References
1. Torre, E.L., Dolinskaya, I., and Smilowitz, K.R. Disaster
relief routing: Integrating research and practice",
Socio-Economic Planning Sciences, 46(1), pp. 88-97
(2012).
2. http: www.emdat.be/Database/consult the Database
3. Rezaei-Malek, M. and Tavakkoli-Moghaddam, R. Robust
humanitarian relief logistics network planning",
Uncertain Supply Chain Management, 2(2), pp. 73-96
(2014).
4. Hu, Z.H. A container multimodal transportation
scheduling approach based on immune anity model
for emergency relief", Expert Systems with Applications,
38(3), pp. 2632-2639 (2011).
5. Ahmadi, M., Sei, A., and Tootooni, B. A humanitarian
logistics model for disaster relief operation
considering network failure and standard relief time: A
case study on San Francisco district", Transportation
Research Part E, 75, pp. 145-163 (2015).
6. Zheng, Y.J., Chen, S.Y., and Ling, H.F. Evolutionary
optimization for disaster relief operations: A survey",
Applied Soft Computing Journal, 27, pp. 553-566
(2015).
7. Naja, M. and Eshghi, K. Logistics activities management
in the earthquake response phase: A robust
optimization approach", Proceedings of the 2012 International
Conference on Industrial Engineering and
Operations Management, Istanbul, Turkey (2012).
8. Adelzadeh, M., Mahdavi Asl, V., and Koosha, M. A
mathematical model and a solving procedure for multidepot
vehicle routing problem with fuzzy time window
and heterogeneous vehicle", Int. J .Adv. Manuf. Technol,
75(5), pp. 793-802 (2014).
9. Kuo, Y. and Wang, Ch. A variable neighborhood
search for the multi-depot vehicle routing problem with
loading cost", Expert Systems with Applications, 39(8),
pp. 6949-6954 (2012).
10. Ramos, T., Gomes, M., and Barbosa, A. Economic
and environmental concerns in planning recyclable
waste collection systems", Transportation Research
Part E, 62, pp. 34-54 (2014).
11. Nasiri, M. and ShisheGar, Sh. Disaster relief routing
by considering heterogeneous vehicles and reliability of
routes using an MADM approach", Uncertain Supply
Chain Management, 2(3), pp. 137-150 (2014).
12. Saadatseresht, M., Mansourian, A., and Taleai, M.
Evacuation planning using multiobjective evolutionary
optimization approach", European Journal of Operational
Research, 198(1), pp. 305-314 (2009).
13. Afshar-Nadja, B. and Razmi-Farooji, A. A comparison
of NSGA II and MOSA for solving multi-depots
time-dependent vehicle routing problem with heterogeneous
eet", Journal of Optimization in Industrial
Engineering, 7(16), pp. 65-73 (2014).
14. Simic, D., Kovacevic, I., Svircevic, V., and Simic, S.
Hybrid re
y model in routing heterogeneous
eet
of vehicles in logistics distribution", Logic. Jnl. IGPL,
23(3), pp. 521-532 (2015).
15. Zheng, Y., Hai-Feng, L., Sheng-Yong, C., and Jin-Yun,
X. A hybrid neuro-fuzzy network based on dierential
biogeography-based optimization for online population
classication in earthquakes", IEEE Transactions on
Fuzzy Systems, 23(4), pp. 1070-1083 (2015).
16. Knott, R. The logistics of bulk relief supplies" Disasters,
11(2), pp. 113-115 (1987).
17. Knott, R. Vehicle scheduling for emergency relief
management: A knowledge-based approach", Disasters,
Wiley Online Library, 12(4), pp. 285-293 (1988).
18. Barbarosoglu, G. An interactive approach for hierarchical
analysis of helicopter logistics in disaster
relief operations", European Journal of Operational
Research, 140(1), pp. 118-133 (2002).
19. Barbarosoglu, G. A two-stage stochastic programming
framework for transportation planning in disaster
response", Journal of the Operational Research Society,
55, pp. 43-53 (2004).
20. Haghani, A. and OH, S. Formulation and solution of
a multi commodity multi modal network
ow model
for disaster relief operations", Tronspn Res-A, 30(3)
pp. 231-250 (1996).
21. OH, S. and Haghani, A. Testing and evaluation of
a multi-commodity multi-modal network
ow model
for disaster relief management", Journal of Advanced
Transportation, 31(3), pp. 249-282 (1997).
22. Yi, W. and Ozdamar, L. A dynamic logistics coordination
model for evacuation and support in disaster
response activities", European Journal of Operational
Research, 179(3), pp. 1177-1193 (2007).
23. Ozdamar, L., Ekinci, E., and Kucukyazici, B. Emergency
logistics planning in natural disasters", Annals
of Operations Research, 129(1), pp. 217-245 (2004).
24. Rennemo, S., Fougner, K.Ro., Hvattum, L., and
Tirado, G. A three-stage stochastic facility routing
model for disaster response planning", Transportation
Research Part E, 62, pp. 116-135 (2014).
25. Hu, Z.H. A container multimodal transportation
scheduling approach based on immune anity model
for emergency relief", Expert Systems with Applications,
38(3), pp. 2632-2639 (2011).
26. Naja, M., Eshghi, K., and Dullaert, W. A multiobjective
robust optimization model for logistics planning
in the earthquake response phase", Transportation
Research Part E, 49(1), pp. 217-249 (2013).
27. Adivar, B. and Mert, A. International disaster relief
planning with fuzzy credibility", Fuzzy Optim Decis
Making, 9(4), pp. 413-433 (2010).
28. Ozdamar, L. Emergency logistics planning in natural
disasters", Annals of Operations Research, 129(1), pp.
217-245 (2004).
29. Goli, A. and Alinaghian, M. Location and multidepot
vehicle routing for emergency vehicles using
tour coverage and random sampling", Decision Science
Letters, 4(4), pp. 579-592 (2015).
Z. Gharib et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 2312{2330 2329
30. Talarico, L., Meisel, F., and Sorensen, K. Ambulance
routing for disaster response with patient groups",
Computers & Operations Research, 56, pp. 120-133
(2015).
31. Vitoriano, B., Ortu~no, M.T., Tirado, G., and Montero,
J. A multi-criteria optimization model for humanitarian
aid distribution", J. Glob. Optim, 51(2), pp. 189-
208 (2011).
32. Hamedi, M., Haghani, A., and Yang, S. Reliable
transportation of humanitarian supplies in disaster
response: model and heuristic", Procedia, Social and
Behavioral Sciences, 54, pp. 1205-1219 (2012).
33. Dondo, R. and Cerda, J. A cluster-based optimization
approach for the multi-depot heterogeneous
eet
vehicle routing problem with time windows", European
Journal of Operational Research, 176(3), pp. 1478-
1507 (2007).
34. He, R., Xu, W., Sun, J., and Zu, B. Balanced
K-means algorithm for partitioning areas in
large-scale vehicle routing problem", Third International
Symposium on Intelligent Information Technology
Application, IEEE computer society (2009). DOI
10.1109/IITA.2009.307
35. Dehnavi, A., Aghdam, I.N., Pradhan, B., and Varzandeh,
M.H.M. A new hybrid model using step-wise
weight assessment ratio analysis (SWARA) technique
and adaptive neuro-fuzzy inference system (ANFIS)
for regional landslide hazard assessment in Iran",
Catena, 135, pp. 122-148 (2015).
36. Zheng, Y.J., Ling, H.F., Chen, S.Y., and Xue, J.Y.
A hybrid neuro-fuzzy network based on dierential
biogeography-based optimization for online population
classication in earthquakes", Ieee Transactions on
Fuzzy Systems, 23(4), pp. 1070-1083 (2015).
37. Zheng, Y.J., Ling, H.F., Xue, J.Y., and Chen, S.Y.
Population classication in re evacuation: a multiobjective
particle swarm optimization approach",
IEEE Transactions on Evolutionary Computation,
18(1), pp. 70-81 (2014).
38. Rath, S. and Gutjahr, W.J. A math-heuristic for
the warehouse location-routing problem in disaster
relief", Computers & Operations Research, 42, pp. 25-
39 (2014).
39. Turkmen, I. Ecient impulse noise detection method
with ANFIS for accurate image restoration", Int. J.
Electron. Commun. (AEU), 65(2), pp. 132-139 (2011).
40. Wei, M., Bai, B., Sung, A.H., Liu, Q., Wang, J.,
and Cather, M.E. Predicting injection proles using
ANFIS", Information Sciences, 177(20), pp. 4445-
4461 (2007).
41. Duda, R. and Hart, P., Pattern Classication and
Scene Analysis, John Wiley & Sons, 3, pp. 1-35 (1973).
42. Dunn, J.C. A fuzzy relative of the ISO DATA process
and its use in detecting compact well separated
clusters", Journ. Cybern, 3(3), pp. 95-104 (1974).
43. Jang, J.S.R., Sun, C.T., and Mizutani, E., Neuro-
Fuzzy and Soft Computing: A Computational Approach
to Learning and Machine Intelligence, N.J.,
Prentice-Hall, Upper Saddle River (1997).
44. Jang, J.S.R. ANFIS: Adaptive network based fuzzy
inference system", IEEE Trans. Sys, Man and Cybernetics,
23(3), pp. 665-685 (1993).
45. Iida, Y. Basic concepts and future directions of
road network reliability analysis", Journal of Advanced
Transportation, 33(2), pp. 125-134 (1999).
46. Safari, H.A., Faghih, M., and Fathi, R. Integration of
graph theory and matrix approach with fuzzy AHP for
equipment selection", Journal of Industrial Engineering
and Management, 6(2), pp. 477-494 (2013).
47. Jangra, K., Grover, S., Chan, F.T., and Aggarwal,
S. A Digraph and matrix method to evaluate the
machinability of tungsten carbide composite with wire
EDM", Int. J .Adv. Manuf. Technol, 56(9), pp. 959-
974 (2011).
48. Mohaghar, A., Sadat Fagheyi, M., Moradi-Moghadam,
M., and Sadat Ahangari, S. Integration of fuzzy
GTMA and logarithmic fuzzy preference programming
for supplier selection", Report and Opinion, 5(5), pp.
9-16 (2013).
49. Rao, R.V. and Padmanabhan, K.K. Rapid prototyping
process selection using graph theory and matrix
approach", J. Mater. Process. Technol., 194(1-3), pp.
81-88 (2007).
50. Baykasoglu, A. A review and analysis of graph
theoretical-matrix permanent approach to decision
making with example applications", Articial Intelligence
Review, 42(4), pp. 573-605 (2014).
51. Rao, R.V. and Gandhi, O.P. Digraph and matrix
methods for the machinability evaluation of work
materials", Int. J. Mach. Tools .Manuf, 42(3), pp. 321-
330 (2002).
52. Shanmugam, G., Ganesan, P., and Vanathi, P.T.
Meta heuristic algorithms for vehicle routing problem
with stochastic demands", Journal of Computer
Science, 7(4), pp. 533-542 (2011).
53. Mavrotas, G. Eective implementation of the "-
constraint method in multi-objective mathematical
programming problems", Applied Mathematics and
Computation, 213(2), pp. 455-465 (2009).
54. Deb, K., Pratap, A., Agarwal, S., and Meyarivan,
T. A fast elitist multiobjective genetic algorithm:
NSGA-II", IEEE Transactions on Evolutionary Computation,
6(2), pp. 182-197 (2002).
55. Baccouche, M., Boukachour, J., Benabdelhad, A.,
and Benaissa, M. Scheduling aircraft landing: Hybrid
genetic algorithm approach", Vth International Meeting
for Research in Logistics, Sur CD-ROM, August
Fortaleza, Ceara, Brazil, 26 August (2004).
56. Mokhtarimousavi, S., Rahami, H., and Kaveh, A.
Multi- objective mathematical modeling of aircraft
landing problem on a runway in static mode scheduling
2330 Z. Gharib et al./Scientia Iranica, Transactions E: Industrial Engineering 25 (2018) 2312{2330
and sequence determination using NSGA-II", Int. J.
Optim. Civil Eng., 5(1), pp. 21-36 (2015).
57. Fister, I., Yang, X.S., and Fister Jr, I. Fire
y
algorithm: A brief review of the expanding literature",
Cuckoo Search and Fire
y Algorithm, 516, pp. 347-360
(2013).
58. Fister Jr, I., Perc, M.S., Kamal, M., and Fister, I. A
review of chaos-based re
y algorithms: Perspectives
and research challenges", Applied Mathematics and
Computation, 252, pp. 155-165 (2015).
59. Bacanin, N. and Tuba, M., Fire
y Algorithm for
Cardinality Constrained Mean-Variance Portfolio Optimization
Problem with Entropy Diversity Constraint,
The Scientic World Journal, p. 16 (2014).
60. Yang, Xin-She. Fire
y algorithms for multimodal
optimization", in Stochastic Algorithms: Foundations
and Applications, Springer, pp. 169-178 (2009).
61. Schott, J.R. Fault tolerant design using single and
multicriteria genetic algorithm optimization" (No.
AFIT/CI/CIA-95-039), Air Force Inst Of TechWright-
Patterson AFB OH, Master of Science Thesis (1995).
62. Zitzler, E., Evolutionary Algorithms for Multiobjective
Optimization: Methods and Applications, Ithaca:
Shaker, 63 (1999).