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Warehouse location selection with TOPSIS group decision-making under different expert priority allocations

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Warehouses are crucial infrastructures in supply chains. As a strategic task that would potentially impact various long-term agenda, warehouse location selection becomes an important decision-making process. Due to quantitative and qualitative multiple criteria in selecting alternative warehouse locations, the task becomes a multiple criteria decision-making problem. Current literature offers several approaches to addressing the domain problem. However, the number of factors or criteria considered in the previous works is limited and does not reflect real-life decision-making. In addition, such a problem requires a group decision, with decision-makers having different motivations and value systems. Analysing the varying importance of experts comprising the group would provide insights into how these variations influence the final decision regarding the location. Thus, in this work, we adopted the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to address a warehouse location decision problem under a significant number of decision criteria in a group decision-making environment. To elucidate the proposed approach, a case study in a product distribution firm was carried out. Findings show that decision-makers in this industry emphasise criteria that maintain the distribution networks more efficiently at minimum cost. Results also reveal that varying priorities of the decision-makers have little impact on the group decision, which implies that their degree of knowledge and expertise is comparable to a certain extent. With the efficiency and tractability of the required computations, the TOPSIS method, as demonstrated in this work, provides a useful, practical tool for decision-makers with limited technical computational expertise in addressing the warehouse location problem.
Rocznik
Strony
22--39
Opis fizyczny
Bibliogr. 83 poz., rys., tab.
Twórcy
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
  • Cebu Technological University, Philippines
autor
  • Cebu Technological University, Philippines
  • De La Salle University, Philippines
Bibliografia
  • Alberto, P. (2000). The logistics of industrial location decisions: An application of the analytic hierarchy process methodology. International Journal of Logistics: Research and Applications, 3(3), 273-289. doi: 10.1080/713682767
  • An, Y., Zeng, B., Zhang, Y., & Zhao, L. (2014). Reliable pmedian facility location problem: two-stage robust models and algorithms. Transportation Research Part B: Methodological, 64, 54-72. doi: 10.1016/j. trb.2014.02.005
  • Ardjmand, E., Park, N., Weckman, G., & Amin-Naseri, M. R. (2014). The discrete Unconscious search and its application to uncapacitated facility location problem. Computers & Industrial Engineering, 73, 32-40. doi: 10.1016/j.cie.2014.04.010
  • Athawale, V., Chatterjee, P., & Chakraborty, S. (2012). Decision making for facility location selection using PROMETHEE II method. International Journal of Industrial and Systems Engineering, 11(1-2), 16-30. doi: 10.1504/IJISE.2012.046652
  • Aydin, N., & Murat, A. (2013). A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem. International Journal of Production Economics, 145(1), 173- 183. doi: 10.1016/j.ijpe.2012.10.019
  • Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051-13069. doi: 10.1016/j.eswa.2012.05.056
  • Boltürk, E., Çevik Onar, S., Öztayşi, B., Kahraman, C., & Goztepe, K. (2016). Multi-attribute warehouse location selection in humanitarian logistics using hesitant fuzzy AHP. International Journal of the Analytic Hierarchy Process, 8(2), 271-298. doi: 10.13033/ijahp. v8i2.387
  • Brunaud, B., Bassett, M. H., Agarwal, A., Wassick, J. M., & Grossmann, I. E. (2018). Efficient formulations for dynamic warehouse location under discrete transportation costs. Computers & Chemical Engineering, 111, 311-323. doi: 10.1016/j.compchemeng.2017.05.011
  • Büyüközkan, G., & Uztürk, D. (2017, July). Combined QFD TOPSIS approach with 2-tuple linguistic information for warehouse selection. In 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 1-6). IEEE. doi: 10.1109/FUZZ-IEEE.2017.8015684
  • Chan, F. T. S., Kumar, N., & Choy, K. L. (2007). Decisionmaking approach for the distribution centre location problem in a supply chain network using the fuzzy-based hierarchical concept. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(4), 725-739. doi: 10.1243/09544054JEM526
  • Cheng, E. W., Li, H., & Yu, L. (2005). The analytic network process (ANP) approach to location selection: a shopping mall illustration. Construction Innovation, 5(2), 83-98. doi: 10.1108/14714170510815195
  • Chou, S. Y., Chang, Y. H., & Shen, C. Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132-145. doi: 10.1016/j. ejor.2007.05.006
  • Chu, T. C. (2002). Facility location selection using fuzzy TOPSIS under group decisions. International Journal of Uncertainty, Fuzziness and Knowledgebased systems, 10(6), 687-701. doi: 10.1142/ S0218488502001739
  • Colson, G., & Dorigo, F. (2004). A public warehouses selection support system. European Journal of Operational Research, 153(2), 332-349. doi: 10.1016/S0377- 2217(03)00156-5
  • Cura, T. (2010). A parallel local search approach to solving the uncapacitated warehouse location problem. Computers & Industrial Engineering, 59(4), 1000- 1009. doi: 10.1016/j.cie.2010.09.012
  • Demirel, T., Demirel, N. Ç., & Kahraman, C. (2010). Multicriteria warehouse location selection using Choquet integral. Expert Systems with Applications, 37(5), 3943-3952. doi: 10.1016/j.eswa.2009.11.022
  • Deng, H., Yeh, C. H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963-973. doi: 10.1016/S0305-0548(99)00069-6
  • Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2013). A hybrid fuzzy technique for the selection of warehouse location in a supply chain under a Utopian environment. International Journal of Management Science and Engineering Management, 8(4), 250-261. doi: 10.1080/17509653.2013.825075
  • Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2016). Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria. International Journal of Management Science and Engineering Management, 11(4), 262-278. doi: 10.1080/17509653.2015.1086964
  • Dey, B., Bairagi, B., Sarkar, B., & Sanyal, S. K. (2017). Group heterogeneity in multi member decision making model with an application to warehouse location selection in a supply chain. Computers & Industrial Engineering, 105, 101-122. doi: 10.1016/j. cie.2016.12.025
  • Dogan, I. (2012). Analysis of facility location model using Bayesian Networks. Expert Systems with Applications, 39(1), 1092-1104. doi: 10.1016/j.eswa.2011.07.109
  • Emeç, Ş., & Akkaya, G. (2018). Stochastic AHP and fuzzy VIKOR approach for warehouse location selection problem. Journal of Enterprise Information Management, 31(6), 950-962. doi: 10.1108/JEIM-12-2016- 0195
  • Franek, J., & Kashi, K. (2017). Application of hybrid madm methods for performance evaluation in manufacturing. Forum Scientiae Oeconomia, 5(2), 41-54. doi: 10.23762/fso_vol5no2_17_4
  • García, J. L., Alvarado, A., Blanco, J., Jiménez, E., Maldonado, A. A., & Cortés, G. (2014). Multi-attribute evaluation and selection of sites for agricultural product warehouses based on an analytic hierarchy process. Computers and Electronics in Agriculture, 100, 60-69. doi: 10.1016/j.compag.2013.10.009
  • Ghaderi, A., & Jabalameli, M. S. (2013). Modeling the budget-constrained dynamic uncapacitated facility location–network design problem and solving it via two efficient heuristics: a case study of health care. Mathematical and Computer Modelling, 57(3-4), 382-400. doi: 10.1016/j.mcm.2012.06.017
  • Guastaroba, G., & Speranza, M. G. (2014). A heuristic for BILP problems: the single source capacitated facility location problem. European Journal of Operational Research, 238(2), 438-450. doi: 10.1016/j. ejor.2014.04.007
  • Hakim, R. T., & Kusumastuti, R. D. (2018). A model to determine relief warehouse location in East Jakarta using the analytic hierarchy process. International Journal of Technology, 9(7), 1405-1414. doi: 10.14716/ ijtech.v9i7.1596
  • He, J., Feng, C., Hu, D., & Liang, L. (2017). A decision model for emergency warehouse location based on a novel stochastic MCDA method: evidence from China. Mathematical Problems in Engineering, 2017, 7804781. doi: 10.1155/2017/7804781
  • Ho, S. C. (2015). An iterated tabu search heuristic for the single source capacitated facility location problem. Applied Soft Computing, 27, 169-178. doi: 10.1016/j. asoc.2014.11.004
  • Huang, H. C., & Li, R. (2008). A k-product uncapacitated facility location problem. European Journal of Operational Research, 185(2), 552-562. doi: 10.1016/j. ejor.2007.01.010
  • Hung, C. C., & Chen, L. H. (2009, March). A fuzzy TOPSIS decision making model with entropy weight under intuitionistic fuzzy environment. In Proceedings of the International Multiconference of Engineers and Computer Scientists (vol. 1, pp. 13–16). IMECS Hong Kong.
  • Hwang, C., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Berlin, Germany: Springer.
  • Jha, M. K., Raut, R. D., Gardas, B. B., & Raut, V. (2018). A sustainable warehouse selection: an interpretive structural modelling approach. International Journal of Procurement Management, 11(2), 201-232. doi: 10.1504/IJPM.2018.090025
  • Kabak, M., & Keskin, İ. (2018). Hazardous materials warehouse selection based on GIS and MCDM. Arabian Journal for Science and Engineering, 43(6), 3269- 3278. doi: 10.1007/s13369-018-3063-z
  • Kelemenis, A., & Askounis, D. (2010). A new TOPSISbased multi-criteria approach to personnel selection. Expert Systems with Applications, 37(7), 4999-5008. doi: 10.1016/j.eswa.2009.12.013
  • Kim, G., Park, C. S., & Yoon, K. P. (1997). Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. International Journal of Production Economics, 50(1), 23-33. doi: 10.1016/S0925- 5273(97)00014-5
  • Klose, A., & Görtz, S. (2007). A branch-and-price algorithm for the capacitated facility location problem. European Journal of Operational Research, 179(3), 1109-1125. doi: 10.1016/j.ejor.2005.03.078
  • Korpela, J., & Tuominen, M. (1996). A decision aid in warehouse site selection. International Journal of Production Economics, 45(1-3), 169-180. doi: 10.1016/0925- 5273(95)00135-2
  • Kratica, J., Dugošija, D., & Savić, A. (2014). A new mixed integer linear programming model for the multi level uncapacitated facility location problem. Applied Mathematical Modelling, 38(7-8), 2118-2129. doi: 10.1016/j.apm.2013.10.012
  • Kuo, M. S. (2011). Optimal location selection for an international distribution center by using a new hybrid method. Expert Systems with Applications, 38(6), 7208-7221. doi: 10.1016/j.eswa.2010.12.002
  • Kutlu Gündoğdu, F., & Kahraman, C. (2019). A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection. Journal of Intelligent & Fuzzy Systems, 37(1), 1197-1211. doi: 10.3233/JIFS-182651
  • Lee, S. M., Green, G. I., & Kim, C. S. (1980). A multicriteria warehouse location model. Academy of Management Proceedings, 1980(1), 317-321. doi: 10.5465/ ambpp.1980.4977851
  • Li, H., Lv, T., & Li, Y. (2015). The tractor and semitrailer routing problem with many-to-many demand considering carbon dioxide emissions. Transportation Research Part D: Transport and Environment, 34, 68- 82. doi: 10.1016/j.trd.2014.10.004
  • Li, J., Chu, F., Prins, C., & Zhu, Z. (2014). Lower and upper bounds for a two-stage capacitated facility location problem with handling costs. European Journal of Operational Research, 236(3), 957-967. doi: 10.1016/j.ejor.2013.10.047
  • MacCarthy, B. L., & Atthirawong, W. (2003). Factors affecting location decisions in international operationsa Delphi study. International Journal of Operations & Production Management, 23(7), 794-818. doi: 10.1108/01443570310481568
  • Melachrinoudis, E., & Min, H. (2000). The dynamic relocation and phase-out of a hybrid, two-echelon plant/ warehousing facility: A multiple objective approach. European Journal of Operational Research, 123(1), 1-15. doi: 10.1016/S0377-2217(99)00166-6
  • Monthatipkul, C. (2016). A non-linear program to find an approximate location of a second warehouse: A case study. Kasetsart Journal of Social Sciences, 37(3), 190- 201. doi: 10.1016/j.kjss.2016.08.007
  • Nevima, J., & Kiszová, Z. (2017). Modified human development index and its weighted alternative – the case of Visegrad Four plus Austria and Slovenia. Forum Scientiae Oeconomia, 5(2), 102-111. doi: 10.23762/ fso_vol5no2_17_8
  • Nezhad, A. M., Manzour, H., & Salhi, S. (2013). Lagrangian relaxation heuristics for the uncapacitated singlesource multi-product facility location problem. International Journal of Production Economics, 145(2), 713-723. doi: 10.1016/j.ijpe.2013.06.001
  • Ocampo, L., & Clark, E. (2015). A sustainable manufacturing strategy decision framework in the context of multi-criteria decision-making. Jordan Journal of Mechanical & Industrial Engineering, 9(3), 177-186.
  • Ocampo, L. A. (2019). Applying fuzzy AHP–TOPSIS technique in identifying the content strategy of sustainable manufacturing for food production. Environment, Development and Sustainability, 21(5), 2225-2251. doi: 10.1007/s10668-018-0129-8
  • Özcan, T., Çelebi, N., & Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773-9779. doi: 10.1016/j.eswa.2011.02.022
  • Ozsen, L., Coullard, C. R., & Daskin, M. S. (2008). Capacitated warehouse location model with risk pooling. Naval Research Logistics, 55(4), 295-312. doi: 10.1002/nav.20282
  • Rahmani, A., & MirHassani, S. A. (2014). A hybrid FireflyGenetic Algorithm for the capacitated facility location problem. Information Sciences, 283, 70-78. doi: 10.1016/j.ins.2014.06.002
  • Rakas, J., Teodorović, D., & Kim, T. (2004). Multi-objective modeling for determining location of undesirable facilities. Transportation Research Part D: Transport and Environment, 9(2), 125-138. doi: 10.1016/j. trd.2003.09.002
  • Rao, C., Goh, M., Zhao, Y., & Zheng, J. (2015). Location selection of city logistics centers under sustainability. Transportation Research Part D: Transport and Environment, 36, 29-44. doi: 10.1016/j.trd.2015.02.008
  • 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: 10.1016/j.cor.2011.07.016
  • Raut, R. D., Narkhede, B. E., Gardas, B. B., & Raut, V. (2017). Multi-criteria decision making approach: a sustainable warehouse location selection problem. International Journal of Management Concepts and Philosophy, 10(3), 260-281. doi: 10.1504/IJMCP.2017.085834
  • Resende, M. G., & Werneck, R. F. (2006). A hybrid multistart heuristic for the uncapacitated facility location problem. European Journal of Operational Research, 174(1), 54-68. doi: 10.1016/j.ejor.2005.02.046
  • 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: 10.1016/j.ajsl.2013.05.006
  • Roh, S. Y., Shin, Y. R., & Seo, Y. J. (2018). The pre-positioned warehouse location selection for international humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 34(4), 297-307. doi: 10.1016/j. ajsl.2018.12.003
  • Roh, S., Pettit, S., Harris, I., & Beresford, A. (2015). The prepositioning of warehouses at regional and local levels for a humanitarian relief organisation. International Journal of Production Economics, 170, 616-628. doi: 10.1016/j.ijpe.2015.01.015
  • Rosenwein, M. B. (1996). A comparison of heuristics for the problem of batching orders for warehouse selection. International Journal of Production Research, 34(3), 657-664. doi: 10.1080/00207549608904926
  • Roszkowska, E. (2011). Multi-criteria decision making models by applying the TOPSIS method to crisp and interval data. Multiple Criteria Decision Making. University of Economics in Katowice, ’10-11, 200-230.
  • Roy, B. (1990). The outranking approach and the foundations of ELECTRE methods. In Bana e Costa C.A. (eds). Readings in multiple criteria decision aid (pp. 155-183). Springer, Berlin, Heidelberg. doi: 10.1007/978-3-642-75935-2_8
  • Roy, B. (1991). The outranking approach and the foundations of ELECTRE methods. Theory and Decision, 31(1), 49-73. doi: 10.1007/BF00134132
  • Saaty, T. L. (1980). The analytic hierarchy process. McGrawHill, New York. Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8), 801-813. doi: 10.1016/j.mcm.2006.03.023
  • Shukla, A., Agarwal, P., Rana, R. S., & Purohit, R. (2017). Applications of TOPSIS algorithm on various manufacturing processes: a review. Materials Today: Proceedings, 4(4), 5320-5329. doi: 10.1016/j.matpr.2017.05.042
  • Singh, R. K., Chaudhary, N., & Saxena, N. (2018). Selection of warehouse location for a global supply chain: A case study. IIMB Management Review, 30(4), 343- 356. doi: 10.1016/j.iimb.2018.08.009
  • Stankevičienė, J., & Nikanorova, M. (2020). Eco-innovation as a pillar for sustainable development of circular economy. Business: Theory and Practice, 21(2), 531- 544. doi: 10.3846/btp.2020.12963
  • Temur, G. T. (2016). A novel multi attribute decision making approach for location decision under high uncertainty. Applied Soft Computing, 40, 674-682. doi: 10.1016/j.asoc.2015.12.027
  • Tyagi, R., & Das, C. (1995). Manufacturer and warehouse selection for stable relationships in dynamic wholesaling and location problems. International Journal of Physical Distribution & Logistics Management, 25(6), 54-72. doi: 10.1108/09600039510093276
  • Vanichchinchai, A., & Apirakkhit, S. (2018). An identification of warehouse location in Thailand. Asia Pacific Journal of Marketing and Logistics, 30(3), 749-758. doi: 10.1108/APJML-10-2017-0229
  • Vavrek, R., Adamisin, P., & Kotulic, R. (2017). Multi-criteria evaluation of municipalities in Slovakia - case study in selected districts. Polish Journal of Man agement Studies, 16(2), 290-301. doi: 10.17512/ pjms.2017.16.2.25
  • Vlachopoulou, M., Silleos, G., & Manthou, V. (2001). Geographic information systems in warehouse site selection decisions. International Journal of Production Economics, 71(1-3), 205-212. doi: 10.1016/S0925- 5273(00)00119-5
  • Wagner, M. R., Bhadury, J., & Peng, S. (2009). Risk management in uncapacitated facility location models with random demands. Computers & Operations Research, 36(4), 1002-1011. doi: 10.1016/j.cor.2007.12.008
  • Weber, A. (1909). Ueber den Standort der Industrieni. TuKbingen: J.C.B. Mohr. [English translation: The Theory of the Location of Industries. Chicago: Chicago University Press, 1929].
  • Weber, A. (1929) (translated by Carl J. Friedrich from Weber’s 1909 book). Theory of the location of industries. Chicago: The University of Chicago Press.
  • Wutthisirisart, P., Sir, M. Y., & Noble, J. S. (2015). The twowarehouse material location selection problem. International Journal of Production Economics, 170, 780-789. doi: 10.1016/j.ijpe.2015.07.008
  • Xifeng, T., Ji, Z., & Peng, X. (2013). A multi-objective optimization model for sustainable logistics facility location. Transportation Research Part D: Transport and Environment, 22, 45-48. doi: 10.1016/j. trd.2013.03.003
  • Yadav, S. K., Joseph, D., & Jigeesh, N. (2018). A review on industrial applications of TOPSIS approach. International Journal of Services and Operations Management, 30(1), 23-28. doi: 10.1504/IJSOM.2018.091438
  • Yap, J. Y. L., Ho, C. C., & Ting, C. -Y. (2019). A systematic review of the applications of multi-criteria decisionmaking methods in site selection problems. Built Environment Project and Asset Management, 9(4), 548- 563. doi: 10.1108/BEPAM-05-2018-0078
  • You, M., Xiao, Y., Zhang, S., Yang, P., & Zhou, S. (2019). Optimal mathematical programming for the warehouse location problem with Euclidean distance linearization. Computers & Industrial Engineering, 136, 70-79. doi: 10.1016/j.cie.2019.07.020
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-367dad2d-63f1-42c0-9103-1c32398d4433
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