A Healthcare Assessment for Recycling Hazardous Waste by a New Intuitionistic Fuzzy Decision Method Based On an Assembled Proportionate Evaluation Approach

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Shahed University, Tehran, Iran.

2 Department of Industrial Engineering, Shahed University, Tehran, Iran

Abstract

Nowadays, the proper safety and health assessment in hazardous waste recycling organizations has become an encouraging subject, mainly in developing countries. An assessment of hazardous waste recycling (HWR) facility choice can be introduced as a complex multi-criteria decision-making (MCDM) problem that contains many alternatives solutions with incompatible tangible and intangible indexes. This paper proposes a new decision method based on MCDM approach under intuitionistic fuzzy (IF) environment. The proposed approach is separated from association operators of IFSs; Furthermore, a few modifications in the common complex proportional evaluation method and a procedure for obtaining indexes of weights are introduced. This paper is constructed based on the entropy method to compute weights of criteria, the similarity measure to obtain the decision-makers (DMs)’ weights under IF conditions. Afterward, a new ranking method is introduced based on a new similarity ideal solution method. The major advantage of the suggested new ranking approach is to achieve the best alternatives compared to DMs’ decisions as well as the effects of evaluation values. Hence, the proposed model is a more generalized and proper demonstration to take the real-life fuzziness than the previous studies carefully. Recently, increasing challenges for environmental subjects needs the assessment of HWR facility selection concerning various indexes; so, the feasible problem is given based on a real case study of HWR facility selection, which proves the validity and feasibility of the proposed method. Eventually, a comparative analysis is presented to verify the performance of the proposed method by comparing with IF-CODAS approach.

Keywords

References

       [1]        Guimarães, A. G., Vaz-Fernandes, P., Ramos, M. R., and Martinho, A. P. (2018). Co-processing of hazardous waste: The perception of workers regarding sustainability and health issues in a Brazilian cement company. Journal of Cleaner Production, 186, 313-324.
       [2]        Nahrgang, J. D., Morgeson, F. P., and Hofmann, D. A. (2011). Safety at work: a meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes. Journal of applied psychology, 96(1), 71.
       [3]        Guo, L., Qu, Y., Tseng, M. L., Wu, C., and Wang, X. (2018). Two-echelon reverse supply chain in collecting waste electrical and electronic equipment: A game theory model. Computers and Industrial Engineering, 126, 187-195.
       [4]        Hu, H., Li, X., Zhang, Y., Shang, C., and Zhang, S. (2019). Multi-objective location-routing model for hazardous material logistics with traffic restriction constraint in inter-city roads. Computers and Industrial Engineering, 128, 861-876.
       [5]        Kuo, T. C. (2013). Waste electronics and electrical equipment disassembly and recycling using Petri net analysis: Considering the economic value and environmental impacts. Computers and Industrial Engineering, 65(1), 54-64.
       [6]        Tremblay, A., and Badri, A. (2018). Assessment of occupational health and safety performance evaluation tools: State of the art and challenges for small and medium-sized enterprises. Safety science, 101, 260-267.
       [7]        Riaño‐Casallas, M. I., and Tompa, E. (2018). Cost‐benefit analysis of investment in occupational health and safety in Colombian companies. American journal of industrial medicine, 61(11), 893-900.
       [8]        Abrahamsen, E. B., Abrahamsen, H. B., Milazzo, M. F., and Selvik, J. T. (2018). Using the ALARP principle for safety management in the energy production sector of chemical industry. Reliability Engineering and System Safety, 169, 160-165.
       [9]        Arya, A., and Yadav, S. P. (2018). Development of intuitionistic fuzzy super-efficiency slack based measure with an application to health sector. Computers and Industrial Engineering, 115, 368-380.
     [10]      Çalı, S., and Balaman, Ş. Y. (2019). Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Computers and Industrial Engineering, 129, 315-332.
     [11]      Burillo, P., and Bustince, H. (1996). Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets. Fuzzy sets and systems, 78(3), 305-316.
     [12]      Szmidt, E., and Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy sets and systems, 118(3), 467-477.
     [13]      Mishra, A. R., Singh, R. K., and Motwani, D. (2019). Multi-criteria assessment of cellular mobile telephone service providers using intuitionistic fuzzy WASPAS method with similarity measures. Granular Computing, 4(3), 511-529.
     [14]      Qi, X., Liang, C., and Zhang, J. (2015). Generalized cross-entropy based group decision making with unknown expert and attribute weights under interval-valued intuitionistic fuzzy environment. Computers and Industrial Engineering, 79, 52-64.
     [15]      Hung, W. L., and Yang, M. S. (2006). Fuzzy entropy on intuitionistic fuzzy sets. International Journal of Intelligent Systems, 21(4), 443-451.
     [16]      Ye, J. (2010). Two effective measures of intuitionistic fuzzy entropy. Computing, 87(1), 55-62.
     [17]      Chen, T. Y., and Li, C. H. (2010). Determining objective weights with intuitionistic fuzzy entropy measures: a comparative analysis. Information Sciences, 180(21), 4207-4222.
     [18]      Joshi, R., and Kumar, S. (2018). A new parametric intuitionistic fuzzy entropy and its applications in multiple attribute decision making. International Journal of applied and computational mathematics, 4(1), 1-23.
     [19]      Wei, A. P., Li, D. F., Jiang, B. Q., and Lin, P. P. (2019). The novel generalized exponential entropy for intuitionistic fuzzy sets and interval valued intuitionistic fuzzy sets. International Journal of Fuzzy Systems, 21(8), 2327-2339.
     [20]      Song, Y., Fu, Q., Wang, Y. F., and Wang, X. (2019). Divergence-based cross entropy and uncertainty measures of Atanassov’s intuitionistic fuzzy sets with their application in decision making. Applied Soft Computing, 84, 105703.
     [21]      Rahimi, M., Kumar, P., Moomivand, B., and Yari, G. (2021). An intuitionistic fuzzy entropy approach for supplier selection. Complex and Intelligent Systems, 1-8.
     [22]      Hatami-Marbini, A., Tavana, M., Moradi, M., and Kangi, F. (2013). A fuzzy group Electre method for safety and health assessment in hazardous waste recycling facilities. Safety science, 51(1), 414-426.
     [23]      Büyüközkan, G., Göçer, F., and Karabulut, Y. (2019). A new group decision making approach with IF AHP and IF VIKOR for selecting hazardous waste carriers. Measurement, 134, 66-82.
     [24]      Kumar, A., and Dixit, G. (2019). A novel hybrid MCDM framework for WEEE recycling partner evaluation on the basis of green competencies. Journal of Cleaner Production, 241, 118017.
     [25]      Danesh, G., Monavari, S. M., Omrani, G. A., Karbasi, A., and Farsad, F. (2019). Compilation of a model for hazardous waste disposal site selection using GIS-based multi-purpose decision-making models. Environmental monitoring and assessment, 191(2), 1-14.
     [26]      Zhang, C., Hu, Q., Zeng, S., and Su, W. (2021). IOWLAD-based MCDM model for the site assessment of a household waste processing plant under a Pythagorean fuzzy environment. Environmental Impact Assessment Review, 89, 106579.
     [27]      Karagöz, S., Deveci, M., Simic, V., and Aydin, N. (2021). Interval type-2 Fuzzy ARAS method for recycling facility location problems. Applied Soft Computing, 102, 107107.
     [28]      Sisay, G., Gebre, S. L., and Getahun, K. (2021). GIS-based potential landfill site selection using MCDM-AHP modeling of Gondar Town, Ethiopia. African Geographical Review, 40(2), 105-124.
     [29]      Mishra, A. R., Mardani, A., Rani, P., and Zavadskas, E. K. (2020). A novel EDAS approach on intuitionistic fuzzy set for assessment of health-care waste disposal technology using new parametric divergence measures. Journal of Cleaner Production, 272, 122807.
     [30]      Yazdani, M., Tavana, M., Pamučar, D., and Chatterjee, P. (2020). A rough based multi-criteria evaluation method for healthcare waste disposal location decisions. Computers and Industrial Engineering, 143, 106394.
     [31]      Garg, C. P. (2021). Modeling the e-waste mitigation strategies using Grey-theory and DEMATEL framework. Journal of Cleaner Production, 281, 124035.
     [32]      Puška, A., Stević, Ž., and Pamučar, D. (2021). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 1-31.
     [33]      Mishra, A. R., and Rani, P. (2021). Multi-criteria healthcare waste disposal location selection based on Fermatean fuzzy WASPAS method. Complex and Intelligent Systems, 1-16.
     [34]      Manupati, V. K., Ramkumar, M., Baba, V., and Agarwal, A. (2021). Selection of the best healthcare waste disposal techniques during and post COVID-19 pandemic era. Journal of Cleaner Production, 281, 125175.
     [35]      Torkayesh, A. E., Malmir, B., and Asadabadi, M. R. (2021). Sustainable waste disposal technology selection: The stratified best-worst multi-criteria decision-making method. Waste Management, 122, 100-112.
     [36]      Mi, X., Tian, Y., and Kang, B. (2021). A hybrid multi-criteria decision making approach for assessing health-care waste management technologies based on soft likelihood function and D-numbers. Applied Intelligence, 1-20.
     [37]      Hatami-Marbini, A., Emrouznejad, A., and Tavana, M. (2011). A taxonomy and review of the fuzzy data envelopment analysis literature: two decades in the making. European journal of operational research, 214(3), 457-472.
     [38]      Ye, J. (2010). Multicriteria fuzzy decision-making method using entropy weights-based correlation coefficients of interval-valued intuitionistic fuzzy sets. Applied Mathematical Modelling, 34(12), 3864-3870.
     [39]      Yue, Z. (2011). Deriving decision maker’s weights based on distance measure for interval-valued intuitionistic fuzzy group decision making. Expert Systems with Applications, 38(9), 11665-11670.
     [40]      Yue, Z. (2014). Aggregating crisp values into intuitionistic fuzzy number for group decision making. Applied Mathematical Modelling, 38(11-12), 2969-2982.
     [41]      Mishra, A. R., Rani, P., Mardani, A., Pardasani, K. R., Govindan, K., and Alrasheedi, M. (2020). Healthcare evaluation in hazardous waste recycling using novel interval-valued intuitionistic fuzzy information based on complex proportional assessment method. Computers and Industrial Engineering, 139, 106140.
     [42]      Hashemi, H., Bazargan, J., and Mousavi, S. M. (2013). A compromise ratio method with an application to water resources management: an intuitionistic fuzzy set. Water resources management, 27(7), 2029-2051.
     [43]      Karagoz, S., Deveci, M., Simic, V., Aydin, N., and Bolukbas, U. (2020). A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: a case study of Istanbul. Waste Management and Research, 38(6), 660-672.
     [44]      Dorfeshan, Y., Tavakkoli-Moghaddam, R., Mousavi, S. M., and Vahedi-Nouri, B. (2020). A new weighted distance-based approximation methodology for flow shop scheduling group decisions under the interval-valued fuzzy processing time. Applied Soft Computing, 91, 106248.
     [45]      Atanassov, k. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96.
     [46]      Atanassov, K. T. (1994). New operations defined over the intuitionistic fuzzy sets. Fuzzy sets and Systems, 61(2), 137-142.
     [47]      Xu, Z., and Yager, R. R. (2006). Some geometric aggregation operators based on intuitionistic fuzzy sets. International journal of general systems, 35(4), 417-433.
     [48]      Szmidt, E., and Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy sets and systems, 114(3), 505-518.
 
Advances in Industrial Engineering
Volume 55, Issue 3
July 2021
Pages 267-284

Files

Share

How to cite

Statistics