Entropy and discrimination measures based q-rung orthopair fuzzy MULTIMOORA framework for selecting solid waste disposal method

Abstract

Fastest growing population, rapid urbanization, and growth in the disciplines of science and technology cause continually development in the amount and diversity of solid waste. In modern world, evaluation of an appropriate solid waste disposal method (SWDM) can be referred as multi-criteria decision-making (MCDM) problem due to involvement of several conflicting quantitative and qualitative sustainability indicators. The imprecision and ambiguity are usually arisen in SWDM assessment problem, and the q-rung orthopair fuzzy set (q-ROFS) has been recognized as one of the adaptable and valuable ways to tackle the complex uncertain information arisen in realistic problems. In the context of q-ROFSs, entropy is a significant measure for depicting fuzziness and uncertain information of q-ROFS and the discrimination measure is generally used to quantify the distance between two q-ROFSs by evaluating the amount of their discrimination. Thus, the aim of this study is to propose a novel integrated framework based on multi-attribute multi-objective optimization with the ratio analysis (MULTIMOORA) method with q-rung orthopair fuzzy information (q-ROFI). In this approach, an integrated weighting process is presented by combining objective and subjective weights of criteria with q-ROFI. Inspired by the q-rung orthopair fuzzy entropy and discrimination measure, objective weights of criteria are estimated by entropy and discrimination measure-based model. Whereas, the subjective weights are derived based on aggregation operator and the score function under q-ROFS environment. In this respect, novel entropy and discrimination measure are proposed for q-ROFSs. Furthermore, to display the feasibility and usefulness of the introduced approach, a case study related to SWD method selection is presented under q-ROFS perspective. Finally, comparison and sensitivity investigation are presented to confirm the robustness and solidity of the introduced approach.

Hybridizations of Archimedean copula and generalized MSM operators and their applications in interactive decision-making with q-rung probabilistic dual hesitant fuzzy environment

“…In further research, other aggregation operators (Saha et al, 2022a;Saha et al, 2021a;Senapati, 2021;Senapati et al, 2022;Saha et al, 2022b;Saha et al, 2021b) can also be extended to tackle the dependency among attributes with qRPDHF information and the proposed weight determination technique. Our model can be used to provide a realistic solution to well-known problems, such as sustainable supplier selection (Mishra et al, 2022a), warehouse site selection (Saha et al, 2023), bio-energy production technology selection (Hezam et al, 2023), solid waste disposal method selection (Mishra et al, 2022b), renewable energy source…”

Mathematical Modelling Of Municipal Solid Waste Management In Spherical Fuzzy Environment

“…The latest articles based on MSWM in Indian cities [21,22,23,24,25,26,27,28] discussed about challenges, current status, sustainable waste management, life-cycle assessment and so on but the concept of mathematical modelling is not introduced by any author. Moreover, some authors worked on waste management issues at global level like [29,30,31,32,33,34,35,36]. In article [37] the authors worked on Forecasting of MSW generation using non-linear auto regressive neural models.…”

A Lance Distance-Based MAIRCA Method for q-Rung Orthopair Fuzzy MCDM with Completely Unknown Weight Information

“…In the q-ROF context, apart from the development of various aggregation operators (Peng and Luo, 2021;Saha et al, 2022a), the alternative ranking techniques have become one of the focuses of many scholars. So far, various types of decision-making techniques have been extended and utilized in the q-ROF environment, and these methods can be classified according to their characteristics as: (1) the distance-based methods, such as TOPSIS (Dincer et al, 2022;Pinar et al, 2021;Ye et al, 2021;Alkan and Kahraman, 2021;Pinar and Boran, 2020;Khan et al, 2021b), TODIM (Krishankumar et al, 2021;Chen et al, 2021;Arya and Kumar, 2021;Liu et al, 2021;Wang and Li, 2018), VIKOR (Khan et al, 2021a;Sun et al, 2021), CODAS (Deveci et al, 2022a), EDAS (Darko and Liang, 2020;Liang et al, 2023), andMABAC (Gong et al, 2020;Wang et al, 2020a); (2) the utility-based approaches, such as WASPAS (Deveci et al, 2022b;Xiao et al, 2022), ARAS (Mishra and Rani, 2021), COPRAS (Krishankumar et al, 2019) and MARCOS (Ali, 2022); (3) the distance-and utility-based hybrid approaches, such as MULTIMOORA (Mishra et al, 2022;Riaz et al, 2022;Aydemir and Gunduz, 2020), PROMETHEE Akram and Shumaiza, 2021;Zhang et al, 2021b), DNMA (Saha et al, 2022b), CoCoSo (Deveci et al, 2022c), and GLDS Liao et al, 2020); (4) other methods, such as ORESTE (Long and Liao, 2021) and Thermodynamic Zhang et al, 2021a). These aforementioned decision-making approaches have been widely applied to handle complex decision i...…”