi objective evolutionary algorithm", Information Sciences, Vol. 345, pp.177-198.
39
-Zitzler, E., Knowles, J. and Thiele, L. (2008) "Quality assessment of pareto set approximations", In J. Branke et al. (Eds.) Multiobjective Optimization, pp.373-404, Springer-Verlag, Berline.
40
ORIGINAL_ARTICLE
Evaluation and Relocating Bicycle Sharing Stations in Mashhad City using Multi-Criteria Analysis
The purpose of this study is to evaluate the present status of Mashhad’s Bicycle Sharing (BS) Program's stations, and to locate future stations, taking into account the 7 criteria of “proximity to subway stations”, “proximity to other stations”, “distance from important intersections”, “distance from population centers”, “proximity to educational, recreational and commercialcenters”, “slope level” and “proximity to cycling infrastructure (bike lanes)”. The approach employed by the present study is Multiple-criteria decision making (MCDM) and Fuzzy membership maps and Analytic Hierarchy Process (AHP) based on GIS to weight the 7 mentioned criteria, also the stations will be ranked based on VIKOR approach and finally categorized through Jenks natural breaks classification method (JENKS). In order to analyze the data ArcGIS 10 software has been used. The findings show that 26 stations (20.3%) are very unsatisfactory and 25 stations (19.5%) are unsatisfactory among the total 128 stations that have been built so far. The findings also indicate that there are a lot of stations with very unsatisfactory conditions on the borders of the coverage area of the BS program which imply that widespread coverage has been prioritized over efficiency and proper distribution of the stations. Also 22 planned stations that have been stipulated in the contract between Mashhad’s municipality and the beneficiary firm have been located based on the ratings that were assigned. This study, as the first study with the mentioned approach on this subject in Iran shows that priorities regarding the performance of BS program may not be well conceived in different regions and cities, especially in developing countries with their own specific conditions. Thus, in this research we have tried to present the existing problems in locating the stations, and contribute to development of the existing programs and possible future programs in other cities.
http://www.ijte.ir/article_55932_33d8d976e58892399d60053585de8766.pdf
2019-01-01
265
283
10.22119/ijte.2018.96377.1365
Shared bicycles
relocating stations
GIS
VIKOR
Mashhad City
Danial
Jahanshahi
danial.jahanshahi@gmail.com
1
MA. Grad., Department of Urban Management, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Masoud
Minaei
m.minaei@um.ac.ir
2
Assistant Professor, Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
Omid Ali
Kharazmi
kharazmi@um.ac.ir
3
Assistant Professor, Department of Urban Management, Ferdowsi University of Mashhad, Mashhad, Iran
AUTHOR
Foad
Minaei
minaiy.gis@gmail.com
4
MSc. in Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran
AUTHOR
- Asgharpour, M. J. (2004) "Multiple criteria decision making", Tehran University Publications.
1
- Ahmad, S. and De Oliveira, J. (2016) "Determinants of urban mobility in India: Lessons for promoting sustainable and inclusive urban transportation in developing countries", Transport policy, Vol. 50, pp.106-114.
2
- Basofi, A., Fariza, A., Ahsan, A. S. and Kamal, I. M (2015) "A comparison between natural and Head/tail breaks in LSI (Landslide Susceptibility Index) classification for landslide susceptibility mapping: A case study in Ponorogo, East Java, Indonesia", International Conference on Science in Information Technology (ICSITech), Yogyakarta, pp. 337-342.
3
- Bellman, R. E. and Zadeh, L. A (1970) "Decision-making in a fuzzy environment", Management Science, Vol. 17, No. 4, pp. 141–64.
4
- Bernatchez, A. C., Gauvin, L., Fuller, D. and Dubé, A. S., Drouin, L. (2015) "Knowing about a public bicycle share program in Montreal, Canada: Are diffusion of innovation and proximity enough for equitable awareness?” Journal of Transport & Health, Vol. 2, pp. 360-368.
5
- Cheng, C. H. and Mon, D. L (1994) "Evaluation weapon system by AHP based on fuzzy scales", Fuzzy Sets and Systems, Vol. 63, pp. 1–10.
6
- DeMaio, P. (2009) "Bike-sharing: history, impacts, models of provision, & future", Journal of Public Transport, Vol. 12, No. 4, pp. 41–56.
7
- Faraji Sabokbar, H. (2005) "Locating commercial service units using AHP method", Geographical Studies, Vol. 51, pp. 125-138.
8
- Fishman, E., Washington, S. and Haworth, N. (2013) "Bike share: a synthesis of the literature", Transport Review, Vol. 33, No. 2, pp. 148-165.
9
- Fishman, E., Washington, S., Haworth, N. and Mazzei, A. (2014) "Barriers to bike sharing: an analysis from Melbourne and Brisbane", Journal of Transport geography, Vol. 41, pp. 325-337.
10
- García-Palomares, J. C., Gutiérrez, J. and Latorre, M. (2012) "Optimizing the location of stations in bike-sharing programs: A GIS approach", Applied Geography, Vol. 35, No. 1, pp. 235-246.
11
- Gili, A., Álvarez, C., Bagnato, R. and Noellemeyer, E. (2017) "Comparison of three methods for delineating management zones for site-specific crop management, Computers and Electronics in Agriculture", Vol. 139, No. 15, pp. 213-223.
12
- Gupta, A., Bargar, A., Gupta, S. and Ma, D. (2014) "Interactive visual analytics for multi-city bikeshare data analysis", the 3rd International Workshop on Urban Computing (URBCOMP 2014), New York.
13
- Hwang, C. L. (1995) "Multiple attribute decision making: an introduction", SAGE, pp. 3-36.
14
- Herath, G. (2004) "Incorporating community objectives in improved wetland management: the use of the analytic hierarchy process", Journal of Environmental Management, Vol. 70, pp. 263–273.
15
- Institute for Transportation and Development Policy (2013) “the bike-share planning guide", New York: ITDP.
16
- Jahanshahi, D., Kharazmi, O. and Ajza Shokouhi, M. (2018) "How barriers and motivators can affect Mashhad citizens' usage of bicycle sharing system: A qualitative approach", Studies of Architecture, Urbanism and Environmental Sciences Journal, Vol. 1, No. 1, pp. 29-38.
17
- Javadi, Q. and Aqa Muhammadi, M. (2014) "Spatial planning of public bs program using gis & multi criteria decision Making", Journal of Science and Technologies of Surveying Engineering. Vol. 3, No. 4, pp. 53-64.
18
- Jenks, G. F. (1967) "The data model concept in statistical mapping", International Yearbook of Cartography, Vol. 7, pp. 186–190.
19
- Jill, C. and Roger, H. (2013) "Business research: A practical guide for undergraduate and postgraduate students (Revised ed.)", Palgrave Macmillan, ISBN 9781137037480, Retrieved 2016-05-02.
20
- Karki, T. K. and Tao, L. (2016) "How accessible and convenient are the public bicycle sharing programs in china? Experiences from Suzhou city", Habitat International, Vol. 53, pp. 188-194.
21
- Khakpour, B. and Bavanpouri, A. (2009) "An analysis on imbalanced development of mashhad", The Journal of Knowledge and Development, Vol. 27, pp. 182-202.
22
- Khalili, M. and Haydari Nouri, P. (2014) "Relocating bicycle sharing stations to increase accessibility and quality of non-motor transportation using GIS and presenting encouraging approaches (Case Study: District 8 of Tehran)", 14th International Conference of Transportation and Traffic, Tehran.
23
- Khan, F. (2012) "An initial seed selection algorithm for k-means clustering of georeferenced data to improve replicability of cluster assignments for mapping application", Applied Soft Computing, Vol. 12, No. 11, pp. 3698-3700.
24
- Lee, AR (1995) "Application of modified fuzzy AHP method to analyze bolting sequence of structural joints", UMI Dissertation Service, A Bell & Howell Company.
25
- Li, H. X., Chen, L. P. and Hung, H. P. (2001) "Fuzzy neural intelligent systems: mathematical fundamentals and application in engineering". CRC Press. pp. 4-18.
26
- Marshall, C. and Gretchen B. R (1999) "Designing Qualitative Research" 3rd Ed. London: Sage Publications, pp. 115
27
- Midgley, P (2011) "Bicycle-sharing schemes: Enhancing sustainable mobility in urban areas", New York: United Nations department of economics and social affairs.
28
- Minaei, M (2009) "Implementation of Agricultural Planning Model Using Fuzzy Logic and Geographic Information System (GIS)", MSc. Thesis. Faculty of Geography, Tehran University.
29
- Minaei, M. and Kainz, W (2016) "Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran". ISPRS International Journal of Geo-Information, Vol. 5, No. 5, pp. 1-16.
30
- Moteallemi, A., Bina, B., Minaei, M. and Mortezaie, S (2017) "The evaluation of Noise Pollution at Samen district in Mashhad by means of Geographic Information System (GIS)", International Journal of Occupational Hygiene, Vol.9, No. 4, pp. 31-46.
31
- Naeimi, M., Alimoradi, Z., Razi., M. and Monajjem, S (2013) " Developing A Priority-Based Decision Making Mod To Evaluate Geometric Configuration Of Urban Interchanges", International Journal of Transportation Engineering, Vol. 1, No. 4, pp. 285-310.
32
- National Association of City Transit Officials "NACTO" (2015) "Walkable station spacing is key to successful, equitable bike share", New York: NACTO.
33
- Navabakhsh, M. and Bazrafshan, M. (2014) "Sustainable Urban Development in Shiraz in the Past 10 Years", Iranian Journal of Social Development, Vol. 3, pp. 49-69.
34
- Nikpour, A., Malekshahi, Q. and Rezqi Rami, F. (2015) "Evaluation of Sustainable Urban Development Indicators with an Emphasis on Equity in the Distribution of Services (Case study: City of Babol)", Urban Research and Planning Publication, Vol. 22, pp. 125-138.
35
- Opricovic, S. and Tzeng, G. H. (2004) "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS", Euro. J. Oper. Res., Vol. 156, No. 2, pp. 445–455.
36
- Opricovic, S. and Tzeng, G. H. (2007) "Extended VIKOR method in comparison with out ranking methods", Euro. J. Operational Rewsearch, Vol. 178, pp. 514–529.
37
- Randall, P., Brown, L., Deschaine, L., Dimarzio, J., Kaiser, G. and Vierow, J. (2004) "Application of the analytic hierarchy process to compare alternatives for the long-term management of surplus mercury", Journal of Environmental Management Vol. 71, pp. 35–43.
38
- Roland Berger Study (2015) April 23. Retrieved from http://www.rolandberger.com/press_releases/bike-sharing-4-0.html
39
- Saaty, T. L. (1980) "The analytic hierarchy process", New York: Wiley
40
- Shaheen, S. A., Guzman, S. and Zhang, H. (2010) "Bikesharing in Europe, the Americas, and Asia", Transportation Research Board, pp.159-167.
41
- Shaheen, S. A., Zhang, H., Martin, E. and Guzman, S. (2011) "China Hangzhou public bicycle. Understanding early adoption and behavioral response to bikesharing", Journal of Transportation Research Board, Vol. 2247, pp.33-41.
42
- Soghab Isfahani, I., Hakimi Khaki, A., Pakdel, P., Moqadam Husseini, S. A., Haydarzadeh, E. and Shakouri, J. (2013) "The paths taken and not taken", (Documentary of activities and Experiences of Mashhad’s Municipality 1989-2013). Mashhad: Ahange Qalam Publications.
43
- Soltani, A. and Shariati, S. (2013) "Examining the incentives and disincentives for the use of bicycles in urban transport (case study of Isfahan)", Journal of the Association of Iranian Architectural, Vol. 5, pp. 63-73.
44
- Tran, T. D., Ovtracht, N. and d'Arcier, B. F. (2015) "Modeling bike sharing system using built environment factors", CIRP, Vol. 30, pp. 293-298.
45
- Zadeh, L. A. (1965) "Fuzzy set", Information and Control, Vol. 8, No. 3, pp. 338–53.
46
- Zhu, A. X., Wang, R., Qiao, J., Qin, C. Z., Chen, Y., Liu, J., Du, F., Lin, Y. and Zhu, T. (2014) "An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic", Geomorphology. Vol. 214, pp. 128-138
47
ORIGINAL_ARTICLE
Determination of Effective Travel Variables on Air Transport Demand with Using Structural Equation
Air transport system has always been involved in all aspects of life because of its high potential in transporting passengers and goods. In this research we surveys the effects of travel variables on demand of domestic air transport, and for gathering required information from passengers, a questionnaire was designed including 20 effective parameters on air transport demand with questions These parameters were investigated based on their importance using “Factor Analysis” and finally proved that the factors “price paid by passengers”, ”services offered by air transport system” and “time” has had the greatest impact on air transport demand with more than 20 % influence. “Structural Equation Modeling” has been used for checking the “Factor Analysis” results. The results of the model proved the correctness of factor analysis. Furthermore, the result of factor analysis has showed that the most important parameter has been “low cost travel” with the factor load of 0.9-1 in all case studies. Results show that both groups with factor load of more than 0.5 in the obtained factors have identified the factor “services” as the important and effective factor with the influence value of more than 20%.
http://www.ijte.ir/article_52976_4abf8d8305d6566d051bc5346d66ce6f.pdf
2019-01-01
285
303
10.22119/ijte.2017.52976
Air transport
transport demand
Factor analysis
Structural Equation
travel variable
Mojtaba
Moradi
mmoradi822@gmail.com
1
MSc. Grad., Department of Civil Engineering, Yazd University, Yazd, Iran
AUTHOR
Mohammad Mehdi
Khabiri
mkhabiri@yazd.ac.ir
2
Associate Professor, Department of Civil Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
Mohammad Saber
Fallah Nezhad
fallahnezhad@yazd.ac.ir
3
Associate Professor, College of Engineering, Yazd University, Yazd, Iran
AUTHOR
-Anand, S. (2014) "Application of factor analysis to k-means clustering algorithm on transportation data", International Journal of Computer Applications, No. 95, pp. 40-46.
1
-Barnhart, C. and Fearing, D. (2012) "Demand and capacity management in air transportation", The Association of European Operational Research Societies No. 1, pp.135-155.
2
-Bass, P., Donoso, P. and Munizaga, M. (2011) "A model to assess public transport demand stability", Transportation Research Part A 1; No.45, pp.755-764.
3
-Bieger, T., Wittmer, A. and Laesser, C. (2006) "What is driving the continued growth in demand for air travel, Customer value of air transport?", Journal of Air Transport Management No.13, pp. 31-36.
4
-Beria, P. and Laurino, A. (2016) " Determinants of daily fluctuations in air passenger volumes. The effect of events and holidays on Milan Malpensa airport " Journal of Air Transport Management No. 53,pp. 73-84.
5
-Goedeking, P. (2010) "Assessing and comparing the strengths and weaknesses of aviation networks", Transportation Research Part A No.10, pp.119-125.
6
-Israel, G. (2013) “Determining Sample Size 1.”, University of Florida , p.1-5.
7
-Janic, M. (2015)"Reprint of ‘‘Modeling the resilience, friability and costs of an air transport network affected by a large-scale disruptive event’’. Transportation Research Part A, Vol.71, pp.77-92.
8
-Kopsch, F. (2011) "A demand model for domestic air travel in Sweden" Journal of Air Transport Management, No.20, pp.46-48.
9
-Lieshout, R, Malighetti P, Redondi R, Burghouwt G. (2015)"The competitive landscape of air transport in Europe" Journal of Transport Geography; Vol.50, pp.68-82.
10
-Liu, D. (2015)"Measuring aeronautical service efficiency and commercial service efficiency of East Asia airport companies: An application of Network Data Envelopment Analysis" Journal of Air Transport Management; No.52, pp.11-22.
11
-Lupo, T. (2015) “Fuzzy ServPerf model combined with ELECTRE III to comparatively evaluate service quality of international airports in Sicily”, Journal of Air Transport Management Vol. 30, pp.1-11.
12
-Rolim, P., Bettini, H. and Oliveira, A. (2016)" Estimating the impact of airport privatization on airline demand: A regression-based event study", Journal of Air Transport Management No. 54, pp.31-41.
13
-Saffarzadeh, M. and NaserAlavi, S. S. (2009) "Airline schedule planning using heuristic search method", Journal of Transportation Engineering , Volume 6 , Number 2 (19); Pp.175 -184.
14
-Scarpel, R. (2014) "A demand trend change early warning forecast model for the city of São Paulo multi-airport system”, Transportation Research Part A, No.65, pp.23-32.
15
-Scotti, D. and Dresner, M. (2015) "The impact of baggage fees on passenger demand on US air routes", Transport Policy, Vol. 43, pp.4-10.
16
-Sherry, L. (2014) "A method for quantifying travel productivity for corporate travel managers", Journal of Air Transport Management, No.42: pp.118-124.
17
-Xiao, Y, Fu, X. and Zhang, A. (2013) "Demand uncertainty and airport capacity choice", Transportation Research Part B Vol. 57, pp.91-104.
18
-Yang, H. and Fu, X. (2015)"A comparison of price-cap and light-handed airport regulation with demand uncertainty", Transportation Research Part B, Vol.73, pp.122-132.
19