Industrial Waste Management Planning Evaluation Using Integrated Best-Worst Technique With Fuzzy TOPSIS

Haridas Mondal; Totan Garai; Otavio Oliveira; Shariful Alam

doi:10.31181/dma412027140

Authors

Haridas Mondal Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India https://orcid.org/0009-0003-1980-210X
Totan Garai Department of Mathematics, Syamsundar College, Syamsundar, Purba Bardhaman, West Bengal, India https://orcid.org/0000-0002-0476-6447
Otavio Oliveira Production Department, Sao Paulo State University(UNESP), São Paulo, Brazil https://orcid.org/0000-0002-5192-3644
Shariful Alam Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, West Bengal, India https://orcid.org/0000-0001-8263-117X

DOI:

https://doi.org/10.31181/dma412027140

Keywords:

Fuzzy decision-making, Industrial waste management(IWM), Best-Worst Method, BWM, TOPSIS, FTOPSIS, Expert-based decision-making

Abstract

The management of industrial waste and electronic waste is increasingly critical today. Industrialization and inadequate planning lead to daily industrial byproducts and waste that pose significant challenges to society. Industrial waste also presents increasing and complex challenges, resulting in soil and water contamination, degradation of air quality, and considerable threats to ecosystems and human health. To keep society safe from the detrimental consequences of waste, it's essential to have sustainable and efficient methods for managing industrial waste. In this paper, we discuss how an expert-based multi-criteria decision-making technique can aid in implementing an effective industrial waste management strategy by analyzing various expert reviews and public opinions. Multiple waste management strategies are reviewed in response to various important factors, including environmental, social, legal, compliance, cost, and technical considerations. These decision-making factors are analyzed and ranked based on their weights using the Best-Worst technique. Subsequently, the ranking for the effective industrial waste management(IWM) strategy is obtained through the fuzzy TOPSIS ranking technique. A sensitivity analysis has been conducted based on different factor weights. Finally, the ranking of the management strategy has been verified by utilizing another well-established decision-making technique.

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References

Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17, B–141. https://doi.org/10.1287/mnsc.17.4.B141

Zopounidis, C., & Doumpos, M. (2002). Multi-criteria decision aid in financial decision making: Methodologies and literature review. Journal of Multi-Criteria Decision Analysis, 11, 167–186. https://doi.org/10.1002/mcda.333

Koot, M., Mes, M. R., & Iacob, M. E. (2021). A systematic literature review of supply chain decision making supported by the internet of things and big data analytics. Computers & industrial engineering, 154, 107076. https://doi.org/10.1016/j.cie.2020.107076

Gazi, K. H., Biswas, A., Basuri, T., Ghosh, A., & Mondal, S. P. (2025). Finding humanitarian supply chain management challenges using uncertain mcdm methodology. Spectrum of Mechanical Engineering and Operational Research, 2, 248–279. https://doi.org/10.31181/smeor21202548

Si, A., Das, S., & Kar, S. (2025). Hybrid approach for covid-19 vaccine distribution. Decision Making Advances, 3, 1–17. https://doi.org/10.31181/dma31202546

Kiker, G. A., Bridges, T. S., Varghese, A., Seager, T. P., & Linkov, I. (2005). Application of multi-criteria decision analysis in environmental decision making. Integrated environmental assessment and management, 1, 95–108. https://doi.org/10.1897/IEAM_2004a-015.1

Mukherjee, A. K., Gazi, K. H., Mukherjee, S. B., Ciurdariu, L., Biswas, A., Ramalingam, S., Mondal, S. P., & Singh, P. (2025). Identifying alternative energy sources: A multi-criteria intuitionistic fuzzy approach to sustainable alternatives. Franklin Open, 100363. https://doi.org/10.1016/j.fraope.2025.100363

Deng, Y., & Chan, F. T. (2011). A new fuzzy dempster mcdm method and its application in supplier selection. Expert Systems with Applications, 38, 9854–9861. https://doi.org/10.1016/j.eswa.2011.02.017

Tešić, D., Božanić, D., Mondal, S. P., & Puška, A. (2025). Modification of the ranking of alternatives with weights of criterion (rawec) method and improvement with fermatean fuzzy numbers. Journal of Soft Computing and Decision Analytics, 3, 146–157. https://doi.org/10.31181/jscda31202570

Lee-Kwang, H., & Lee, J.-H. (1999). A method for ranking fuzzy numbers and its application to decision-making. IEEE transactions on fuzzy systems, 7, 677–685. https://doi.org/10.1109/91.811235

Dursun, M., & Karsak, E. E. (2010). A fuzzy mcdm approach for personnel selection. Expert Systems with applications, 37, 4324–4330. https://doi.org/10.1016/j.eswa.2009.11.067

Önüt, S., Kara, S. S., & Işik, E. (2009). Long term supplier selection using a combined fuzzy mcdm approach: A case study for a telecommunication company. Expert systems with applications, 36, 3887–3895. https://doi.org/10.1016/j.eswa.2008.02.045

Varchandi, S., Memari, A., & Jokar, M. R. A. (2024). An integrated best–worst method and fuzzy topsis for resilient-sustainable supplier selection. Decision Analytics Journal, 11, 100488. https://doi.org/10.1016/j.dajour.2024.100488

Sun, C.-C. (2010). A performance evaluation model by integrating fuzzy ahp and fuzzy topsis methods. Expert systems with applications, 37, 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066

Javad, M. O. M., Darvishi, M., & Javad, A. O. M. (2020). Green supplier selection for the steel industry using bwm and fuzzy topsis: A case study of khouzestan steel company. Sustainable Futures, 2, 100012. https://doi.org/10.1016/j.sftr.2020.100012

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. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009

Chen, D., Faibil, D., & Agyemang, M. (2020). Evaluating critical barriers and pathways to implementation of e-waste formalization management systems in ghana: A hybrid bwm and fuzzy topsis approach. Environmental Science and Pollution Research, 27, 44561–44584. https://doi.org/10.1007/s11356-020-10360-8

Rani, P., Mishra, A. R., Mardani, A., Cavallaro, F., Alrasheedi, M., & Alrashidi, A. (2020). A novel approach to extended fuzzy topsis based on new divergence measures for renewable energy sources selection. Journal of Cleaner Production, 257, 120352. https://doi.org/10.1016/j.jclepro.2020.120352

Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning—a review. Renewable and sustainable energy reviews, 8, 365–381. https://doi.org/10.1016/j.rser.2003.12.007

Lootsma, F. A., & Schuijt, H. (1997). The multiplicative ahp, smart and electre in a common context. Journal of Multi-Criteria Decision Analysis, 6, 185–196. https://doi.org/10.1002/(SICI)1099-1360(199707)6:4<185::AID-MCDA136>3.0.CO;2-E

Seker, S., & Aydin, N. (2020). Sustainable public transportation system evaluation: A novel two-stage hybrid method based on ivif-ahp and codas. International Journal of Fuzzy Systems, 22, 257–272. https://doi.org/10.1007/s40815-019-00785-w

Hamurcu, M., & Eren, T. (2022). Applications of the moora and topsis methods for decision of electric vehicles in public transportation technology. Transport, 37, 251–263. https://doi.org/10.3846/transport.2022.17783

Beccali, M., Cellura, M., & Mistretta, M. (2003). Decision-making in energy planning. application of the electre method at regional level for the diffusion of renewable energy technology. Renewable energy, 28, 2063–2087. https://doi.org/10.1016/S0960-1481(03)00102-2

Nouri, D., Sabour, M., & GhanbarzadehLak, M. (2018). Industrial solid waste management through the application of multi-criteria decision-making analysis: A case study of shamsabad industrial complexes. Journal of material cycles and waste management, 20, 43–58. https://doi.org/10.1007/s10163-016-0544-6

Van Thanh, N. (2022). Optimal waste-to-energy strategy assisted by fuzzy mcdm model for sustainable solid waste management. Sustainability, 14, 6565. https://doi.org/10.3390/su14116565

Abdel-Basset, M., Gamal, A., Sallam, K. M., Hezam, I. M., & Alshamrani, A. M. (2023). Sustainable flue gas treatment system assessment for iron and steel sector: Spherical fuzzy mcdm-based innovative multistage approach. International Journal of Energy Research, 2023, 6645065. https://doi.org/10.1155/2023/6645065

Van Thanh, N., Hai, N. H., & Lan, N. T. K. (2022). Fuzzy mcdm model for selection of infectious waste management contractors. Computers, Materials & Continua, 72. https://doi.org/10.32604/cmc.2022.026357

Maddah, S., Bidhendi, G. N., Borhani, F., & Taleizadeh, A. A. (2022). Resilient-sustainable supplier selection considering health-safety-environment performance indices: A case study in automobile industry. https://doi.org/10.21203/rs.3.rs-2046543/v1

Afrasiabi, A., Tavana, M., & Di Caprio, D. (2022). An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection. Environmental Science and Pollution Research, 29(25), 37291–37314. https://doi.org/10.1007/s11356-021-17851-2

Kayani, S. A., Warsi, S. S., & Liaqait, R. A. (2023). A smart decision support framework for sustainable and resilient supplier selection and order allocation in the pharmaceutical industry. Sustainability, 15, 5962. https://doi.org/10.3390/su15075962

Tavakoli, M., Tajally, A., Ghanavati-Nejad, M., & Jolai, F. (2023). A markovian-based fuzzy decision-making approach for the customer-based sustainable-resilient supplier selection problem. Soft Computing, 1. https://doi.org/10.1007/s00500-023-08380-w

Garai, T. (2025a). Bipolar expected value-based mcdm technique on wastewater management under trapezoidal bipolar fuzzy environment. Journal of Intelligent Systems in Current Computer Engineering, 3, E26662949326075. https://doi.org/10.2174/01266

Nemati, E. (2024). Assessment of suppliers through the resiliency and sustainability paradigms using a new mcdm model under interval type-2 fuzzy sets. Soft Computing, 28(11), 7439–7453. https://doi.org/10.1007/s00500-023-09603-w

Fetanat, A., & Khorasaninejad, E. (2015). A novel hybrid mcdm approach for offshore wind farm site selection: A case study of iran. Ocean & Coastal Management, 109, 17–28. https://doi.org/10.1016/j.ocecoaman.2015.02.005

Bonab, S. R., Haseli, G., Rajabzadeh, H., Ghoushchi, S. J., Hajiaghaei-Keshteli, M., & Tomaskova, H. (2023). Sustainable resilient supplier selection for iot implementation based on the integrated bwm and trust under spherical fuzzy sets. Decision making: applications in management and engineering, 6, 153–185. https://doi.org/10.31181/dmame12012023b

Ambilkar, P., Verma, P., & Das, D. (2024). Sustailient supplier selection using neutrosophic best–worst approach: A case study of additively manufactured trinkets. Benchmarking: An International Journal, 31, 1515–1547. https://doi.org/10.1108/BIJ-02-2023-0122

Nayeri, S., Khoei, M. A., Rouhani-Tazangi, M. R., GhanavatiNejad, M., Rahmani, M., & Tirkolaee, E. B. (2023). A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system. Engineering Applications of Artificial Intelligence, 124, 106511. https://doi.org/10.1016/j.engappai.2023.106511

Garai, T. (2025b). Parametric center-based sustainable modeling of chennai water resource management under bipolar-fuzzy mcdm techniques. International Journal of Information Technology & Decision Making. https://doi.org/10.1142/S0219622025500919

Suryadi, A., & Rau, H. (2023). Considering region risks and mitigation strategies in the supplier selection process for improving supply chain resilience. Computers & Industrial Engineering, 181, 109288. https://doi.org/10.1016/j.cie.2023.109288

Sheykhizadeh, M., Ghasemi, R., Vandchali, H. R., Sepehri, A., & Torabi, S. A. (2024). A hybrid decision-making framework for a supplier selection problem based on lean, agile, resilience, and green criteria: A case study of a pharmaceutical industry. Environment, Development and Sustainability, 26, 30969–30996. https://doi.org/10.1007/s10668-023-04135-7

Taghavi, S. M., Ghezavati, V., Mohammadi Bidhandi, H., & Mirzapour Al-e-Hashem, S. M. J. (2024). Sustainable and resilient supplier selection, order allocation, and production scheduling problem under disruption utilizing conditional value at risk. Journal of Modelling in Management, 19, 658–692. https://doi.org/10.1108/JM2-10-2022-0250

Dubois, D. J. (1980). Fuzzy sets and systems: Theory and applications (Vol. 144).

Dinagar, S., Kamalanathan, R., & Rameshan, N. (2017). Sub interval average method for ranking of linear fuzzy numbers. International Journal of Pure and Applied Mathematics, 114, 119–130.

Chen, C.-T. (2000). Extensions of the topsis for group decision-making under fuzzy environment. Fuzzy sets and systems, 114, 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1