MULTIATTRIBUTE WAREHOUSE LOCATION SELECTION IN HUMANITARIAN LOGISTICS USING HESITANT FUZZY AHP
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Published
Sep 19, 2016
Cengiz Kahraman
Eda Boltürk
Sezi Çevik Onar
Ba?ar Öztay?i
Kerim Göztepe
Abstract
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.
How to Cite
Kahraman, C., Boltürk, E., Çevik Onar, S., Öztay?i, B., & Göztepe, K. (2016). MULTIATTRIBUTE WAREHOUSE LOCATION SELECTION IN HUMANITARIAN LOGISTICS USING HESITANT FUZZY AHP. International Journal of the Analytic Hierarchy Process, 8(2). https://doi.org/10.13033/ijahp.v8i2.387
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Keywords
Warehouse location selection, Multi-attribute decision-making (MADM), Fuzzy logic, Humanitarian logistics, , Hesitant Fuzzy Sets
References
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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: http://dx.doi.org/10.1016/j.eswa.2009.11.022
DÃaz-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: http://dx.doi.org/10.1016/S1665-6423(14)70604-2
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: http://dx.doi.org/10.1016/j.engappai.2015.06.020
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.
doi: http://dx.doi.org/10.1108/JHLSCM-01-2014-0001
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: http://dx.doi.org/10.1016/j.rser.2010.01.020
Hsieh, C.H. and Chen, S.H. (1999). A model and algorithm of fuzzy product positioning. Information Sciences, 121, 61–82. doi: http://dx.doi.org/10.1016/S0020-0255(99)00050-X
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: http://dx.doi.org/10.1016/j.cor.2014.10.015
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: http://dx.doi.org/10.1016/j.ijdrr.2013.08.005
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 multiâ€attribute cost–benefit analysis of eâ€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: http://dx.doi.org/10.1080/18756891.2016.1180817
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: http://dx.doi.org/10.13033/ijahp.v8i1.297
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: http://dx.doi.org/10.1142/S021848851240003X
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