A Novel Intuitionistic Fuzzy Distance Measure-SWARA-COPRAS Method for Multi-Criteria Food Waste Treatment Technology Selection

  • Dinesh Tripathi Department of Mathematics, Government College Satna, India
  • Santosh K. Nigam Department of Mathematics, Government College Satna, India
  • Arunodaya Raj Mishra Department of Mathematics, Government College Raigaon, Satna, India
  • Abdul Raoof Shah Department of Statistics, Government Degree College, Pulwama, India
Keywords: Intuitionistic fuzzy sets; Food waste; Sustainability; Distance measure; SWARA; COPRAS; Multi-attribute decision-analysis.


As an extension of fuzzy set, intuitionistic fuzzy set (IFS) considers the degrees of non-membership and hesitancy along with the degree of membership, therefore, the knowledge and semantic representation of IFS become more significant, resourceful and appropriate. However, with the presence of multiple sustainability indicators and uncertain information, the selection of appropriate food waste treatment technology (FWTT) can be considered as a multi-criteria decision-making (MCDM) problem. Thus, this study aims to introduce a decision support system for assessing the FWTT alternative under uncertain environment. For this purpose, a new intuitionistic fuzzy information-based MCDM methodology is proposed by combining intuitionistic fuzzy distance measure, stepwise weight assessment ratio analysis (SWARA) and the complex proportional assessment (COPRAS) methods. The combination of distance measure-based procedure and SWARA method is used to take the benefits of both the objective and subjective weights of criteria during FWTTs evaluation. Next, the hybridized COPRAS methodology is presented to assess and rank the considered FWTTs from sustainability perspective under intuitionistic fuzzy environment. Further, the present method is implemented on a case study of FWTT selection problem within the context of IFS, which shows its feasibility and effectiveness. This method not only reflects the subjective perspective of decision expert but also captures the objective evaluation of the actual performance measures of each FWTT candidate. Sensitivity and comparative analyses show a high degree of robustness and uniformity in the obtained results. Obtained outcomes point out that the present COPRAS model can effectively choose the suitable FWTT candidate and have the potential to offer practical reference for the policymakers.


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Alipour, M., Hafezi, R., Rani, P., Hafezi, M., & Mardani, A. (2021). A new Pythagorean fuzzy-based decision-making method through entropy measure for fuel cell and hydrogen components supplier selection. Energy, 234, 121208, https://doi.org/10.1016/j.energy.2021.121208.

Alkan, N., & Kahraman, C. (2022). An intuitionistic fuzzy multi-distance based evaluation for aggregated dynamic decision analysis (IF-DEVADA): Its application to waste disposal location selection. Engineering Applications of Artificial Intelligence, 111, 104809, https://doi.org/10.1016/j.engappai.2022.104809.

Arji, G., Ahmadi, H., Nilashi, M., Rashid, T. A., Ahmed, O. H., Aljojo, N., & Zainol, A. (2019). Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification. Biocybernetics and Biomedical Engineering, 39(4), 937-955.

Arora, J., & Tushir, M. (2020). An Enhanced Spatial Intuitionistic Fuzzy C-means Clustering for Image Segmentation. Procedia Computer Science, 167, 646-655.

Ashraf, Z., Khan, M. S., & Lohani, Q. M. D. (2019). New bounded variation based similarity measures between Atanassov intuitionistic fuzzy sets for clustering and pattern recognition. Applied Soft Computing, 85, 105529, https://doi.org/10.1016/j.asoc.2019.105529.

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1), 87-96. DOI: https://doi.org/10.1016/S0165-0114(86)80034-3

Aytekin, A. (2022). Determining criteria weights for vehicle tracking system selection using PIPRECIA-S. Journal of Process Management and New Technologies, 10(1-2), 115-124.

Ayyildiz, E. (2022). Fermatean fuzzy step-wise Weight Assessment Ratio Analysis (SWARA) and its application to prioritizing indicators to achieve sustainable development goal-7. Renewable Energy, 193, 136-148.

Bahani, K., Moujabbir, M., & Ramdania, M. (2021). An accurate fuzzy rule-based classification systems for heart disease diagnosis. Scientific African, 14, e01019, https://doi.org/10.1016/j.sciaf.2021.e01019.

Büyük, B. M., & Temur, G. T. (2022). Food waste treatment option selection through spherical fuzzy AHP. Journal of Intelligent and Fuzzy Systems, 42(1), 97-107, DOI: 10.3233/JIFS-219178.

Cakar, T., & Çavuş, B. (2021). Supplier selection process in dairy industry using fuzzy TOPSIS method. Operational Research in Engineering Sciences: Theory and Applications, 4(1), 82-98. https://doi.org/10.31181/oresta2040182c. DOI: https://doi.org/10.31181/oresta2040182c

Chadderton, C., Foran, C. M., Rodriguez, G., Gilbert, D., Cosper, S. D., & Linkov, I. (2017). Decision support for selection of food waste technologies at military installations. Journal of Cleaner Production, 141, 267-277. DOI: https://doi.org/10.1016/j.jclepro.2016.08.091

Chen, C., Liu, B., Song, F., Jiang, J., Li, Z., Song, C., Li, J., Jin, G., & Wu, J. (2022). An adaptive fuzzy logic control of green tea fixation process based on image processing technology. Biosystems Engineering, 215, 1-20.

Du, W. S. (2021). Subtraction and division operations on intuitionistic fuzzy sets derived from the Hamming distance. Information Sciences, 571, 206–224. doi:10.1016/j.ins.2021.04.068.

Duan, J., & Li, X. (2021). Similarity of intuitionistic fuzzy sets and its applications. International Journal of Approximate Reasoning, 137, 166–180. doi:10.1016/j.ijar.2021.07.009.

Đukić, T. (2022). Ranking factors that affect satisfaction and motivation of employees using the PIPRECIA method. Journal of Process Management and New Technologies, 10 (1-2), 102-114.

El-Mashad, H. M., & Zhang, R. (2010). Biogas production from co-digestion of dairy manure and food waste. Bioresour Technology, 101(11), 4021-4028. DOI: https://doi.org/10.1016/j.biortech.2010.01.027

Fan, Z., Dong, H., Geng, Y., & Fujii, M. (2022). Life cycle cost–benefit efficiency of food waste treatment technologies in China. Environment, Development and Sustainability, https://doi.org/10.1007/s10668-022-02251-4.

Feng, Z., Hanqiang, L., Jiulun, F., Wen, C. C., Rong, L., & Na, L. (2018). Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation. Neurocomputing, 312, 296-309.

Garcia-Garcia, G., Woolley, E., Rahimifard, S., Colwill, J., White, R., & Needham, L. (2017). A methodology for sustainable management of food waste. Waste and Biomass Valorization, 8, 2209-2227. DOI: https://doi.org/10.1007/s12649-016-9720-0

Genc, T. O., & Ekici, A. (2022). A new lens to the understanding and reduction of household food waste: A fuzzy cognitive map approach. Sustainable Production and Consumption, 33, 389-411.

Giwa, A. S., Xu, H., Chang, F., Wu, J., Li, Y., Ali, N., Ding, S., & Wang, K. (2019). Effect of biochar on reactor performance and methane generation during the anaerobic digestion of food waste treatment at long-run operations. Journal of Environmental Chemical Engineering, 7(4), 103067, https://doi.org/10.1016/j.jece.2019.103067.

Gohain, B., Chutia, R., & Dutta, P. (2022). Distance measure on intuitionistic fuzzy sets and its application in decision-making, pattern recognition, and clustering problems. International Journal of Intelligent Systems, 37(3), 2458-2501.

Hao, Z., Xu, Z., Zhao, H., & Zhang, R. (2021). The context-based distance measure for intuitionistic fuzzy set with application in marine energy transportation route decision making. Applied Soft Computing, 101, 107044, doi:10.1016/j.asoc.2020.107044.

Haseli, G., & Sheikh, R. (2022). Base Criterion Method (BCM). Multiple Criteria Decision Making. Springer, Singapore, 17-38.

Haseli, G., Sheikh, R., & Sana, S. S. (2020). Base-criterion on multi-criteria decision-making method and its applications. International Journal of Management Science and Engineering Management, 15(2), 79-88. DOI: 10.1080/17509653.2019.1633964.

Haseli, G., Sheikh, R., Wang, J., Tomaskova, H., & Tirkolaee, E. B. (2021). A novel approach for group decision making based on the best–worst method (G-BWM): Application to supply chain management. Mathematics, 9(16), 01-20. https://doi.org/10.3390/math9161881.

He, J., Huang, Z., Mishra, A. R., & Alrasheedi, M. (2021). Developing a new framework for conceptualizing the emerging sustainable community-based tourism using an extended interval-valued Pythagorean fuzzy SWARA-MULTIMOORA. Technological Forecasting and Social Change, 171, 120955. doi:10.1016/j.techfore.2021.120955.

Kersuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11, 243–258. DOI: https://doi.org/10.3846/jbem.2010.12

Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC). Symmetry, 13(4), 525; https://doi.org/10.3390/sym13040525.

Kusakci, S., Yilmaz, M. K., Kusakci, A. O., Sowe, S., & Nantembelele, F. A. (2022). Towards sustainable cities: A sustainability assessment study for metropolitan cities in Turkey via a hybridized IT2F-AHP and COPRAS approach. Sustainable Cities and Society, 78, 103655, https://doi.org/10.1016/j.scs.2021.103655.

Lal, S., Mohapatra, & S. K. (2020). A feasibility study to utilize kitchen waste for power generation in urban areas using CI engine. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 42(15), 1914-1922.

Lu, J., Zhang, S., Wu, J., & Wei, Y. (2021). COPRAS method for multiple attribute group decision making under picture fuzzy environment and their application to green supplier selection. Technological and Economic Development of Economy, 27(2), 369-385. https://doi.org/10.3846/tede.2021.14211.

Maneckshaw, B., & Mahapatra, G. S. (2022). Novel fuzzy matrix swap algorithm for fuzzy directed graph on image processing. Expert Systems with Applications, 193, 116291, https://doi.org/10.1016/j.eswa.2021.116291.

Mardani, A., Saraji, M. K., Mishra, A. R., & Rani, P. (2020). A novel extended approach under hesitant fuzzy sets to design a framework for assessing the key challenges of digital health interventions adoption during the COVID-19 outbreak. Applied Soft Computing Journal 96, 106613; https://doi.org/10.1016/j.asoc.2020.106613.

Masoomi, B., Sahebi, I. G., Fathi, M., Yıldırım, F., Ghorbani, S. (2022). Strategic supplier selection for renewable energy supply chain under green capabilities (fuzzy BWM-WASPAS-COPRAS approach). Energy Strategy Reviews, 40, 100815, https://doi.org/10.1016/j.esr.2022.100815.

Mishra, A. R. (2016). Intuitionistic Fuzzy Information Measures with Application in Rating of Township Development. Iranian Journal of Fuzzy Systems, 13(3), 49-70.

Mishra, A. R., Singh, R. K., & Motwani, D. (2019). Multi-criteria assessment of cellular mobile telephone service providers using intuitionistic fuzzy WASPAS method with similarity measures. Granular Computing, 4, 511-529, doi:10.1007/s41066-018-0114-5.

Morelli, B., Cashman, S., Ma, X., Turgeon, J., Arden, S., & Garland, J. (2020). Environmental and cost benefits of co-digesting food waste at wastewater treatment facilities. Water Science and Technology, 82(2), 227–241.

Narang, M., Joshi, M. C., Bisht, K., & Pal, A. (2022). Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator. Decision Making: Applications in Management and Engineering, 5(1), 90-112. https://doi.org/10.31181/dmame0310022022n. DOI: https://doi.org/10.31181/dmame0310022022n

Narang, M., Joshi, M. C., & Pal, A. K. (2021). A hybrid fuzzy COPRAS-base-criterion method for multi-criteria decision making. Soft Computing, 25(13), 8391–8399. doi:10.1007/s00500-021-05762-w.

Omar, M. F., Shukor, J. A., Kassim, M. M., & Hussin, K. C. (2021). Decision model using hierarchical fuzzy TOPSIS: Towards improving decision making in food waste management. Journal of Language and Linguistic Studies, 17(3), 1639-1650.

Oskouei, A. G., Hashemzadeh, M., Asheghi, B., & Balafar, M. A. (2021). CGFFCM: Cluster-weight and Group-local Feature-weight learning in Fuzzy C-Means clustering algorithm for color image segmentation. Applied Soft Computing, 113, 108005, https://doi.org/10.1016/j.asoc.2021.108005.

Pamučar, D., Bozanic, D., Puška, A., & Marinković, D. (2022). Application of neuro-fuzzy system for predicting the success of a company in public procurement. Decision Making: Applications in Management and Engineering, 5(1), 135-153. https://doi.org/10.31181/dmame0304042022p. DOI: https://doi.org/10.31181/dmame0304042022p

Pham, T. P. T., Kaushik, R., Parshetti, G. K., Mahmood, R., & Balasubramanian, R. (2015). Food waste-to-energy conversion technologies: current status and future directions. Waste Management, 38, 399-408. DOI: https://doi.org/10.1016/j.wasman.2014.12.004

Rahmati, S., Mahdavi, M. H., Ghoushchi, S. J., Tomaskova, H., & Haseli, G. (2022). Assessment and prioritize risk factors of financial measurement of management control system for production companies using a hybrid Z-SWARA and Z-WASPAS with FMEA method: A meta-analysis. Mathematics, 10, 01-27. https://doi.org/10.3390/ math10020253.

Rani, P., Mishra, A. R., Deveci, M., & Antucheviciene, J. (2022b). New complex proportional assessment approach using Einstein aggregation operators and improved score function for interval-valued Fermatean fuzzy sets. Computers & Industrial Engineering, 169, 108165, https://doi.org/10.1016/j.cie.2022.108165.

Rani, P., Mishra, A. R., Krishankumar, R., Ravichandran, K. S., & Kar, S. (2021). Multi-criteria food waste treatment method selection using single-valued neutrosophic-CRITIC-MULTIMOORA framework. Applied Soft Computing, 111, 107657, https://doi.org/10.1016/j.asoc.2021.107657.

Rani, P., Mishra, A. R., Mardani, A., Cavallaro, F., Štreimikienė, D., & Khan, S. A. R. (2020). Pythagorean fuzzy SWARA–VIKOR framework for performance evaluation of solar panel selection. Sustainability, 12(10), 4278; https://doi.org/10.3390/su12104278.

Rani, P., Mishra, A. R., Saha, A., Hezam, I. M., and Pamucar, D. (2022a). Fermatean Fuzzy Heronian Mean Operators and MEREC‐Based Additive Ratio Assessment Method: An Application to Food Waste Treatment Technology Selection. International Journal of Intelligent Systems 37(3), 2612–2647; DOI: 10.1002/int.22787.

Ren, J., & Toniolo, S. (2020). Life cycle sustainability prioritization of alternative technologies for food waste to energy: a multi-actor multi-criteria decision making approach. In Waste-to-Energy: Multi-Criteria Decision Analysis for Sustainability Assessment and Ranking, Elsevier, 345-380. https://doi.org/10.1016/B978-0-12-816394-8.00012-4.

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

Saaty, T. L. (2005). Theory and applications of the analytic network process: decision making with benefits, opportunities, costs, and risks. RWS Publications, Pittsburgh.

Saaty, T.L. (1980). The Analytical Hierarchy Process; McGraw-Hill: New York, NY, USA. DOI: https://doi.org/10.21236/ADA214804

Sakcharoen, T., Ratanatamskul, C., & Chandrachai, A. (2021). Factors affecting technology selection, techno-economic and environmental sustainability assessment of a novel zero-waste system for food waste and wastewater management. Journal of Cleaner Production, 314, 128103, https://doi.org/10.1016/j.jclepro.2021.128103.

Saraji, M. K., & Streimikiene, D. (2022). Evaluating the circular supply chain adoption in manufacturing sectors: A picture fuzzy approach. Technology in Society, 70, 102050, https://doi.org/10.1016/j.techsoc.2022.102050.

Shahmoradi, S., & Shouraki, S. B. (2018). Evaluation of a novel fuzzy sequential pattern recognition tool (fuzzy elastic matching machine) and its applications in speech and handwriting recognition. Applied Soft Computing, 62, 315-327. DOI: https://doi.org/10.1016/j.asoc.2017.10.036

Shewa, W. A., Hussain, A., Chandra, R., Lee, J., Saha, S., & Lee, H. S. (2020). Valorization of food waste and economical treatment: effect of inoculation methods. Journal of Cleaner Production, 261, 121170, https://doi.org/10.1016/j.jclepro.2020.121170.

Slorach, P. C., Jeswani, H. K., Cuéllar-Franca, R.,& Azapagic, A. (2019). Environmental and economic implications of recovering resources from food waste in a circular economy. Science of The Total Environment, 693, 133516; https://doi.org/10.1016/j.scitotenv.2019.07.322.

Szalai, S., Eller, B., Juhász, E., Movahedi, M. R., Németh, A., Harrach, D., Baranyai, G., & Fischer, S. (2022b). Investigation of deformations of ballasted railway track during collapse using the Digital Image Correlation Method (DICM). Reports in Mechanical Engineering, 3(1), 258-282. https://doi.org/10.31181/rme20016032022s. DOI: https://doi.org/10.31181/rme20016032022s

Szalai, S., Szürke, S. K., Harangozó, D., & Fischer, S. (2022a). Investigation of deformations of a lithium polymer cell using the Digital Image Correlation Method (DICM). Reports in Mechanical Engineering, 3(1), 206-224. https://doi.org/10.31181/rme20008022022s. DOI: https://doi.org/10.31181/rme20008022022s

Tyagi, M., Panchal, D., Kumar, D., & Walia, R. S. (2021). Modeling and Analysis of Lean Manufacturing Strategies Using ISM-Fuzzy MICMAC Approach. Operational Research in Engineering Sciences: Theory and Applications, 4(1), 38-66. https://doi.org/10.31181/oresta2040123t. DOI: https://doi.org/10.31181/oresta2040123t

Ulutaş, A., Stanujkic, D., Karabasevic, D., Popovic, G., & Novaković, S. (2022). Pallet Truck Selection with MEREC and WISP-S methods. Strategic Management, DOI: 10.5937/StraMan2200013U.

Vojinović, N., Stević, Z., & Tanackov, I. (2020). A novel IMF SWARA-FDWGA-PESTEL analysis for assessment of healthcare system. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 139-151. DOI: https://doi.org/10.31181/oresta070422211v

Wei, D., Wang, Z., Si, L., Tan, C., Lu, X. (2021). An image segmentation method based on a modified local-information weighted intuitionistic fuzzy C-means clustering and gold-panning algorithm. Engineering Applications of Artificial Intelligence, 101, 104209, https://doi.org/10.1016/j.engappai.2021.104209.

Xie, D., Xiao, F., & Pedrycz, W. (2022). Information quality for intuitionistic fuzzy values with its application in decision making. Engineering Applications of Artificial Intelligence, 109, 104568, https://doi.org/10.1016/j.engappai.2021.104568.

Xu, G. L., Wan, S. P., & Xie, X. L. (2015). A Selection Method Based on MAGDM with Interval-Valued Intuitionistic Fuzzy Sets. Mathematical Problems in Engineering, 2015, Article ID 791204, 01-13, doi: 10.1155/2015/791204. DOI: https://doi.org/10.1155/2015/791204

Xu, Z. S. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179–1187. DOI: https://doi.org/10.1109/TFUZZ.2006.890678

Xu, Z. S., & Chen, J. (2008). An overview of distance and similarity measures of intuitionistic fuzzy sets. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 16(4), 529-555. DOI: https://doi.org/10.1142/S0218488508005406

Yücenur, G. N., & Şenol, K. (2021). Sequential SWARA and fuzzy VIKOR methods in elimination of waste and creation of lean construction processes. Journal of Building Engineering, 44, 103196, https://doi.org/10.1016/j.jobe.2021.103196.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353. DOI: https://doi.org/10.1016/S0019-9958(65)90241-X

Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multicriteria complex proportional assessment of projects. Technological and Economic Development of Economy, 1(3), 131–139.

Zhang, K., Huang, Y., Yuan, X., & Zhao, C. (2019). Infrared and Visible Image Fusion Based on Intuitionistic Fuzzy Sets. Infrared Physics & Technology, 105, 103124, doi:10.1016/j.infrared.2019.103124.

Zhou, R., Jin, J., Cui, Y., Ning, S., Bai, X., Zhang, L., Zhou, Y., Wu, C., & Tong, F. (2022). Agricultural drought vulnerability assessment and diagnosis based on entropy fuzzy pattern recognition and subtraction set pair potential. Alexandria Engineering Journal, 61(1), 51-63.

How to Cite
Tripathi, D., Nigam, S. K., Mishra, A. R., & Shah, A. R. (2022). A Novel Intuitionistic Fuzzy Distance Measure-SWARA-COPRAS Method for Multi-Criteria Food Waste Treatment Technology Selection . Operational Research in Engineering Sciences: Theory and Applications. https://doi.org/10.31181/oresta111022106t