Abstract
This study investigates the criteria affecting the location of humanitarian logistics distribution centers in the Sakarya province of Turkey, an area prone to natural disasters. The study identifies potential distribution center locations and uses the Best-Worst Method (BWM) to determine criteria such as population, distance to major highways and airports, public transportation availability, natural disaster risk, and suitable infrastructure. BWM is used to assign weights to each criterion and rank them based on their importance. The Additive Ratio Assessment (ARAS) method is then used to evaluate potential distribution center locations based on the established criteria. Disaster management experts and academicians provide their opinions through an online and face-to-face survey. Based on the results, Adapazarı is identified as the most suitable district for a humanitarian logistics distribution center. The study highlights the importance of considering multiple criteria when selecting distribution center locations and provides a framework for using multi-criteria decision-making methods in logistics planning. Disaster managers and policymakers can use the results to make informed decisions about the location of humanitarian logistics distribution centers.
Weights of main criteria according to decision-makers (DM).
| Location | Transportation modes | Cost | Cooperation | Ksi* | |
|---|---|---|---|---|---|
| DM1 | 0.529 | 0.305 | 0.102 | 0.064 | 0.080 |
| DM2 | 0.533 | 0.308 | 0.056 | 0.103 | 0.042 |
| DM3 | 0.466 | 0.259 | 0.172 | 0.103 | 0.052 |
| DM4 | 0.466 | 0.259 | 0.103 | 0.172 | 0.052 |
| DM5 | 0.500 | 0.294 | 0.059 | 0.147 | 0.088 |
| DM6 | 0.649 | 0.148 | 0.080 | 0.123 | 0.091 |
| DM7 | 0.259 | 0.466 | 0.172 | 0.103 | 0.052 |
| DM8 | 0.273 | 0.091 | 0.500 | 0.136 | 0.045 |
| DM9 | 0.259 | 0.466 | 0.172 | 0.103 | 0.052 |
| DM10 | 0.475 | 0.188 | 0.055 | 0.282 | 0.088 |
| DM11 | 0.466 | 0.259 | 0.172 | 0.103 | 0.052 |
| DM12 | 0.466 | 0.259 | 0.172 | 0.103 | 0.052 |
| DM13 | 0.485 | 0.265 | 0.176 | 0.074 | 0.044 |
| DM14 | 0.510 | 0.315 | 0.126 | 0.049 | 0.119 |
| Weighted average | 0.452 | 0.277 | 0.151 | 0.119 | 0.065 |
| Sub-criteria weight | 0.113 | 0.102 | 0.112 | 0.057 | 0.024 |
The weights of the sub-criteria of the location criterion according to the decision-makers.
| Geographical location | Proximity to residential area | Disaster-free location | Ksi* | |
|---|---|---|---|---|
| DM1 | 0.167 | 0.292 | 0.542 | 0.042 |
| DM2 | 0.583 | 0.111 | 0.306 | 0.119 |
| DM3 | 0.333 | 0.167 | 0.500 | 0.167 |
| DM4 | 0.292 | 0.542 | 0.167 | 0.042 |
| DM5 | 0.313 | 0.125 | 0.563 | 0.063 |
| DM6 | 0.167 | 0.542 | 0.292 | 0.042 |
| DM7 | 0.167 | 0.292 | 0.542 | 0.042 |
| DM8 | 0.292 | 0.542 | 0.167 | 0.042 |
| DM9 | 0.292 | 0.542 | 0.167 | 0.042 |
| DM10 | 0.563 | 0.125 | 0.313 | 0.063 |
| DM11 | 0.292 | 0.167 | 0.542 | 0.042 |
| DM12 | 0.542 | 0.292 | 0.167 | 0.042 |
| DM13 | 0.542 | 0.167 | 0.292 | 0.042 |
| DM14 | 0.542 | 0.167 | 0.292 | 0.042 |
| Weighted average | 0.363 | 0.291 | 0.346 | 0.059 |
| Sub-criteria weight | 0.164 | 0.132 | 0.157 | |
| Standard deviation | 0.158 | 0.176 | 0.158 |
The weights of the sub-criteria of the cost criterion according to the decision-makers.
| Operation and storage cost | Initial investment cost | Labor cost | Ksi* | |
|---|---|---|---|---|
| DM1 | 0.688 | 0.188 | 0.125 | 0.063 |
| DM2 | 0.700 | 0.100 | 0.200 | 0.100 |
| DM3 | 0.542 | 0.292 | 0.167 | 0.042 |
| DM4 | 0.292 | 0.542 | 0.167 | 0.042 |
| DM5 | 0.700 | 0.100 | 0.200 | 0.100 |
| DM6 | 0.688 | 0.188 | 0.125 | 0.063 |
| DM7 | 0.292 | 0.542 | 0.167 | 0.042 |
| DM8 | 0.167 | 0.542 | 0.292 | 0.042 |
| DM9 | 0.542 | 0.292 | 0.167 | 0.042 |
| DM10 | 0.700 | 0.100 | 0.200 | 0.100 |
| DM11 | 0.542 | 0.292 | 0.167 | 0.042 |
| DM12 | 0.542 | 0.292 | 0.167 | 0.042 |
| DM13 | 0.808 | 0.108 | 0.083 | 0.058 |
| DM14 | 0.197 | 0.712 | 0.091 | 0.076 |
| Weighted average | 0.528 | 0.306 | 0.165 | 0.061 |
| Sub-criteria weight | 0.080 | 0.046 | 0.025 | |
| Standard deviation | 0.210 | 0.201 | 0.052 |
The weights of the sub-criteria of the transportation modes criterion according to the decision-makers.
| Access to airport | Access to port | Access to road | Access to railway | Ksi* | |
|---|---|---|---|---|---|
| DM1 | 0.474 | 0.066 | 0.184 | 0.276 | 0.079 |
| DM2 | 0.142 | 0.059 | 0.561 | 0.237 | 0.151 |
| DM3 | 0.466 | 0.172 | 0.259 | 0.103 | 0.052 |
| DM4 | 0.084 | 0.218 | 0.536 | 0.163 | 0.117 |
| DM5 | 0.259 | 0.172 | 0.466 | 0.103 | 0.052 |
| DM6 | 0.071 | 0.110 | 0.666 | 0.154 | 0.102 |
| DM7 | 0.103 | 0.172 | 0.466 | 0.259 | 0.052 |
| DM8 | 0.603 | 0.172 | 0.466 | 0.259 | 0.052 |
| DM9 | 0.103 | 0.172 | 0.466 | 0.259 | 0.052 |
| DM10 | 0.181 | 0.078 | 0.596 | 0.145 | 0.130 |
| DM11 | 0.084 | 0.163 | 0.536 | 0.218 | 0.117 |
| DM12 | 0.273 | 0.455 | 0.182 | 0.091 | 0.091 |
| DM13 | 0.055 | 0.188 | 0.475 | 0.282 | 0.088 |
| DM14 | 0.068 | 0.117 | 0.714 | 0.102 | 0.102 |
| Weighted average | 0.212 | 0.165 | 0.469 | 0.189 | 0.088 |
| Sub-criteria weight | 0.059 | 0.046 | 0.130 | 0.052 | |
| Standard deviation | 0.180 | 0.097 | 0.162 | 0.073 |
The weights of the sub-criteria of the cooperation criterion according to the decision-makers.
| Public institutions | National non-governmental organizations | Logistics service providers | Universities and research centers | Ksi* | |
|---|---|---|---|---|---|
| DM1 | 0.295 | 0.098 | 0.538 | 0.069 | 0.052 |
| DM2 | 0.182 | 0.273 | 0.485 | 0.061 | 0.061 |
| DM3 | 0.466 | 0.172 | 0.103 | 0.259 | 0.052 |
| DM4 | 0.273 | 0.182 | 0.455 | 0.091 | 0.091 |
| DM5 | 0.322 | 0.107 | 0.525 | 0.045 | 0.119 |
| DM6 | 0.607 | 0.071 | 0.143 | 0.179 | 0.107 |
| DM7 | 0.466 | 0.103 | 0.172 | 0.259 | 0.052 |
| DM8 | 0.273 | 0.182 | 0.091 | 0.455 | 0.091 |
| DM9 | 0.103 | 0.172 | 0.466 | 0.259 | 0.052 |
| DM10 | 0.150 | 0.071 | 0.301 | 0.478 | 0.124 |
| DM11 | 0.268 | 0.179 | 0.472 | 0.081 | 0.065 |
| DM12 | 0.466 | 0.103 | 0.259 | 0.172 | 0.052 |
| DM13 | 0.550 | 0.080 | 0.319 | 0.051 | 0.089 |
| DM14 | 0.593 | 0.185 | 0.148 | 0.074 | 0.148 |
| Weighted average | 0.358 | 0.141 | 0.320 | 0.181 | 0.082 |
| Sub-criteria weight | 0.043 | 0.017 | 0.038 | 0.022 | |
| Standard deviation | 0.165 | 0.059 | 0.168 | 0.145 |
References
AFAD. 2022. “AFAD IRAP.” 2022. https://www.afad.gov.tr/il-planlari.Suche in Google Scholar
Agarwal, S., R. Kant, and R. Shankar. 2022. “Exploring Sustainability Balanced Scorecard for Performance Evaluation of Humanitarian Organizations.” Cleaner Logistics and Supply Chain 3 (March): 100026. https://doi.org/10.1016/j.clscn.2021.100026.Suche in Google Scholar
Ahmad, S., A. Ahmad, and F. Talib. 2020. “Lean-Green Performance Management in Indian SMEs: A Novel Perspective Using the Best-Worst Method Approach.” Benchmarking: An International Journal 28 (2): 737–65, https://doi.org/10.1108/bij-05-2020-0255.Suche in Google Scholar
Ahmad, W. N. K. W., J. Rezaei, S. Sadaghiani, and L. A. Tavasszy. 2017. “Evaluation of the External Forces Affecting the Sustainability of Oil and Gas Supply Chain Using Best Worst Method.” Journal of Cleaner Production 153: 242–52. https://doi.org/10.1016/j.jclepro.2017.03.166.Suche in Google Scholar
Ahmadi, H. B., S. Kusi-Sarpong, and J. Rezaei. 2017. “Assessing the Social Sustainability of Supply Chains Using Best Worst Method.” Resources, Conservation and Recycling 126: 99–106. https://doi.org/10.1016/j.resconrec.2017.07.020.Suche in Google Scholar
Ak, M. F., and D. Acar. 2021. “İnsani Yardım Tedarik Zinciri Depo Yer Seçimi: ÇKKV Metodolojisi Temelli Bir Örnek Olay İncelemesi.” European Journal of Science and Technology 22 (1): 400–9, https://doi.org/10.31590/ejosat.849896.Suche in Google Scholar
Alidoosti, Z., A. Sadegheih, K. Govindan, M. S. Pishvaee, A. Mostafaeipour, and A. K. Hossain. 2021. “Social Sustainability of Treatment Technologies for Bioenergy Generation from the Municipal Solid Waste Using Best Worst Method.” Journal of Cleaner Production 288 (March): 125592. https://doi.org/10.1016/j.jclepro.2020.125592.Suche in Google Scholar
Amoozad Mahdiraji, H., S. Arzaghi, G. Stauskis, and E. Zavadskas. 2018. “A Hybrid Fuzzy BWM-COPRAS Method for Analyzing Key Factors of Sustainable Architecture.” Sustainability 10 (5): 1626. https://doi.org/10.3390/su10051626.Suche in Google Scholar
Asadabadi, M. R., H. Badri Ahmadi, H. Gupta, and J. J. H. Liou. 2023. “Supplier Selection to Support Environmental Sustainability: The Stratified BWM TOPSIS Method.” Annals of Operations Research 322 (1): 321–44. https://doi.org/10.1007/s10479-022-04878-y.Suche in Google Scholar
Bahrami, Y., H. Hassani, and A. Maghsoudi. 2019. “BWM–ARAS: A New Hybrid MCDM Method for Cu Prospectivity Mapping in the Abhar Area, NW Iran.” Spatial Statistics 33 (October): 100382. https://doi.org/10.1016/j.spasta.2019.100382.Suche in Google Scholar
Baltzopoulos, G., E. Chioccarelli, P. Cito, and R. Baraschino. 2023. “Preliminary Engineering Report on Ground Motion Data of the Feb. 2023 Turkey Seismic Sequence.” Earthquake Reports.Suche in Google Scholar
Boakai, S., and F. Samanlioglu. 2023. “An MCDM Approach to Third Party Logistics Provider Selection.” International Journal of Logistics Systems and Management 44 (3): 283. https://doi.org/10.1504/IJLSM.2023.129365.Suche in Google Scholar
Boltürk, E., S. Ç. Onar, B. Öztayşi, and C. Kahraman. 2016. “Multi-Attribute Warehouse Location Selection in Humanitarian Logistics Using Hesitant Fuzzy AHP.” International Journal of the Analytic Hierarchy Process 8 (2): 271–98, https://doi.org/10.13033/ijahp.v8i2.387.Suche in Google Scholar
Budak, A., İ. Kaya, A. Karaşan, and M. Erdoğan. 2020. “Real-Time Location Systems Selection by Using a Fuzzy MCDM Approach: An Application in Humanitarian Relief Logistics.” Applied Soft Computing 92 (July): 106322. https://doi.org/10.1016/j.asoc.2020.106322.Suche in Google Scholar
Büyüközkan, G., and F. Göçer. 2018. “An Extension of ARAS Methodology under Interval Valued Intuitionistic Fuzzy Environment for Digital Supply Chain.” Applied Soft Computing 69 (August): 634–54. https://doi.org/10.1016/j.asoc.2018.04.040.Suche in Google Scholar
Carter, W. N. 2008. Disaster Management: A Disaster Manager’s Handbook. Manila: Asian Development Bank.Suche in Google Scholar
Celik, E., M. Yucesan, and M. Gul. 2021. “Green Supplier Selection for Textile Industry: A Case Study Using BWM-TODIM Integration under Interval Type-2 Fuzzy Sets.” Environmental Science and Pollution Research 28 (45): 64793–817. https://doi.org/10.1007/s11356-021-13832-7.Suche in Google Scholar
Cheraghalipour, A., M. M. Paydar, and M. H. Keshteli. 2018. “Applying a Hybrid BWM-VIKOR Approach to Supplier Selection: A Case Study in the Iranian Agricultural Implements Industry.” International Journal of Applied Decision Sciences 11 (3): 274. https://doi.org/10.1504/IJADS.2018.092796.Suche in Google Scholar
Coltman, T. R., T. M. Devinney, and B. W. Keating 2011. “Best-Worst Scaling Approach to Predict Customer Choice for 3PL Services: Customer Choice for 3PL Services.” Journal of Business Logistics 32 (2): 139–52. https://doi.org/10.1111/j.2158-1592.2011.01012.x.Suche in Google Scholar
CRED. 2022. “CRED.” Centre for Research on the Epidemiology of Disasters (CRED) 2021 Disasters in Numbers. 2022. https://cred.be/sites/default/files/2021_EMDAT_report.pdf.Suche in Google Scholar
Deng, F., Y. Li, H. Lin, J. Miao, and X. Liang. 2020. “A BWM-TOPSIS Hazardous Waste Inventory Safety Risk Evaluation.” International Journal of Environmental Research and Public Health 17 (16): 5765. https://doi.org/10.3390/ijerph17165765.Suche in Google Scholar
Dong, J., S. Wan, and S.-M. Chen. 2021. “Fuzzy Best-Worst Method Based on Triangular Fuzzy Numbers for Multi-Criteria Decision-Making.” Information Sciences 547 (February): 1080–104. https://doi.org/10.1016/j.ins.2020.09.014.Suche in Google Scholar
Ebrahimi, M., and M. Mirzayi Modam. 2016. “Selecting the Best Zones to Add New Emergency Services Based on a Hybrid Fuzzy MADM Method: A Case Study for Tehran.” Safety Science 85 (June): 67–76. https://doi.org/10.1016/j.ssci.2015.10.011.Suche in Google Scholar
Ecer, F. 2021. “Sustainability Assessment of Existing Onshore Wind Plants in the Context of Triple Bottom Line: A Best-Worst Method (BWM) Based MCDM Framework.” Environmental Science and Pollution Research 28 (16): 19677–93. https://doi.org/10.1007/s11356-020-11940-4.Suche in Google Scholar
Er Kara, M., and S. O. Fırat. 2018. “Supplier Risk Assessment Based on Best-Worst Method and K-Means Clustering: A Case Study.” Sustainability 10 (4): 1066. https://doi.org/10.3390/su10041066.Suche in Google Scholar
Fazlollahtabar, H., and N. Kazemitash. 2021. “Green Supplier Selection Based on the Information System Performance Evaluation Using the Integrated Best-Worst Method.” Facta Universitatis – Series: Mechanical Engineering 19 (3): 345–60. https://doi.org/10.22190/fume201125029f.Suche in Google Scholar
Foroughi, A., B. F. Moghaddam, M. H. Behzadi, and F. M. Sobhani. 2022. “Developing a Bi-objective Resilience Relief Logistic Considering Operational and Disruption Risks: A Post-Earthquake Case Study in Iran.” Environmental Science and Pollution Research 29 (37): 56323–40. https://doi.org/10.1007/s11356-022-18699-w.Suche in Google Scholar
Ghram, M., and H. Frikha. 2019. “Multiple Criteria Hierarchy Process within ARAS Method.” In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 995–1000. Paris: IEEE.10.1109/CoDIT.2019.8820401Suche in Google Scholar
Gupta, P., V. Chawla, V. Jain, and S. Angra. 2022. “Green Operations Management for Sustainable Development: An Explicit Analysis by Using Fuzzy Best-Worst Method.” Decision Science Letters 11 (3): 357–66. https://doi.org/10.5267/j.dsl.2022.1.003.Suche in Google Scholar
Hashemkhani Zolfani, S., R. Bazrafshan, F. Ecer, and Ç. Karamaşa. 2022. “The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America.” Mathematics 10 (14): 2401. https://doi.org/10.3390/math10142401.Suche in Google Scholar
Hu, Y., A. Al-Barakati, and P. Rani. 2022. “Investigating the Internet-Of-Things (Iot) Risks for Supply Chain Management Using Q-Rung Orthopair Fuzzy-SWARA-ARAS Framework.” Technological and Economic Development of Economy 0 (0): 1–26. https://doi.org/10.3846/tede.2022.16583.Suche in Google Scholar
Karbalaei Ramezanali, A., F. Feizi, A. Jafarirad, and M. Lotfi. 2020. “Application of Best-Worst Method and Additive Ratio Assessment in Mineral Prospectivity Mapping: A Case Study of Vein-type Copper Mineralization in the Kuhsiah-E-Urmak Area, Iran.” Ore Geology Reviews 117 (February): 103268. https://doi.org/10.1016/j.oregeorev.2019.103268.Suche in Google Scholar
Khan, S. A., S. Kusi-Sarpong, I. Naim, H. B. Ahmadi, and A. Oyedijo. 2022. “A Best-Worst-Method-Based Performance Evaluation Framework for Manufacturing Industry.” Kybernetes 51 (10): 2938–63. https://doi.org/10.1108/K-03-2021-0202.Suche in Google Scholar
Köfteci̇, S, and H. Gerçek. 2010. “Yük Taşımacılığında Taşıma Türü Seçimi İçin Lojistik Maliyetlere Dayalı İkili Lojit Model.” Teknik Dergi 21 (103): 5087–112.Suche in Google Scholar
Li, J., L.-L. Niu, Q. Chen, and Z.-x. Wang. 2021. “Approaches for Multicriteria Decision-Making Based on the Hesitant Fuzzy Best–Worst Method.” Complex & Intelligent Systems 7 (5): 2617–34. https://doi.org/10.1007/s40747-021-00406-w.Suche in Google Scholar
Liao, C.-N., Y.-K. Fu, and Li-C. Wu. 2015. “Integrated FAHP, ARAS-F and MSGP Methods for Green Supplier Evaluation and Selection.” Technological and Economic Development of Economy 22 (5): 651–69. https://doi.org/10.3846/20294913.2015.1072750.Suche in Google Scholar
Liao, H., Z. Wen, and L. Liu. 2019. “Integrating BWM and ARAS under Hesitant Linguistic Environment for Digital Supply Chain Finance Supplier Section.” Technological and Economic Development of Economy 25 (6): 1188–212. https://doi.org/10.3846/tede.2019.10716.Suche in Google Scholar
Linardos, V., M. Drakaki, P. Tzionas, and Y. Karnavas. 2022. “Machine Learning in Disaster Management: Recent Developments in Methods and Applications.” Machine Learning and Knowledge Extraction 4 (2): 446–73. https://doi.org/10.3390/make4020020.Suche in Google Scholar
Liu, K. 2022. “GIS-based MCDM Framework Combined with Coupled Multi-Hazard Assessment for Site Selection of Post-Earthquake Emergency Medical Service Facilities in Wenchuan, China.” International Journal of Disaster Risk Reduction 73 (April): 102873. https://doi.org/10.1016/j.ijdrr.2022.102873.Suche in Google Scholar
Liu, N., and Z. Xu. 2021. “An Overview of ARAS Method: Theory Development, Application Extension, and Future Challenge.” International Journal of Intelligent Systems 36 (7): 3524–65. https://doi.org/10.1002/int.22425.Suche in Google Scholar
López, C., A. Ishizaka, M. Gul, M. Yücesan, and D. Valencia. 2022. “A Calibrated Fuzzy Best-Worst-Method to Reinforce Supply Chain Resilience during the COVID 19 Pandemic.” Journal of the Operational Research Society 2022: 1–24, https://doi.org/10.1080/01605682.2022.2122739.Suche in Google Scholar
Maharjan, R., and S. Hanaoka. 2017. “Warehouse Location Determination for Humanitarian Relief Distribution in Nepal.” Transportation Research Procedia 25: 1151–63. https://doi.org/10.1016/j.trpro.2017.05.128.Suche in Google Scholar
Malek, J., and T. N. Desai. 2019. “Prioritization of Sustainable Manufacturing Barriers Using Best Worst Method.” Journal of Cleaner Production 226 (July): 589–600. https://doi.org/10.1016/j.jclepro.2019.04.056.Suche in Google Scholar
Mohaghar, A., I. G. Sahebi, and A. Arab. 2017. “Appraisal of Humanitarian Supply Chain Risks Using Best-Worst Method.” International Journal of Industrial and Manufacturing Engineering 11 (2): 349–54.Suche in Google Scholar
Paciarotti, C., W. D. Piotrowicz, and F. George. 2021. “Humanitarian Logistics and Supply Chain Standards. Literature Review and View from Practice.” Journal of Humanitarian Logistics and Supply Chain Management 11 (3): 550–73. https://doi.org/10.1108/JHLSCM-11-2020-0101.Suche in Google Scholar
Patil, A., V. Shardeo, A. Dwivedi, and J. Madaan. 2021. “An Integrated Approach to Model the Blockchain Implementation Barriers in Humanitarian Supply Chain.” Journal of Global Operations and Strategic Sourcing 14 (1): 81–103. https://doi.org/10.1108/JGOSS-07-2020-0042.Suche in Google Scholar
Peker, İ., S. Korucuk, Ş. Ulutaş, B. S. Okatan, and F. Yaşar. 2016. “Afet Lojistiği Kapsaminda En Uygun Dağitim Merkez Yerinin AHS-VIKOR Bütünleşik Yöntemi Ile Belirlenmesi: Erzincan İli Örneği.” Journal of Management and Economics Research 14 (1): 82–103. https://doi.org/10.11611/jmer728.Suche in Google Scholar
Rejeb, A., K. Rejeb, J. G. Keogh, and S. Zailani. 2022. “Barriers to Blockchain Adoption in the Circular Economy: A Fuzzy Delphi and Best-Worst Approach.” Sustainability 14 (6): 3611. https://doi.org/10.3390/su14063611.Suche in Google Scholar
Rezaei, J. 2015. “Best-Worst Multi-Criteria Decision-Making Method.” Omega 53 (June): 49–57. https://doi.org/10.1016/j.omega.2014.11.009.Suche in Google Scholar
Rezaei, J., T. Nispeling, J. Sarkis, and L. Tavasszy. 2016. “A Supplier Selection Life Cycle Approach Integrating Traditional and Environmental Criteria Using the Best Worst Method.” Journal of Cleaner Production 135 (November): 577–88. https://doi.org/10.1016/j.jclepro.2016.06.125.Suche in Google Scholar
Roh, S. Y., Y. R. Shin, and Y. J. Seo. 2018. “The Pre-positioned Warehouse Location Selection for International Humanitarian Relief Logistics.” The Asian Journal of Shipping and Logistics 34 (4): 297–307. https://doi.org/10.1016/j.ajsl.2018.12.003.Suche in Google Scholar
Roh, S.-Y., H.-M. Jang, and C.-H. Han. 2013. “Warehouse Location Decision Factors in Humanitarian Relief Logistics.” The Asian Journal of Shipping and Logistics 29 (1): 103–20. https://doi.org/10.1016/j.ajsl.2013.05.006.Suche in Google Scholar
Roh, S., S. Pettit, I. Harris, and A. Beresford. 2015. “The Pre-positioning of Warehouses at Regional and Local Levels for a Humanitarian Relief Organisation.” International Journal of Production Economics 170 (December): 616–28. https://doi.org/10.1016/j.ijpe.2015.01.015.Suche in Google Scholar
Rostamzadeh, R., A. Esmaeili, A. S. Nia, J. Saparauskas, and M. Keshavarz-Ghorabaee. 2017. “A Fuzzy ARAS Method for Supply Chain Management Performance Measurement in SMEs under Uncertainty.” Transformations in Business and Economics 16 (December): 319–48.Suche in Google Scholar
Sahebi, I. G., A. Arab, and M. R. S. Moghadam. 2017. “Analyzing the Barriers to Humanitarian Supply Chain Management: A Case Study of the Tehran Red Crescent Societies.” International Journal of Disaster Risk Reduction 24: 232–41. https://doi.org/10.1016/j.ijdrr.2017.05.017.Suche in Google Scholar
Sahebi, I. G., B. Masoomi, and S. Ghorbani. 2020. “Expert Oriented Approach for Analyzing the Blockchain Adoption Barriers in Humanitarian Supply Chain.” Technology in Society 63: 101427. https://doi.org/10.1016/j.techsoc.2020.101427.Suche in Google Scholar
Sarabi, E. P., and S. A. Darestani. 2021. “Developing a Decision Support System for Logistics Service Provider Selection Employing Fuzzy MULTIMOORA & BWM in Mining Equipment Manufacturing.” Applied Soft Computing 98 (January): 106849. https://doi.org/10.1016/j.asoc.2020.106849.Suche in Google Scholar
Soner, O., E. Celik, and E. Akyuz. 2022. “A Fuzzy Best–Worst Method (BWM) to Assess the Potential Environmental Impacts of the Process of Ship Recycling.” Maritime Policy & Management 49 (3): 396–409. https://doi.org/10.1080/03088839.2021.1889066.Suche in Google Scholar
Tian, Z.-P., H.-Yu. Zhang, J.-Q. Wang, and T.-Li. Wang. 2018. “Green Supplier Selection Using Improved TOPSIS and Best-Worst Method under Intuitionistic Fuzzy Environment.” Informatica 29 (4): 773–800. https://doi.org/10.15388/informatica.2018.192.Suche in Google Scholar
Timperio, G., G. B. Panchal, A. Samvedi, M. Goh, and R. De Souza. 2017. “Decision Support Framework for Location Selection and Disaster Relief Network Design.” Journal of Humanitarian Logistics and Supply Chain Management 7 (3): 222–45. https://doi.org/10.1108/JHLSCM-11-2016-0040.Suche in Google Scholar
Tirkolaee, E. B., and A. E. Torkayesh. 2022. “A Cluster-Based Stratified Hybrid Decision Support Model under Uncertainty: Sustainable Healthcare Landfill Location Selection.” Applied Intelligence 52 (12): 13614–33. https://doi.org/10.1007/s10489-022-03335-4.Suche in Google Scholar
Torkayesh, A. E., B. Malmir, and M. Rajabi Asadabadi. 2021. “Sustainable Waste Disposal Technology Selection: The Stratified Best-Worst Multi-Criteria Decision-Making Method.” Waste Management 122 (March): 100–12. https://doi.org/10.1016/j.wasman.2020.12.040.Suche in Google Scholar
Trivedi, A. 2018. “A Multi-Criteria Decision Approach Based on DEMATEL to Assess Determinants of Shelter Site Selection in Disaster Response.” International Journal of Disaster Risk Reduction 31 (October): 722–8. https://doi.org/10.1016/j.ijdrr.2018.07.019.Suche in Google Scholar
Tu, Y., K. Chen, H. Wang, and Z. Li. 2020. “Regional Water Resources Security Evaluation Based on a Hybrid Fuzzy BWM-TOPSIS Method.” International Journal of Environmental Research and Public Health 17 (14): 4987. https://doi.org/10.3390/ijerph17144987.Suche in Google Scholar
Tuzkaya, U. R., K. B. Yilmazer, and G. Tuzkaya. 2015. “An Integrated Methodology for the Emergency Logistics Centers Location Selection Problem and its Application for the Turkey Case.” Journal of Homeland Security and Emergency Management 12 (1): 121–44, https://doi.org/10.1515/jhsem-2013-0107.Suche in Google Scholar
Yadav, A. K., and D. Kumar. 2023. “A LAG-Based Framework to Overcome the Challenges of the Sustainable Vaccine Supply Chain: An Integrated BWM–MARCOS Approach.” Journal of Humanitarian Logistics and Supply Chain Management 13 (2): 173–98, https://doi.org/10.1108/JHLSCM-09-2021-0091.Suche in Google Scholar
Yadav, G., S. K. Mangla, S. Luthra, and J. Suresh. 2018. “Hybrid BWM-ELECTRE-Based Decision Framework for Effective Offshore Outsourcing Adoption: A Case Study.” International Journal of Production Research 56 (18): 6259–78. https://doi.org/10.1080/00207543.2018.1472406.Suche in Google Scholar
Yazdi, M., A. Nedjati, E. Zarei, and R. Abbassi. 2020. “A Reliable Risk Analysis Approach Using an Extension of Best-Worst Method Based on Democratic-Autocratic Decision-Making Style.” Journal of Cleaner Production 256 (May): 120418. https://doi.org/10.1016/j.jclepro.2020.120418.Suche in Google Scholar
Yılmaz, H., and Ö. Kabak. 2020. “Prioritizing Distribution Centers in Humanitarian Logistics Using Type-2 Fuzzy MCDM Approach.” Journal of Enterprise Information Management 33 (5): 1199–232. https://doi.org/10.1108/JEIM-09-2019-0310.Suche in Google Scholar
You, P., S. Guo, H. Zhao, and H. Zhao. 2017. “Operation Performance Evaluation of Power Grid Enterprise Using a Hybrid BWM-TOPSIS Method.” Sustainability 9 (12): 2329. https://doi.org/10.3390/su9122329.Suche in Google Scholar
Yucesan, M., M. Gul, and E. Celik. 2021. “A Holistic FMEA Approach by Fuzzy-Based Bayesian Network and Best-Worst Method.” Complex & Intelligent Systems 7 (3): 1547–64. https://doi.org/10.1007/s40747-021-00279-z.Suche in Google Scholar
Zavadskas, E. K., and Z. Turskis. 2010. “A New Additive Ratio Assessment (ARAS) Method in Multicriteria Decision-Making.” Technological and Economic Development of Economy 16 (2): 159–72. https://doi.org/10.3846/tede.2010.10.Suche in Google Scholar
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- Crisis as Opportunities for Robust Government: A Systematic Review of Policy Process Literature
- Evaluating the Quality of State Hazard Mitigation Plans Based on Hazard Identification, Risk, and Vulnerability Assessments
- The Homeland Kaleidoscope: Perceptions of Threats and Coping Among Israeli Civilians in a Diversity of Conflict Zones
- Distribution Center Location Selection in Humanitarian Logistics Using Hybrid BWM–ARAS: A Case Study in Türkiye
- Associations Between Public Service Motivation, Depression and Anxiety Among Firefighters: A Chain Mediation Model of Employee Resilience and Job Satisfaction
- In Defense of Disinformation
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Crisis as Opportunities for Robust Government: A Systematic Review of Policy Process Literature
- Evaluating the Quality of State Hazard Mitigation Plans Based on Hazard Identification, Risk, and Vulnerability Assessments
- The Homeland Kaleidoscope: Perceptions of Threats and Coping Among Israeli Civilians in a Diversity of Conflict Zones
- Distribution Center Location Selection in Humanitarian Logistics Using Hybrid BWM–ARAS: A Case Study in Türkiye
- Associations Between Public Service Motivation, Depression and Anxiety Among Firefighters: A Chain Mediation Model of Employee Resilience and Job Satisfaction
- In Defense of Disinformation