Open Journal Systems


Cengiz Kahraman, Eda Boltürk, Sezi Çevik Onar, Başar Öztayşi, Kerim Göztepe


Deploying warehouses at strategic locations becomes an important issue for humanitarian relief organizations in order to improve their relief aid capability and rescue plan. The delivery of sufficient technical equipment and provision of shelter and reinforcement to victims is a significant event during relief operations. Warehouse location selection in humanitarian logistics (HL) is a challenging process because choosing a non-optimal location may cause additional problems during rescue activities. The conventional decision making tools used for a warehouse location selection problem tend to be less effective in dealing with the imprecise or vague nature of the linguistic assessment. In many situations, the values of the qualitative attributes are often incompletely determined by the decision-makers. The fuzzy set theory can capture this type of uncertainty. In this paper, a recent extension of ordinary fuzzy sets, namely hesitant fuzzy sets, is used for considering the decision makers hesitancy in the evaluation. To solve the HL warehouse location selection problem, we propose a new hesitant fuzzy Analytic Hierarchy Process (AHP) method. We also present a HL warehouse location selection case study for a Turkish humanitarian relief organization by using hesitant fuzzy preference information.


Warehouse location selection; Multi-attribute decision-making (MADM); Fuzzy logic; Humanitarian logistics;, Hesitant Fuzzy Sets

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Ahmadi, M., Seifi, A., Tootooni, B. (2015). 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: Logistics and Transportation Review, 75, 145-163. doi:

Alberto, P. (2000). The logistics of industrial location decisions: an application of the analytic hierarchy process. International Journal of Logistics Research and Applications, 3(3), 273–289.

Ashrafzadeh, M., Rafiei, F. M., Isfahani, N. M., Zare, Z. (2012). Application of fuzzy TOPSIS method for the selection of Warehouse Location: A case study. Interdisciplinary Journal of Contemporary Research in Business, 3(9), 655-671.

Atkinson, M.A., Bayazit, O., Karpak, B. (2015). A case study using the Analytic Hierarchy Process for IT outsourcing decision making. International Journal of Information Systems and Supply Chain Management, 8(1), 60-84. doi: 10.4018/ijisscm.2015010104

Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101-121.

Barbarosoglu, G., Arda, Y., (2004). A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the Operational Research Society, 55(1), 43-53. doi:10.1057/palgrave.jors.2601652

Blocher, E., Chen, K. H., Lin, T. W. (2008). Cost management: A strategic emphasis. McGraw-Hill/Irwin.

Bottani, E., Rizzi, A. (2006). A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Management, 11(4), 294-308. doi:

Buckley, J.J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17, 233–247. doi:10.1016/0165-0114(85)90090-9

Campbell, A. M., Jones, P. C. (2011). Prepositioning supplies in preparation for disasters. European Journal of Operational Research, 209(2), 156-165. doi:

Cevik Onar S., Oztaysi B., Otay İ., Kahraman C. (2015). Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy, 90, 274-285. doi:

Chakravarty, A. K. (2014). Humanitarian relief chain: Rapid response under uncertainty. International Journal of Production Economics, 151, 146-157. doi:

Chanas, S., Kuchta, D. (1996). A concept of the optimal solution of the transportation problem with fuzzy cost coefficients. Fuzzy Sets and Systems, 82 (3), 299-305. doi:10.1016/0165-0114(95)00278-2

Chang, D.Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95, 649–655. doi:10.1016/0377-2217(95)00300-2

Choi, T. Y., Dooley, K. J., Rungtusanatham, M. (2001). Supply networks and complex adaptive systems: control versus emergence. Journal of Operations Management, 19(3), 351-366. doi:

Chou, J. S., Yang, K. H., Ren, T. C. (2015). Ex-post evaluation of preparedness education in disaster prevention, mitigation and response. International Journal of Disaster Risk Reduction, 12, 188-201. doi:

Cozzolino, A. (2012). Humanitarian logistics and supply chain management. In Humanitarian Logistics (pp. 5-16). Springer Berlin Heidelberg. doi: 10.1007/978-3-642-30186-5_2

Dangol, R., Bahl, M., Karpak, B. (2015). Timing cooperative relationships with sequential capability development process to reduce capability development trade-offs. International Journal of Production Economics, 169, 179-189. doi:

Dekle, J., Lavieri, M.S., Martin, E., Emir-Farinas, H., Francis, R.L. (2005). A Florida country locates disaster recovery centres. Interfaces 35, 133–139.

Demirel, T., Demirel, N. Ç., Kahraman, C. (2010). Multi-attributes warehouse location selection using Choquet integral. Expert Systems with Applications, 37(5), 3943-3952. doi:

Díaz-Delgado, C., Gaytán Iniestra, J. (2014). Flood risk assessment in humanitarian logistics process design. Journal of Applied Research and Technology, 12(5), 976-984. doi:

Filev, D. and Yager, R.R. (1998). On the issue of obtaining OWA operator weights, Fuzzy Sets and Systems, 94(2), 157-169. doi:10.1016/S0165-0114(96)00254-0

Florez, J. V., Lauras, M., Okongwu, U., Dupont, L. (2015). A decision support system for robust humanitarian facility location. Engineering Applications of Artificial Intelligence, , 46, Part B, 326-335. doi:

Gabel, M. J. (1998). The endurance of supranational governance: A consociational interpretation of the European Union. Comparative Politics, 463-475. doi 10.2307/422334

Gralla, E., Goentzel, J., Chomilier, B. (2015). Case study of a humanitarian logistics simulation exercise and insights for training design, Journal of Humanitarian Logistics and Supply Chain Management, 5(1), 113-138.


Guha-Sapir, D., Hoyois, P., and Below, R., (2013). Annual Disaster Statistical Review 2013, The numbers and trends, 1-41.

Heo, E., Kim, J., Boo, K.-J. (2010). Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and Sustainable Energy Reviews, 14 (8), 2214-2220. doi:

Hsieh, C.H. and Chen, S.H. (1999). A model and algorithm of fuzzy product positioning. Information Sciences, 121, 61–82. doi:

Huang, S., Wang, Q., Batta, R., Nagi, R. (2015). An integrated model for site selection and space determination of warehouses. Computers & Operations Research, 62, 169-176. doi:

Ivgin, M. (2013). The decision-making models for relief asset management and interaction with disaster mitigation. International Journal of Disaster Risk Reduction, 5, 107-116. doi:

Kahraman C, Beskese A., Kaya I. (2010). Selection among ERP outsourcing alternatives using a fuzzy multi-criteria decision making methodology. International Journal of Production Research 48(2), 547-566. doi: 10.3233/IFS-151722

Kahraman C., Çevik Onar S., Öztayşi B. (2015). Engineering economic analyses using intuitionistic and hesitant fuzzy sets, Journal of Intelligent & Fuzzy Systems, 29(3), 1151-1168.

Kahraman, C., Ates, N. Y., Çevik, S., Gülbay, M. (2007). Fuzzy multi‐attribute cost–benefit analysis of e‐services. International Journal of Intelligent Systems, 22(5), 547-565. doi: 10.1002/int.20213

Kahraman, C., Öztayşi, B., Çevik Onar, S. (2016). A comprehensive literature review of 50 years of fuzzy set theory. International Journal of Computational Intelligence Systems 9(1), 3-24. doi:

Kashi, K., Franek, J. (2016). AHP in personnel management: Can the key competencies change with company’s strategy?. International Journal of Analytic Hierarchy Process, 8(1), 39-52. doi:

Kaya I., Öztayşi, B., Kahraman C. (2012). A two-phased fuzzy multicriteria selection among public transportation investments for policy-making and risk governance. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 20(31), 31-48. doi:

Leiras, A., de Brito, I., Jr., Queiroz Peres, E., Rejane Bertazzo, T., Tsugunobu Yoshida Yoshizaki, H. (2014). Literature review of humanitarian logistics research: trends and challenges. Journal of Humanitarian Logistics and Supply Chain Management, 4(1), 95-130.


L'Hermitte, C., Tatham, P., Bowles, M., Brooks, B. (2016). Developing organisational capabilities to support agility in humanitarian logistics: An exploratory study. Journal of Humanitarian Logistics and Supply Chain Management , 6(1), 72-99. doi :

Li, Y., Liu, X., Chen, Y. (2011). Selection of logistics centre location using axiomatic fuzzy set and TOPSIS methodology in logistics management. Expert Systems with Applications, 38, 7901–7908. doi:

Liu, H., Rodriguez, R.M. (2014). A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making. Information Sciences, 258, 220–238. doi:

Olazabal, M., Pascual, U. (2016). Use of fuzzy cognitive maps to study urban resilience and transformation. Environmental Innovation and Societal Transitions, 18, 18-40. doi:

Onut, S., Soner, S. (2007). Transhipment site election using the AHP and TOPSIS approaches under fuzzy environment. Waste Management, 28 (9), 1552–1559. doi:

Özcan, T., Çelebi, N., Esnaf, Ş. (2011). Comparative analysis of multi-attributes decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773-9779. doi:

Özdamar, L., Ertem, M.A. (2015). Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55-65. doi:

Oztaysi B., Cevik Onar S., Bolturk E., Kahraman C. (2015). Hesitant fuzzy Analytic Hierarchy Process. 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, 1-7. doi: 10.1109/FUZZ-IEEE.2015.7337948

Oztaysi B., Cevik Onar S., Kahraman C. (2016). Fuzzy multicriteria prioritization of Urban transformation projects for Istanbul. Journal of Intelligent & Fuzzy Systems, 30 (4), 2459-2474. doi: 10.3233/IFS-152016

Pazour, J. A., Carlo, H. J. (2015). Warehouse reshuffling: Insights and optimization. Transportation Research Part E: Logistics and Transportation Review, 73, 207-226.

Powers, R. (1989). Optimization models for logistics decisions. Journal of Business Logistics 10 (1), 106–121. doi:

Ransikarbum, K., Mason, S.J. (2016). Multiple-objective analysis of integrated relief supply and network restoration in humanitarian logistics operations. International Journal of Production Research, 54(1), 49-68. doi:

Rath, S., Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, 42, 25-39. doi:

Rawls, C.G., Turnquist, M.A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation Research. Part B, 44, 521–534. doi:

Rodriguez, R. M., Martinez, L., Torra, V., Xu, Z. S., Herrera, F. (2014). Hesitant Fuzzy Sets: State of the art and future directions. International Journal of Intelligent Systems, 29, 495–524. doi: 10.1002/int.21654

Rodríguez, R.M., Martínez, L. and Herrera F. (2012). Hesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 20, 109–119. doi: 10.1109/TFUZZ.2011.2170076

Roh, S. Y., Jang, H. M., Han, C. H. (2013). Warehouse location decision factors in humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 29(1), 103-120. doi:

Roh, S., Pettit, S., Harris, I., Beresford, A. (2015). The pre-positioning of warehouses at regional and local levels for a humanitarian relief organisation. International Journal of Production Economics, 170, Part B, 616-628. doi:

Russell, T. E. (2005). The humanitarian relief supply chain: analysis of the 2004 South East Asia earthquake and tsunami. Doctoral dissertation, Massachusetts Institute of Technology.

Saaty T.L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill. doi:

Sahoo, N.K., Mohanty, B.S., Tripathy, P.K. (2016). Fuzzy inventory model with exponential demand and time-varying deterioration. Global Journal of Pure and Applied Mathematics, 12(3), 2573–2589.

Sarkis, J., Sundarraj, R.P. (2002). Hub location at Digital Equipment Corporation: a comprehensive analysis of qualitative and quantitative factors. European Journal of Operational Research, 137, 336–347. doi:

Seaman, J. (1999). Malnutrition in emergencies: how can we do better and where do the responsibilities lie? Disasters, 23(4), 306–315. doi: 10.1111/1467-7717.00120

Shahriari, M. (2011). Mapping fuzzy approach in engineering economics. International Research Journal of Finance and Economics, 81, 6-12.

Shqair, M., Altarazi, S., Al-Shihabi, S. (2014). A statistical study employing agent-based modeling to estimate the effects of different warehouse parameters on the distance traveled in warehouses. Simulation Modelling Practice and Theory, 49, 122-135. doi:

Stock, J. R., Lambert, D. M. (2001). Strategic logistics management (Vol. 4). Boston, MA: McGraw-Hill/Irwin.

Tan, R.R., Aviso, K.B., Huelgas, A.P., Promentilla, M.A.B. (2014). Fuzzy AHP approach to selection problems inprocess engineering involving quantitative andqualitative aspects. Process Safety and Environmental Protection, 92, 467–475. doi:

Thomas, A., Kopczak, L. (2005). From logistics to supply chain management: The path forward in the humanitarian sector. White paper, San Francisco, CA: Fritz Institute.

Tofighi, S., Torabi, S.A., Mansouri, S.A. (2016). Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 250(1), 239-250. doi:

Tomasini, R. M., Van Wassenhove, L. N. (2009). From preparedness to partnerships: case study research on humanitarian logistics. International Transactions in Operational Research, 16(5), 549-559. doi: 10.1111/j.1475-3995.2009.00697.x

Torra, V. (2010). Hesitant fuzy sets. International Journal of Inteligent Systems, 25, 529-539.

Tuzkaya, U. R., Önüt, S. (2009). A holonic approach based integration methodology for transportation and warehousing functions of the supply network. Computers & Industrial Engineering, 56(2), 708-723. doi:

Ucal Sari, I., Oztaysi, B., Kahraman, C. (2013). Fuzzy Analytic Hierarchy Process using Type-2 Fuzzy Sets: An application to warehouse location selection, In Doumpos and Grigoroudis (Ed.), Multiattribute decision aid and artificial intelligence, (pp. 285-308). John Wiley & Sons. doi: 10.1002/9781118522516.ch12

Vaillancourt, A. (2016). A theoretical framework for consolidation in humanitarian logistics. Journal of Humanitarian Logistics and Supply Chain Management, 6(1), 2-23. doi:

van Laarhoven, P.J.M., Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11, 199–227. doi:10.1016/S0165-0114(83)80082-7

Van Wassenhove, L. N. (2006). Blackett memorial lecture. Humanitarian aid logistics: Supply chain management in high gear. Journal of the Operational Research Society, 57(5), 475–489.

Van Wassenhove, L. N., Pedraza Martinez, A. J. (2012). Using OR to adapt supply chain management best practices to humanitarian logistics. International Transactions in Operational Research, 19(1-2), 307-322.

Vega, D., Roussat, C. (2015). Humanitarian logistics: The role of logistics service providers. International Journal of Physical Distribution and Logistics Management, 45(4), 352-375. doi:

Vitoriano, B., Ortuño, M. T., Tirado, G., Montero, J. (2011). A multi-attributes optimization model for humanitarian aid distribution. Journal of Global Optimization, 51(2), 189-208. doi: 10.1007/s10898-010-9603-z

Xia, M.M., Xu, Z.S.(2011) Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning, 52, 395–407. doi:10.1016/j.ijar.2010.09.002

Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338-353.


Zhang, N., Wei, G. (2013). Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 37, 4938–4947. doi: