A critical review of best‒worst method applications in the transportation and logistics industry

Mollaoglu, Mahmut; Gul, Muhammet

doi:10.17270/J.LOG.001132

Artykuł - szczegóły

Tytuł artykułu

A critical review of best‒worst method applications in the transportation and logistics industry

Autorzy

Mollaoglu Mahmut , Gul Muhammet

Treść / Zawartość

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Identyfikatory

DOI

10.17270/J.LOG.001132

Warianty tytułu

Języki publikacji

Abstrakty

Background: The Best-worst Method (BWM) has been successfully applied in various fields since it was first proposed in 2015, with numerous extensions developed over time. Its advantages over other pairwise comparison-based multi-criteria decision-making (MCDM) methods-such as requiring fewer pairwise comparisons and providing more consistent evaluations-make it preferable. The primary motivation for this article stems from the fact that no comprehensive review of the method has been published since 2019. The reason for focusing specifically on the “Transportation and Logistics” field is the significant increase in BWM applications within this sector and the large number of BWM studies conducted since the last review article in 2019. Methods: This article provides a bibliometric analysis using VOSviewer visualizations, complemented by a robust interpretation and inference analysis that explores in-depth connections between studies. Specifically, the analysis covers key statistical aspects, including the specific issues to which BWM is applied in the transportation and logistics field, publication trends, methods with which it is integrated, and the concepts (such as fuzzy set, rough set, neutrosophy, stratification etc.) with which it is commonly used. Additionally, the study examines the journals in which these studies are published and the distribution of studies across different countries. Results: The study revealed that several key areas within the transportation and logistics industry, such as occupational health, personnel selection, and the effects of pandemics, remain underexplored. Conclusion: The article highlights emerging research opportunities within the transportation and logistics sector and explores various ideas for the applications of BWM extensions. It also discusses activities, software, books and events related to BWM. The study provides an overview of the current state of BWM applications in transport and logistics and serves as a guide for potential future research.

Słowa kluczowe

best-worst method decision-making process extensions and applications transportation and logistics bibliometric analysis survey

metoda najlepszy-najgorszy proces decyzyjny rozszerzenia i zastosowania transport i logistyka analiza bibliometryczna ankieta

Wydawca

Wyższa Szkoła Logistyki

Czasopismo

LogForum

Rocznik

2025

Tom

Vol. 21, no 1

Strony

13--33

Opis fizyczny

Bibliogr. 100 poz., rys., wykr.

Twórcy

autor

Mollaoglu Mahmut

mahmut.mollaoglu@beun.edu.tr

Maritime Faculty, Zonguldak Bülent Ecevit University, Zonguldak, Turkey
Institute of Social Sciences, Istanbul University, Istanbul, Turkey

autor

Gul Muhammet

muhammetgul@istanbul.edu.tr

Faculty of Transportation and Logistics, İstanbul University, Istanbul, Turkey

https://orcid.org/0000-0002-5319-4289

Bibliografia

1. Aboutorab, H., Saberi, M., Asadabadi, M. R., Hussain, O., & Chang, E. (2018). ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Systems with Applications, 107, 115-125. https://doi.org/10.1016/j.eswa.2018.04.015
2. Ak, M. F., Yucesan, M., & Gul, M. (2022). Occupational health, safety and environmental risk assessment in textile production industry through a Bayesian BWM-VIKOR approach. Stochastic Environmental Research and Risk Assessment, 36(2), 629-642. https://doi.org/10.1007/s00477-021-02069-y
3. Ali, A., & Rashid, T. (2019). Hesitant fuzzy best‐worst multi‐criteria decision‐making method and its applications. International Journal of Intelligent Systems, 34(8), 1953-1967. https://doi.org/10.1002/int.22131
4. Ali, S. S., & Kaur, R. (2021). Effectiveness of corporate social responsibility (CSR) in implementation of social sustainability in warehousing of developing countries: A hybrid approach. Journal of Cleaner Production, 324, 129154. https://doi.org/10.1016/j.jclepro.2021.129154
5. Ali, S. S., Kaur, R., & Khan, S. (2022). Evaluating sustainability initiatives in warehouse for measuring sustainability performance: An emerging economy perspective. Annals of Operations Research, 324(1), 1-40. https://doi.org/10.1007/s10479-021-04454-w
6. Arvidsson, N., Woxenius, J., & Lammgård, C. (2013). Review of road hauliers' measures for increasing transport efficiency and sustainability in urban freight distribution. Transport Reviews, 33(1), 107-127. https://doi.org/10.1080/01441647.2013.763866
7. Baki, R. (2021). An integrated, multi-criteria approach based on environmental, economic, social, and competency criteria for supplier selection. RAIRO-Operations Research, 55(3), 1487-1500. https://doi.org/10.1051/ro/2021041
8. Başhan, V., Demirel, H., & Celik, E. (2022). Evaluation of critical problems of heavy fuel oil separators on ships by best-worst method. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 14750902221097268. https://doi.org/10.1177/14750902221097268
9. Başhan, V., Yucesan, M., Demirel, H., & Gul, M. (2022). Health, safety, and environmental failure evaluation by hybridizing fuzzy multi-attribute decision-making methods for maritime scrubber systems. Environmental Monitoring and Assessment, 194(9), 1-23. https://doi.org/10.1007/s10661-022-10284-5
10. Baskak, D., Ozbey, S., Yucesan, M., & Gul, M. (2023). COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model. Environmental Science and Pollution Research, 30(3), 8133-8153. https://doi.org/10.1007/s11356-022-22796-1
11. Bernhofen, D. M., El-Sahli, Z., & Kneller, R. (2016). Estimating the effects of the container revolution on world trade. Journal of International Economics, 98, 36-50. https://doi.org/10.1016/j.jinteco.2015.09.001
12. Brunelli, M., & Rezaei, J. (2019). A multiplicative best–worst method for multi-criteria decision making. Operations Research Letters, 47(1), 12-15. https://doi.org/10.1016/j.orl.2018.11.008
13. Çelikbilek, Y., Moslem, S., & Duleba, S. (2022). A combined grey multi criteria decision making model to evaluate public transportation systems. Evolving Systems, 14(1) 1-15. https://doi.org/10.1007/s12530-021-09414-0
14. Daraio, C., Diana, M., Di Costa, F., Leporelli, C., Matteucci, G., & Nastasi, A. (2016). Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research. European Journal of Operational Research, 248(1), 1-20. https://doi.org/10.1016/j.ejor.2015.05.059
15. Dehnavi, M. N., Yazdian, S. A., & Sadjadi, S. J. (2023). Evaluating effective criteria on customer satisfaction using the best-worst method and optimizing resource allocation, case study Iran Aseman Airlines. Journal of Air Transport Management, 109, 102375. https://doi.org/10.1016/j.jairtraman.2023.102375
16. Demirel, H., Şener, B., Yildiz, B., & Balin, A. (2020). A real case study on the selection of suitable roll stabilizer type for motor yachts using hybrid fuzzy AHP and VIKOR methodology. Ocean Engineering, 217, 108125. https://doi.org/10.1016/j.oceaneng.2020.108125
17. Dong, J., Wan, S., & Chen, S. M. (2021). Fuzzy best-worst method based on triangular fuzzy numbers for multi-criteria decision-making. Information Sciences, 547, 1080-1104. https://doi.org/10.1016/j.ins.2020.09.014
18. 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. https://doi.org/10.1016/j.jclepro.2020.121981
19. Farooq, D., Moslem, S., Jamal, A., Butt, F. M., Almarhabi, Y., Faisal Tufail, R., & Almoshaogeh, M. (2021). Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model. International Journal of Environmental Research and Public Health, 18(20), 10628. https://doi.org/10.3390/ijerph182010628
20. Fartaj, S. R., Kabir, G., Eghujovbo, V., Ali, S. M., & Paul, S. K. (2020). Modeling transportation disruptions in the supply chain of automotive parts manufacturing company. International Journal of Production Economics, 222, 107511. https://doi.org/10.1016/j.ijpe.2019.09.032
21. Fathi, M. R., Zamanian, A., & Khosravi, A. (2023). Mathematical modeling for sustainable agri-food supply chain. Environment, Development and Sustainability, 26(3) 1-34. https://doi.org/10.1007/s10668-023-02992-w
22. Ganji, S. S., Ahangar, A. N., & Bandari, S. J. (2022). Evaluation of vehicular emissions reduction strategies using a novel hybrid method integrating BWM, Q methodology and ER approach. Environment, Development and Sustainability, 24(10), 11576-11614. https://doi.org/10.1007/s10668-021-01912-0
23. Ghadir, A. H., Vandchali, H. R., Fallah, M., & Tirkolaee, E. B. (2022). Evaluating the impacts of COVID-19 outbreak on supply chain risks by modified failure mode and effects analysis: a case study in an automotive company. Annals of Operations Research, 1-31. https://doi.org/10.1007/s10479-022-04651-1
24. Gudanowska, A. E. (2017). Modern research trends within technology management in the light of selected publications. Procedia Engineering, 182, 247-254. https://doi.org/10.1016/j.proeng.2017.03.185
25. Gul, M., & Yucesan, M. (2022). Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model. Socio-Economic Planning Sciences, 80, 101173. https://doi.org/10.1016/j.seps.2021.101173
26. Gul, M., Yucesan, M., & Ak, M. F. (2022). Control measure prioritization in Fine−Kinney-based risk assessment: a Bayesian BWM-Fuzzy VIKOR combined approach in an oil station. Environmental Science and Pollution Research, 29(39), 59385-59402. https://doi.org/10.1007/s11356-022-19454-x
27. Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31. https://doi.org/10.1016/j.knosys.2017.01.010
28. Gupta, A., & Singh, R. K. (2024). Applications of emerging technologies in logistics sector for achieving circular economy goals during COVID 19 pandemic: analysis of critical success factors. International Journal of Logistics Research and Applications, 1-22. https://doi.org/10.1080/13675567.2021.1985095
29. Gupta, H. (2018). Evaluating service quality of airline industry using hybrid best worst method and VIKOR. Journal of Air Transport Management, 68, 35-47. https://doi.org/10.1016/j.jairtraman.2017.06.001
30. Hafezalkotob, Arian, & Hafezalkotob, Ashkan. (2017). A novel approach for combination of individual and group decisions based on fuzzy best-worst method. Applied Soft Computing, 59, 316-325. https://doi.org/10.1016/j.asoc.2017.05.036
31. Hasan, M. G., Ashraf, Z., & Khan, M. F. (2022). Multi‐choice best‐worst multi‐criteria decision‐making method and its applications. International Journal of Intelligent Systems, 37(2), 1129-1156. https://doi.org/10.1002/int.22663
32. Hassan, N. M., & Abbasi, M. N. (2021). A review of supply chain integration extents, contingencies and performance: A post Covid-19 review. Operations Research Perspectives, 8, 100183. https://doi.org/10.1016/j.orp.2021.100183
33. Ishizaka, A., Khan, S. A., Kheybari, S., & Zaman, S. I. (2023). Supplier selection in closed loop pharma supply chain: a novel BWM–GAIA framework. Annals of Operations Research, 324(1-2), 13-36. https://doi.org/10.1007/s10479-022-04710-7
34. Kamran, M., Bian, J., Li, A., Lei, G., Nan, X., & Jin, Y. (2021). Investigating Eco-Environmental Vulnerability for China–Pakistan Economic Corridor Key Sector Punjab Using Multi-Sources Geo-Information. ISPRS International Journal of Geo-Information, 10(9), 625. https://doi.org/10.3390/ijgi10090625
35. Kaviani, M. A., Tavana, M., Kumar, A., Michnik, J., Niknam, R., & de Campos, E. A. R. (2020). An integrated framework for evaluating the barriers to successful implementation of reverse logistics in the automotive industry. Journal of Cleaner Production, 272, 122714. https://doi.org/10.1016/j.jclepro.2020.122714
36. Kavus, B. Y., Tas, P. G., Ayyildiz, E., & Taskin, A. (2022). A three-level framework to evaluate airline service quality based on interval valued neutrosophic AHP considering the new dimensions. Journal of Air Transport Management, 99, 102179. https://doi.org/10.1016/j.jairtraman.2021.102179
37. Kheybari, S., & Ishizaka, A. (2022). The behavioural best-worst method. Expert Systems with Applications, 209, 118265. https://doi.org/10.1016/j.eswa.2022.118265
38. Kheybari, S., Kazemi, M., & Rezaei, J. (2019). Bioethanol facility location selection using best-worst method. Applied Energy, 242, 612-623. https://doi.org/10.1016/j.apenergy.2019.03.054
39. Kheybari, S., Monfared, M. D., Salamirad, A., & Rezaei, J. (2023). Bioethanol sustainable supply chain design: A multi-attribute bi-objective structure. Computers & Industrial Engineering, 180, 109258. https://doi.org/10.1016/j.cie.2023.109258
40. Krstić, M. D., Tadić, S. R., Brnjac, N., & Zečević, S. (2019). Intermodal terminal handling equipment selection using a fuzzy multi-criteria decision-making model. Promet-Traffic&Transportation, 31(1), 89-100. https://doi.org/10.7307/ptt.v31i1.2949
41. Krstić, M., Elia, V., Agnusdei, G. P., De Leo, F., Tadić, S., & Miglietta, P. P. (2024). Evaluation of the agri-food supply chain risks: the circular economy context. British Food Journal, 126(1), 113-133. https://doi.org/10.1108/BFJ-12-2022-1116
42. Kumar, A., Mangla, S. K., Kumar, P., & Song, M. (2021). Mitigate risks in perishable food supply chains: Learning from COVID-19. Technological Forecasting and Social Change, 166, 120643. https://doi.org/10.1016/j.techfore.2021.120643
43. Lam, J. S. L., Cullinane, K. P. B., & Lee, P. T. W. (2018). The 21st-century Maritime Silk Road: challenges and opportunities for transport management and practice. Transport Reviews, 38(4), 413-415. https://doi.org/10.1080/01441647.2018.1453562
44. Liang, F., Brunelli, M., & Rezaei, J. (2020). Consistency issues in the best worst method: Measurements and thresholds. Omega, 96, 102175. https://doi.org/10.1016/j.omega.2019.102175
45. Liang, F., Brunelli, M., & Rezaei, J. (2022a). Best-worst Tradeoff method. Information Sciences, 610, 957-976. https://doi.org/10.1016/j.ins.2022.07.097
46. Liang, F., Brunelli, M., Septian, K., & Rezaei, J. (2021). Belief-Based Best Worst Method. International Journal of Information Technology & Decision Making, 20(01), 287-320. https://doi.org/10.1142/S0219622020500480
47. Liang, Y., Ju, Y., Tu, Y., & Rezaei, J. (2022b). Nonadditive best-worst method: Incorporating criteria interaction using the Choquet integral. Journal of the Operational Research Society, 1-12. https://doi.org/10.1080/01605682.2022.2096504
48. Liu, P., Zhu, B., & Yang, M. (2021). Has marine technology innovation promoted the high-quality development of the marine economy? Evidence from coastal regions in China. Ocean & Coastal Management, 209, 105695. https://doi.org/10.1016/j.ocecoaman.2021.105695
49. López, C., Ishizaka, A., Gul, M., Yücesan, M., & Valencia, D. (2022). A calibrated Fuzzy Best-Worst-method to reinforce supply chain resilience during the COVID 19 pandemic. Journal of the Operational Research Society, 1-24. https://doi.org/10.1080/01605682.2022.2122739
50. Masoumi, S., Hadji Molana, S. M., Javadi, M., & Azizi, A. (2022). Designing integrated model of decision-making-robust optimisation to manage the maintenance of inter-urban routes under uncertainty. International Journal of Pavement Engineering, 23(10), 3522-3535. https://doi.org/10.1080/10298436.2021.1904238
51. Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next? Omega, 87, 205-225. https://doi.org/10.1016/j.omega.2019.01.009
52. Mirghaderi, S. D., & Modiri, M. (2021). Application of meta-heuristic algorithm for multi-objective optimization of sustainable supply chain uncertainty. Sādhanā, 46(1), 1-23. https://doi.org/10.1007/s12046-020-01554-4
53. Mohammadi, M., & Rezaei, J. (2020a). Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods. Omega, 96, 102254. https://doi.org/10.1016/j.omega.2020.102254
54. Mohammadi, M., & Rezaei, J. (2020b). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96, 102075. https://doi.org/10.1016/j.omega.2019.06.001
55. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of Internal Medicine, 151(4), 264-269. https://doi.org/10.1016/j.ijsu.2010.02.007
56. Moslem, S., Alkharabsheh, A., Ismael, K., & Duleba, S. (2020). An integrated decision support model for evaluating public transport quality. Applied Sciences, 10(12), 4158. https://doi.org/10.3390/app10124158
57. Moslem, S., Duleba, S., & Esztergár-Kiss, D. (2022). Comparative mode choice analysis of university staff commuting travel preferences. European Journal of Transport & Infrastructure Research, 22(2), 83-107. https://doi.org/10.18757/ejtir.2022.22.2.5949
58. Moslem, S., Farooq, D., Ghorbanzadeh, O., & Blaschke, T. (2020). Application of the AHP-BWM model for evaluating driver behavior factors related to road safety: A case study for Budapest. Symmetry, 12(2), 243. https://doi.org/10.3390/sym12020243
59. Nazemzadeh, M., & Vanelslander, T. (2015). The container transport system: Selection criteria and business attractiveness for North-European ports. Maritime Economics & Logistics, 17(2), 221-245. https://doi.org/10.1057/mel.2015.1
60. Notteboom, T. E., & Haralambides, H. E. (2020). Port management and governance in a post-COVID-19 era: quo vadis? Maritime Economics & Logistics, 22(3), 329-352. https://doi.org/10.1057/s41278-020-00162-7
61. Novotná, M., Švadlenka, L., Jovčić, S., & Simić, V. (2022). Micro-hub location selection for sustainable last-mile delivery. Plos One, 17(7), e0270926. https://doi.org/10.1371/journal.pone.0270926
62. Omrani, H., Amini, M., & Alizadeh, A. (2020). An integrated group best-worst method–Data envelopment analysis approach for evaluating road safety: A case of Iran. Measurement, 152, 107330. https://doi.org/10.1016/j.measurement.2019.107330
63. Onstein, A. T., Ektesaby, M., Rezaei, J., Tavasszy, L. A., & van Damme, D. A. (2020). Importance of factors driving firms’ decisions on spatial distribution structures. International Journal of Logistics Research and Applications, 23(1), 24-43. https://doi.org/10.1080/13675567.2019.1574729
64. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 1-11. https://doi.org/10.1136/bmj.n71
65. Pamucar, D., Chatterjee, K., & Zavadskas, E. K. (2019). Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers. Computers & Industrial Engineering, 127, 383-407. https://doi.org/10.1016/j.cie.2018.10.023
66. Pamučar, D., Ecer, F., Cirovic, G., & Arlasheedi, M. A. (2020). Application of improved best worst method (BWM) in real-world problems. Mathematics, 8(8), 1342. https://doi.org/10.3390/math8081342
67. Paul, S. K., Chowdhury, P., Chowdhury, M. T., Chakrabortty, R. K., & Moktadir, M. A. (2021). Operational challenges during a pandemic: an investigation in the electronics industry. The International Journal of Logistics Management, 34(2), 336-362. https://doi.org/10.1108/IJLM-05-2021-0307
68. Rabbani, M., Momen, S., Akbarian-Saravi, N., Farrokhi-Asl, H., & Ghelichi, Z. (2020). Optimal design for sustainable bioethanol supply chain considering the bioethanol production strategies: A case study. Computers & Chemical Engineering, 134, 106720. https://doi.org/10.1016/j.compchemeng.2019.106720
69. Rajak, S., Mathiyazhagan, K., Agarwal, V., Sivakumar, K., Kumar, V., & Appolloni, A. (2022). Issues and analysis of critical success factors for the sustainable initiatives in the supply chain during COVID-19 pandemic outbreak in India: A case study. Research in Transportation Economics, 93, 101114. https://doi.org/10.1016/j.retrec.2021.101114
70. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
71. 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.2014.11.009
72. Rezaei, J. (2020). A concentration ratio for nonlinear best worst method. International Journal of Information Technology & Decision Making, 19(03), 891-907. https://doi.org/10.1142/S0219622020500170
73. Rezaei, J. (2021). Anchoring bias in eliciting attribute weights and values in multi-attribute decision-making. Journal of Decision Systems, 30(1), 72-96. https://doi.org/10.1080/12460125.2020.1840705
74. Rezaei, J., Arab, A., & Mehregan, M. (2022). Equalizing bias in eliciting attribute weights in multiattribute decision‐making: experimental research. Journal of Behavioral Decision Making, 35(2), e2262. https://doi.org/10.1002/bdm.2262
75. Rezaei, J., Hemmes, A., & Tavasszy, L. (2017). Multi-criteria decision-making for complex bundling configurations in surface transportation of air freight. Journal of Air Transport Management, 61, 95-105. https://doi.org/10.1016/j.jairtraman.2016.02.006
76. Rezaei, J., Kothadiya, O., Tavasszy, L., & Kroesen, M. (2018). Quality assessment of airline baggage handling systems using SERVQUAL and BWM. Tourism Management, 66, 85-93. https://doi.org/10.1016/j.tourman.2017.11.009
77. Rezaei, J., van Roekel, W. S., & Tavasszy, L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169. https://doi.org/10.1016/j.tranpol.2018.05.007
78. Rúa, E., Comesaña-Cebral, L., Arias, P., & Martínez-Sánchez, J. (2022). A top-down approach for a multi-scale identification of risk areas in infrastructures: particularization in a case study on road safety. European Transport Research Review, 14(1), 1-18. https://doi.org/10.1186/s12544-022-00563-0
79. Safarzadeh, S., Khansefid, S., & Rasti-Barzoki, M. (2018). A group multi-criteria decision-making based on best-worst method. Computers & Industrial Engineering, 126, 111-121. https://doi.org/10.1016/j.cie.2018.09.011
80. Sahebi, I. G., Masoomi, B., & Ghorbani, S. (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
81. Saner, H. S., Yucesan, M., & Gul, M. (2022). A Bayesian BWM and VIKOR-based model for assessing hospital preparedness in the face of disasters. Natural Hazards, 111(2), 1603-1635. https://doi.org/10.1007/s11069-021-05108-7
82. Sarabi, E. P., & Darestani, S. A. (2021). Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA & BWM in mining equipment manufacturing. Applied Soft Computing, 98, 106849. https://doi.org/10.1016/j.asoc.2020.106849
83. Shojaei, P., Haeri, S. A. S., & Mohammadi, S. (2018). Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. Journal of Air Transport Management, 68, 4-13. https://doi.org/10.1016/j.jairtraman.2017.05.006
84. Siagian, H., Tarigan, Z. J. H., & Jie, F. (2021). Supply chain integration enables resilience, flexibility, and innovation to improve business performance in COVID-19 era. Sustainability, 13(9), 4669. https://doi.org/10.3390/su13094669
85. Tadić, S., Krstić, M., Kovač, M., & Brnjac, N. (2022). Evaluation of smart city logistics solutions. Promet-Traffic&Transportation, 34(5), 725-738. https://doi.org/10.7307/ptt.v34i5.4122
86. Talib, F., Asjad, M., Attri, R., Siddiquee, A. N., & Khan, Z. A. (2020). A road map for the implementation of integrated JIT-lean practices in Indian manufacturing industries using the best-worst method approach. Journal of Industrial and Production Engineering, 37(6), 275-291. https://doi.org/10.1080/21681015.2020.1788656
87. Tarei, P. K., Chand, P., & Gupta, H. (2021). Barriers to the adoption of electric vehicles: Evidence from India. Journal of Cleaner Production, 291, 125847. https://doi.org/10.1016/j.jclepro.2021.125847
88. Tavana, M., Shaabani, A., Santos-Arteaga, F. J., & Valaei, N. (2021). An integrated fuzzy sustainable supplier evaluation and selection framework for green supply chains in reverse logistics. Environmental Science and Pollution Research, 28(38), 53953-53982. https://doi.org/10.1007/s11356-021-14302-w
89. Torkayesh, A. E., Malmir, B., & Asadabadi, M. R. (2021). Sustainable waste disposal technology selection: The stratified best-worst multi-criteria decision-making method. Waste Management, 122, 100-112. https://doi.org/10.1016/j.wasman.2020.12.040
90. Vafadarnikjoo, A., Tavana, M., Botelho, T., & Chalvatzis, K. (2020). A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria. Annals of Operations Research, 289(2), 391-418. https://doi.org/10.1007/s10479-020-03603-x
91. Van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
92. Van Oorschot, J. A., Hofman, E., & Halman, J. I. (2018). A bibliometric review of the innovation adoption literature. Technological Forecasting and Social Change, 134, 1-21. https://doi.org/10.1016/j.techfore.2018.04.032
93. Yanilmaz, S., Baskak, D., Yucesan, M., & Gul, M. (2021). Extension of FEMA and SMUG models with Bayesian best-worst method for disaster risk reduction. International Journal of Disaster Risk Reduction, 66, 102631. https://doi.org/10.1016/j.ijdrr.2021.102631
94. Yazdi, A. K., Wanke, P. F., Hanne, T., & Bottani, E. (2020). A decision-support approach under uncertainty for evaluating reverse logistics capabilities of healthcare providers in Iran. Journal of Enterprise Information Management, 33(5), 991-1022. https://doi.org/10.1108/JEIM-09-2019-0299
95. Yousefi-Babadi, A., Bozorgi-Amiri, A., & Tavakkoli-Moghaddam, R. (2021). Sustainable facility relocation in agriculture systems using the GIS and best–worst method. Kybernetes, 51(7), 2343-2382. https://doi.org/10.1108/K-03-2021-0189
96. Yucesan, M., Gul, M., & Celik, E. (2021). A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method. Complex & Intelligent Systems, 7(3), 1547-1564. https://doi.org/10.1007/s40747-021-00279-z
97. Zarrinpoor, N. (2022). A sustainable medical waste management system design in the face of uncertainty and risk during COVID-19. Fuzzy Optimization and Decision Making, 21(3) 1-36. https://doi.org/10.1007/s10700-022-09401-3
98. Zhang, C., Tang, L., & Zhang, J. (2023). Identifying critical indicators in the evaluation of third-party reverse logistics provider using Best–Worst Method. Information, 14(5), 291. https://doi.org/10.3390/info14050291
99. Zhou, J., Wu, Y., Wu, C., He, F., Zhang, B., & Liu, F. (2020). A geographical information system based multi-criteria decision-making approach for location analysis and evaluation of urban photovoltaic charging station: A case study in Beijing. Energy Conversion and Management, 205, 112340. https://doi.org/10.1016/j.enconman.2019.112340
100. Zhou, Y., Kundu, T., Goh, M., Chakraborty, S., & Bai, X. (2023). A multi-stage multi-criteria data analytics approach to rank commercial service airports. Journal of Air Transport Management, 111(4), 102410. https://doi.org/10.1016/j.jairtraman.2023.102410

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-839f9f8f-9f1b-4a55-9c8a-c735ae93dd02