Abstract
Nowadays, organizational decisions are made collectively in decision groups to achieve more meaningful and impactful outcomes, ranging from product design, policy and strategy formulation and resource allocation. This research, therefore, suggests a group decision-making (GDM) approach utilizing a recently developed MCDM method known as best–worst method (BWM) in combination with GIS for planning suitable areas for new emergency facilities in Istanbul. Using two decision-maker (DM) groups consisting of academic-related professionals and fire brigade practitioners, the BWM method was used to evaluate the associated weights and preference rankings of six pre-selected criteria, derived from pairwise comparisons of the best and worst criterion for each DM. The preference criteria of the two DM groups were examined to deepen the understanding of the varying perceptions about the level of influence of the criteria from a theoretical and practical view as well as to reflect a real-case scenario in typical GDM problems where group agreement or reliability is assessed by consensus using Kendall’s coefficient of concordance, W. The BWM results were compared for model validation with the AHP and found to be reliable and consistent. Further, from statistical tests conducted, it was inferred that criteria C4 (density of hazardous materials) and C1 (high population density) were perceived to be the most important by the academician and fire brigade practitioner DM group, respectively. For both DM groups, criterion C6 (distance from earthquake risk) was viewed to be the least important. Resultant raster suitability maps for both DM groups were produced for visualizing the BWM model.
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References
Afshari AR, Vatanparast M, Ćoćkalo D (2016) Application of multi criteria decision making to urban planning: a review. J Eng Manag Compet 6:46–53
Ajrina SA, Sarno R, Hari Ginardi RV (2018) Comparison of AHP and BWM methods based on geographic information system for determining potential zone of Pasir Batu mining. In: Proceedings-2018 International Seminar on Application for Technology of Information and Communication Creative Technology for Human Life, iSemantic pp 453–457. https://doi.org/10.1109/ISEMANTIC.2018.8549818
Bhushan N, Rai K (2007) Strategic decision making: applying the analytic hierarchy process. Springer, Berlin
Chitsaz N, Azarnivand A (2017) Water scarcity management in arid regions based on an extended multiple criteria technique. Water Resour Manag 31:233–250. https://doi.org/10.1007/s11269-016-1521-5
Dong X, Li Y, Pan Y et al (2018) Study on urban fire station planning based on fire risk assessment and GIS technology. Proc Eng 211:124–130. https://doi.org/10.1016/j.proeng.2017.12.129
Erden T (2012) Disaster and emergency management activities by geospatial tools with special reference to Turkey. Disaster Adv 5:29–36
Erden T, Coskun MZ (2010) Natural hazards and earth system sciences multi-criteria site selection for fire services: the interaction with analytic hierarchy process and geographic information systems. Hazards Earth Syst Sci 10:2127–2134. https://doi.org/10.5194/nhess-10-2127-2010
Gay W, Siegel A (1987) Fire station location analysis: a comprehensive planning approach. MIS Report, ICMA
Hagen A (2002) Multi-method assessment of map similarity. In: Universitat de les Illes Balears (ed) 5th AGILE Conference on geographic information science. Universitat de les Illes Balears Palma, Spain, Palma, Spain, pp 171–182
Hashemkhani Zolfani S, Mosharafiandehkordi S, Kutut V (2019) A pre-planning for hotel locating according to the sustainability perspective based on BWM-WASPAS approach. Int J Strateg Prop Manag 23:405–419. https://doi.org/10.3846/ijspm.2019.10844
IMM (1989) Istanbul Metropolitan Municipality. Critical risk areas and station locations: the study of fire safety and protection. Istanbul
IMM (2016) Istanbul metropolitan municipality annual report. Istanbul
Johnston J (1999) The cost effectiveness of fire station siting and the impact on emergency response
Kabak Ö, Ervural B (2017) Knowledge-based systems multiple attribute group decision making: a generic conceptual framework and a classification scheme. Knowl Based Syst 123:13–30. https://doi.org/10.1016/j.knosys.2017.02.011
Kahraman C, Engin O, Kabak Ö, Kaya İ (2009) Information systems outsourcing decisions using a group decision-making approach. Eng Appl Artif Intell 22:832–841
Karaman H, Şahin M, Elnashai AS (2008) Earthquake loss assessment features of Maeviz-Istanbul (Hazturk). J Earthq Eng 12:175–186. https://doi.org/10.1080/13632460802014006
Kendall MG (1938) A new measure of rank correlation. Biometrika 30:81–93. https://doi.org/10.2307/2332226
Kheybari S, Kazemi M, Rezaei J (2019) Bioethanol facility location selection using best-worst method. Appl Energy 242:612–623. https://doi.org/10.1016/j.apenergy.2019.03.054
Koksalmis E, Kabak Ö (2019) Deriving decision makers’ weights in group decision making: an overview of objective methods. Inf Fusion 49:146–160. https://doi.org/10.1016/j.inffus.2018.11.009
Lee GKL, Chan EHW (2008) The analytic hierarchy process (AHP) approach for assessment of urban renewal proposals. Soc Indic Res 89:155–168. https://doi.org/10.1007/s11205-007-9228-x
Liu A, Xiao Y, Ji X et al (2018a) A novel two-stage integrated model for supplier selection of green fresh product. Sustainability 10:2371. https://doi.org/10.3390/su10072371
Liu Y, Li FY, Wang Y et al (2018b) Assessing the environmental impact caused by power grid projects in high altitude areas based on BWM and Vague sets techniques. Sustainability 10:1768. https://doi.org/10.3390/su10061768
Malczewski J (2006) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20:703–726. https://doi.org/10.1080/13658810600661508
Malczewski J, Rinner C (2015) Multicriteria decision analysis in geographic information science. Springer, New York
Mei Y, Liang Y, Tu Y (2018) A multi-granularity 2-tuple QFD method and application to emergency routes evaluation. Symmetry (Basel) 10:484. https://doi.org/10.3390/sym10100484
Moktadir MA, Ali SM, Kusi-Sarpong S, Shaikh MAA (2018) Assessing challenges for implementing Industry 4.0: implications for process safety and environmental protection. Process Saf Environ Prot 117:730–741. https://doi.org/10.1016/j.psep.2018.04.020
Nie RX, Tian ZP, Wang JQ et al (2018a) Water security sustainability evaluation: applying a multistage decision support framework in industrial region. J Clean Prod 196:1681–1704. https://doi.org/10.1016/j.jclepro.2018.06.144
Nie RX, Tian ZP, Wang XK et al (2018b) Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment. Knowl Based Syst 162:185–201. https://doi.org/10.1016/j.knosys.2018.05.030
Nyimbili PH, Erden T (2020) GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul. Turkey Socioecon Plann Sci. https://doi.org/10.1016/j.seps.2020.100860
NFPA (2010) NFPA 1710, Standard for the organization and deployment of fire suppression operations, emergency medical operations, and special operations to the public by career fire departments
Özmen M, Aydoğan EK (2020) Robust multi-criteria decision making methodology for real life logistics center location problem. Artif Intell Rev 53:725–751. https://doi.org/10.1007/s10462-019-09763-y
Pamucar D, Gigovic L, Bajic Z, Janoševic M (2017) Location selection for wind farms using GIS multi-criteria hybrid model: An approach based on fuzzy and rough numbers. Sustain 9:1315. https://doi.org/10.3390/su9081315
Pontius R (2000) Quantification error versus location error in comparison of categorical maps. Photogramm Eng Remote Sens 66:1011–1016
Prévil C, Thériault M (2003) Combining multicriteria analysis and GIS to help decision making processes in Portneuf county (Quebec, Canada). In: Proceedings of 2nd annual URISA public participation GIS conference. URISA summer conference. Portland Oregon, pp 529–554
Ren J (2018) Technology selection for ballast water treatment by multi-stakeholders: a multi-attribute decision analysis approach based on the combined weights and extension. Chemosphere 191:747–760
Ren J, Liang H, Chan FTS (2017) Urban sewage sludge, sustainability, and transition for eco-city: multi-criteria sustainability assessment of technologies based on best-worst method. Technol Forecast Soc Change 116:29–39. https://doi.org/10.1016/j.techfore.2016.10.070
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega 64:126–130. https://doi.org/10.1016/j.omega.2015.12.001
Rezaei J, van Roekel WS, Tavasszy L (2018) Measuring the relative importance of the logistics performance index indicators using Best Worst method. Transp Policy 68:158–169. https://doi.org/10.1016/j.tranpol.2018.05.007
Saaty TL (1980) The analytic hierarchy process: planning, setting priorities, resource allocation. McGraw-Hill International Book Co, New York
Safarzadeh S, Khansefid S, Rasti-Barzoki M (2018) A group multi-criteria decision-making based on best-worst method. Comput Ind Eng 126:111–121. https://doi.org/10.1016/j.cie.2018.09.011
Salimi N, Rezaei J (2018) Evaluating firms’ R&D performance using best worst method. Eval Program Plann 66:147–155. https://doi.org/10.1016/j.evalprogplan.2017.10.002
Sheskin DJ (2003) Handbook of parametric and nonparametric statistical procedures. Chapman and Hall, London
Sotoudeh-Anvari A, Sadjadi J, Mohammad S et al (2018) A new MCDM-based approach using BWM and SAW for optimal search model. Canada Decis Sci Lett 7:395–404. https://doi.org/10.5267/j.dsl.2018.2.001
Stević Ž, Pamučar D, Zavadskas EK et al (2017) The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods. Symmetry (Basel) 9:264. https://doi.org/10.3390/sym9110264
Suhi SA, Enayet R, Haque T et al (2019) Environmental sustainability assessment in supply chain: an emerging economy context. Environ Impact Assess Rev 79:106306. https://doi.org/10.1016/j.eiar.2019.106306
Torabi SA, Giahi R, Sahebjamnia N (2016) An enhanced risk assessment framework for business continuity management systems. Saf Sci 89:201–218. https://doi.org/10.1016/j.ssci.2016.06.015
TUIK (2019) Turkish Statistical institute, main statistics, population and demography. https://www.turkstat.gov.tr/UstMenu.do?metod=temelist#. Accessed 21 Mar 2020
Vaidya OS, Kumar S (2006) Analytic hierarchy process: an overview of applications. Eur J Oper Res 169:1–29. https://doi.org/10.1016/j.ejor.2004.04.028
van de Kaa G, Fens T, Rezaei J (2019) Residential grid storage technology battles: a multi-criteria analysis using BWM. Technol Anal Strateg Manag 31:40–52. https://doi.org/10.1080/09537325.2018.1484441
Wang W (2019) Site selection of fire stations in cities based on geographic information system and fuzzy analytic hierarchy process. Ing des Syst d Inf 24:619–626. https://doi.org/10.18280/isi.240609
Wu Y, Yan Y, Wang S et al (2019) Study on location decision framework of agroforestry biomass cogeneration project: a case of China. Biomass Bioenerg 127:105289. https://doi.org/10.1016/j.biombioe.2019.105289
Yao, Zhang X, Murray AT (2019) Location optimization of urban fire stations: access and service coverage. Comput Environ Urban Syst 73:184–190. https://doi.org/10.1016/j.compenvurbsys.2018.10.006
You X, Chen T, Yang Q (2016) Approach to multi-criteria group decision-making problems based on the best-worst-method and electre method. Symmetry 8:95. https://doi.org/10.3390/sym8090095
Zhao H, Guo S, Zhao H (2018) Selecting the optimal micro-grid planning program using a novel multi-criteria decision making model based on grey cumulative prospect theory. Energies 11:1840. https://doi.org/10.3390/en11071840
Zolfani SH, Chatterjee P (2019) Comparative evaluation of sustainable design based on step-wiseweight assessment ratio analysis (SWARA) and Best Worst method (BWM) methods: a perspective on household furnishing materials. Symmetry (Basel) 11:74. https://doi.org/10.3390/sym11010074
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The authors sincerely thank Associate Professor Himmet Karaman for his support rendered and assistance with providing some of the data used in this research.
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Nyimbili, P.H., Erden, T. Comparative evaluation of GIS-based best–worst method (BWM) for emergency facility planning: perspectives from two decision-maker groups. Nat Hazards 105, 1031–1067 (2021). https://doi.org/10.1007/s11069-020-04348-3
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DOI: https://doi.org/10.1007/s11069-020-04348-3