A NEW INTEGRATED GREY MCDM MODEL: CASE OF WAREHOUSE LOCATION SELECTION
Abstract
Warehouses link suppliers and customers throughout the entire supply chain. The location of the warehouse has a significant impact on the logistics process. Even though all other warehouse activities are successful, if the product dispatched from the warehouse fails to meet the customer needs in time, the company may face with the risk of losing customers. This affects the performance of the whole supply chain therefore the choice of warehouse location is an important decision problem. This problem is a multi-criteria decision-making (MCDM) problem since it involves many criteria and alternatives in the selection process. This study proposes an integrated grey MCDM model including grey preference selection index (GPSI) and grey proximity indexed value (GPIV) to determine the most appropriate warehouse location for a supermarket. This study aims to make three contributions to the literature. PSI and PIV methods combined with grey theory will be introduced for the first time in the literature. In addition, GPSI and GPIV methods will be combined and used to select the best warehouse location. In this study, the performances of five warehouse location alternatives were assessed with twelve criteria. Location 4 is found as the best alternative in GPIV. The GPIV results were compared with other grey MCDM methods, and it was found that GPIV method is reliable. It has been determined from the sensitivity analysis that the change in criteria weights causes a change in the ranking of the locations therefore GPIV method was found to be sensitive to the change in criteria weights.
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Pang, K.W., Chan, H.L., 2017, Data mining-based algorithm for storage location assignment in a randomised warehouse, International Journal of Production Research, 55(14), pp. 4035–4052.
Aktepe, A., Ersöz, S., 2014, AHP-VIKOR ve MOORA yöntemlerinin depo yeri seçim probleminde uygulanması, Endüstri Mühendisliği Dergisi, 25(1–2), pp. 2–15.
Emeç, Ş., Akkaya, G., 2018, Stochastic AHP and fuzzy VIKOR approach for warehouse location selection problem, Journal of Enterprise Information Management, 31(6), pp. 950–962.
Ecer, F., Pamucar, D., 2020, Sustainable supplier selection: A novel integrated fuzzy best worst method (F-BWM) and fuzzy CoCoSo with Bonferroni (CoCoSo’B) multi-criteria model, Journal of Cleaner Production, 266, 121981.
Ecer, F., Pamucar, D., 2021, MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services, Applied Soft Computing, 104, 107199.
Shojaei, P., Bolvardizadeh, A., 2020, Rough MCDM model for green supplier selection in Iran: a case of university construction project, Built Environment Project and Asset Management, 10(3), pp. 437-452.
Stević, Ž., Karamaşa, Ç., Demir, E., Korucuk, S., 2021, Assessing sustainable production under circular economy context using a novel rough-fuzzy MCDM model: a case of the forestry industry in the Eastern Black Sea region, Journal of Enterprise Information Management. Article in press.
Pamucar, D., Deveci, M., Canıtez, F., Paksoy, T., Lukovac, V., 2021, A Novel Methodology for Prioritizing Zero-Carbon Measures for Sustainable Transport, Sustainable Production and Consumption, 27, pp. 1093-1112.
Tian, G., Zhang, H., Feng, Y., Wang, D., Peng, Y., Jia, H., 2018, Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method, Renewable and Sustainable Energy Reviews, 81, pp. 682-692.
Tadić, S., Krstić, M., Roso, V., Brnjac, N., 2020, Dry Port Terminal Location Selection by Applying the Hybrid Grey MCDM Model, Sustainability, 12(17), 6983.
Liu, S., Lin, Y., 2006, Grey information: Theory and practical applications, Springer Science & Business Media, London.
Bai, C., Sarkis, J., 2010, Integrating sustainability into supplier selection with grey system and rough set methodologies, International Journal of Production Economics, 124(1), pp. 252–264.
Xia, X., Govindan, K., Zhu, Q., 2015, Analyzing internal barriers for automotive parts remanufacturers in China using grey-DEMATEL approach, Journal of Cleaner Production, 87, pp. 811–825.
Attri, R., Grover, S., 2015, Application of preference selection index method for decision making over the design stage of production system life cycle, Journal of King Saud University-Engineering Sciences, 27(2), pp. 207-216.
Mufazzal, S., Muzakkir, S.M., 2018, A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals, Computers & Industrial Engineering, 119, pp. 427-438.
Khan, N.Z., Ansari, T.S.A., Siddiquee, A.N., Khan, Z.A, 2019, Selection of E-learning websites using a novel Proximity Indexed Value (PIV) MCDM method, Journal of Computers in Education, 6(2), pp. 241-256.
Oztaysi, B., 2014, A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems, Knowledge-Based Systems, 70, pp. 44–54.
Zavadskas, E.K., Turskis, Z., Antucheviciene, J., 2015, Selecting a contractor by using a novel method for multiple attribute analysis: Weighted Aggregated Sum Product Assessment with grey values (WASPAS-G), Studies in Informatics and Control, 24(2), pp. 141–150.
Zavadskas, E.K., Kaklauskas, A., Turskis, Z., Tamošaitiene, J., 2008, Selection of the effective dwelling house walls by applying attributes values determined at intervals, Journal of Civil Engineering and Management, 14(2), pp. 85–93.
Lee, C., 1993, The multiproduct warehouse location problem: Applying a decomposition algorithm, International Journal of Physical Distribution and Logistics Management, 23, pp. 3-13.
S.L. Hakimi, S.L., Kuo, C.C.,1991, On a general network location allocation problem, European Journal of Operational Research, 108, pp. 135-142.
Korpela, J., Lehmusvaara, A., 1999, A customer oriented approach to warehouse network evaluation and design, International Journal of Production Economics, 59, pp. 135-146.
Korpela, J., Lehmusvaara, A., Nisonen, J., 2007, Warehouse operator selection by combining AHP and DEA methodologies, International Journal of Production Economics, 108, pp. 135-142.
Ho, W., Emrouznejad, A., 2009, Multi-criteria logistics distribution network design using SAS/OR,
Expert Systems with Applications, 36, pp. 7288-7298.
R.J. Kuo, R.J, Chi, S.C., Kao, S.S., 2002, A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network, Computers in Industry, 47, pp. 199-214.
Tabari, M., Kaboli, A., Aryanezhad, M.B., Shahanaghi, K, Siadat, A., 2008, A new method for location selection: A hybrid analysis Applied Mathematics and Computation, 206, pp. 598-606
Chen, C., 2001, A fuzzy approach to select the location of the distribution center, Fuzzy Sets and Systems, 118, pp. 65-73.
Kahraman, C., Ruan, D., Doğan, I., 2003, Fuzzy group decision-making for facility location selection, Information Sciences, 157, pp. 135-153
Karmaker, C., Saha, M., 2015, Optimization of warehouse location through fuzzy multi-criteria decision making methods, Decision Science Letters, 4(3), pp. 315–334
Stevenson, W.J., 1993, Production/operations management, McGraw-Hill Company, New York.
Frazelle, E., 2002, Supply chain strategy: the logistics of supply chain management, McGraw-Hill Education, New York.
Demirel, T., Demirel, N.Ç., Kahraman, C., 2010, Multi-criteria warehouse location selection using Choquet integral, Expert Systems with Applications, 37(5), pp. 3943–3952
Kuehn, A.A., Hamburger, M.J., 1963, A heuristic program for locating warehouses, Management Science, 9(4), pp. 643–666.
Efroymson, M., Ray, T., 1966, A branch-bound algorithm for plant location, Operations Research, 14(3), pp. 361–368.
Khumawala, B.M., 1972, An efficient branch and bound algorithm for the warehouse location problem, Management Science, 18(12), pp. 718–731.
Weber, A., Friedrich, C.J., 1929, Alfred Weber’s theory of the location of industries, Chicago, Ill., The University of Chicago Press, Chicago.
Tellier, L.N., 1972, The Weber problem: solution and interpretation, Geographical Analysis, 4(3), pp. 215–233.
Owen, S.H., Daskin M.S., 1998, Strategic facility location: a review, European Journal of Operational Research, 111(3), pp. 423–447.
Badri, M.A., 1999, Combining the analytic hierarchy process and goal programming for global facility location-allocation problem, International Journal of Production Economics, 62(3), pp. 237–248.
Vlachopoulou, M., Silleos, G., Manthou, V., 2001, Geographic information systems in warehouse site selection decisions, International Journal of Production Economics, 71(1– 3), pp. 205–212.
Kabak, M., Keskin, İ., 2018, Hazardous materials warehouse selection based on GIS and MCDM, Arabian Journal for Science & Engineering, 43(6), pp. 3269–3278.
Yerlikaya, M.A., Tabak, Ç., Yıldız, K., 2019, Logistic location selection with Critic-Ahp and Vikor integrated approach, Data Science and Applications, 2(1), pp. 21– 25.
Mihajlović, J., Rajković, P., Petrović, G., Ćirić, D., 2019, The selection of the logistics distribution center location based on MCDM methodology in southern and eastern region in Serbia, Operational Research in Engineering Sciences: Theory and Applications, 2(2), pp. 72–85.
Ma, Y., Su, X., Zhao, Y., 2018, Hybrid multi-attribute decision making methods: an application, Tehnički Vjesnik, 25(5), pp. 1421–1428.
Pamučar, D., Božanić, D., 2019, Selection of a location for the development of multimodal logistics center: application of single-valued neutrosophic MABAC model, Operational Research in Engineering Sciences: Theory and Applications, 2(2), pp. 55–71.
Tuzkaya, G., Önüt, S., Tuzkaya, U.R., Gülsün, B., 2008, An analytic network process approach for locating undesirable facilities: an example from Istanbul, Turkey, Journal of Environmental Management, 88(4), pp. 970–983.
Uysal, F., Tosun, Ö., 2014, Selection of sustainable warehouse location in supply chain using the grey approach, International Journal of Information and Decision Sciences, 6(4), pp. 338– 353.
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), pp. 9773–9779.
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), pp. 655–671.
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), pp. 250–261.
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, pp. 60–69.
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), pp. 262–278.
Silva, D.D., Vasconcelos, N.V.C., Cavalcante, C.A.V., 2015, Multicriteria decision model to support the assignment of storage location of products in a warehouse, Mathematical Problems in Engineering, Article ID 481950.
Mangalan, A.V., Kuriakose, S., Mohamed, H., Ray, A., 2016, Optimal location of warehouse using weighted MOORA approach, In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 662–665, IEEE.
Temur, G.T., 2016, A novel multi attribute decision making approach for location decision under high uncertainty, Applied Soft Computing, 40, pp. 674–682.
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, pp. 101–122.
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), pp. 260–281.
Micale, R., La Fata, C.M., La Scalia, G.A, 2019, Combined interval-valued ELECTRE TRI and TOPSIS approach for solving the Storage Location Assignment Problem, Computers & Industrial Engineering, 135, pp. 199–210.
Canbolat, Y.B., Chelst, K., Garg, N., 2007, Combining decision tree and MAUT for selecting a country for a global manufacturing facility, Omega 35(3), pp. 312–325.
Ehsanifar, M., Wood, D.A., Babaie, A., 2021, UTASTAR method and its application in multi-criteria warehouse location selection, Operations Management Research, 14, pp. 202–215.
Maniya, K., Bhatt, M.G., 2010, A selection of material using a novel type decision-making method: Preference Selection Index method, Materials & Design, 31(4), pp. 1785–1789.
Vahdani, B., Mousavi, S.M., Ebrahimnejad, S., 2014, Soft computing-based preference selection index method for human resource management, Journal of Intelligent & Fuzzy Systems, 26(1), pp. 393–403.
Attri, R., Grover, S., 2015, Application of Preference Selection Index method for decision making over the design stage of production system life cycle, Journal of King Saud University-Engineering Sciences, 27(2), pp. 207–216.
Chamoli, S., 2015, Preference Selection Index approach for optimization of V down perforated baffled roughened rectangular channel, Energy, 93, pp. 1418–1425.
Akyüz, G., Aka, S., 2015, An Alternative approach for manufacturing performance measurement: Preference Selection Index (PSI) Method, Business and Economics Research Journal, 6(1), pp. 63–77.
Petković, D., Madić, M., Radovanović, M., Gečevska, V., 2017, Application of the performance selection index method for solving machining MCDM problems, Facta Universitatis-Series Mechanical Engineering, 15(1), pp. 97–106.
Madić, M., Antucheviciene, J., Radovanović, M., Petković, D., 2017, Determination of laser cutting process conditions using the Preference Selection Index method, Optics & Laser Technology, 89, pp. 214–220.
Tuş, A., Adalı, E.A., 2018, CODAS ve PSI yöntemleri ile personel değerlendirmesi, Alphanumeric Journal, 6(2), pp. 243–256.
Jha, K., Chamoli, S., Tyagi, Y.K., Maurya, H.O., 2018, Characterization of biodegradable composites and application of Preference Selection Index for deciding optimum phase combination, Materials Today: Proceedings, 5(2), pp. 3353–3360.
Pathak, V.K.,Singh, R., Gangwar, S., 2019, Optimization of three-dimensional scanning process conditions using Preference Selection Index and metaheuristic method, Measurement, 146, pp. 653–667.
Ulutaş, A., Topal, A., Bakhat, R., 2019, An Application of fuzzy integrated model in green supplier selection, Mathematical Problems in Engineering, Article ID 4256359.
Pamučar, D., Ćirović, G., 2015, The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC), Expert Systems with Applications, 42(6), pp. 3016-3028.
Gigović, L., Pamučar, D., Bajić, Z., Milićević, M., 2016, The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depots, Sustainability, 8(4), 372.
Pamučar, D., Stević, Z., Sremac, S., 2018, A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM), Symmetry, 10(9) 393.
Žižović, M., Pamucar, D., 2019, New model for determining criteria weights: Level Based Weight Assessment (LBWA) model, Decision Making: Applications in Management and Engineering, 2(2), pp. 126-137.
Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P., 2020, Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS), Computers & Industrial Engineering, 140, 106231.
Žižović, M., Pamučar, D., Albijanić, M., Chatterjee, P., Pribićević, I., 2020, Eliminating rank reversal problem using a new multi-attribute model—the rafsi method, Mathematics, 8(6), 1015.
Ulutaş, A., Stanujkic, D., Karabasevic, D., Popovic, G., Zavadskas, E. K., Smarandache, F., Brauers, W. K., 2021, Developing of a Novel Integrated MCDM MULTIMOOSRAL Approach for Supplier Selection, Informatica, 32(1), pp. 145-161.
Yahya, S.M., Asjad, M., Khan, Z.A, 2019, Multi-response optimization of TiO2/EG-water nano-coolant using Entropy based Preference Indexed Value (PIV) method, Materials Research Express, 6(8), 0850a1.
Tomić, V., Marinković, D., Marković, D., 2014, The selection of logistic centers location using multi-criteria comparison: case study of the Balkan Peninsula, Acta Polytechnica Hungarica, 11(10), pp. 97-113.
Ulutaş, A., Karaköy, Ç., Arıç, K. H., Cengiz, E., 2018, Çok Kriterli Karar Verme Yöntemleri İle Lojistik Merkezi Yeri Seçimi, İktisadi Yenilik Dergisi, 5(2), pp. 45-53.
Pamucar, D. S., Pejcic Tarle, S., Parezanovic, T., 2018, New hybrid multi-criteria decision-making DEMATELMAIRCA model: sustainable selection of a location for the development of multimodal logistics centre, Economic research-Ekonomska istraživanja, 31(1), pp. 1641-1665.
Yazdani, M., Chatterjee, P., Pamucar, D., Chakraborty, S., 2020, Development of an integrated decision making model for location selection of logistics centers in the Spanish autonomous communities, Expert Systems with Applications, 148, 113208.
DOI: https://doi.org/10.22190/FUME210424060U
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