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In this article, we develop the COPRAS model to solve the multiple attribute group decision making (MAGDM) under single-valued neutrosophic 2-tuple linguistic sets (SVN2TLSs). Firstly, we introduce the relevant knowledge about SVN2TLSs in a nutshell, such as the definition, the operation laws, a few of fused operators and so on. Then, combine the traditional COPRAS model with SVN2TLNs, and structure as well as elucidate the computing steps of the SVN2TLNCOPRAS pattern. Furthermore, in this article, we propose a method for determining attribute weights in different situations relying on the maximizing deviation method with SVN2TLNs. Last but not least, a numerical example about assessing the safety of construction project has been designed. And for further demonstrating the advantage of the new designed method, we also select a number of existed methods to have comparisons. First published online 13 January 2021

Sustainability evaluation involves a structured procedure to appraise the impacts of an undertaking on the environment, society, and economy. It aims to determine whether the actions or decisions are in line with sustainable development goals and can provide long-term benefits while minimizing negative consequences on the planet and society. In the current competitive market, the idea of sustainability is progressively gaining significance in modern manufacturing practices. This research employs a multi-criteria decision-making framework to assess sustainability, specifically focusing on enhancing the machining performance of Inconel 690 superalloy through the utilization of eco-friendly lubricating/cooling agents. The results demonstrated significant improvements, including an 80.64% reduction in Flank wear, a 43.08% reduction in Surface roughness, a 15.58% reduction in Machining cost, an 11.57% reduction in Carbon emissions, and a 25.91% reduction in Noise generation under the Cryo-MQL medium compared to dry machining conditions. The sustainability index (SI) for Taguchi-designed sixteen experimental runs is determined by the Analytic Hierarchy Process (AHP) coupled with the Complex Proportional Assessment (COPRAS) approach. The AHP-COPRAS approach was chosen due to its ability to systematically integrate conflicting criteria while minimizing subjective biases, offering a more robust and comprehensive sustainability evaluation framework compared to other MCDM methods. This claims that a cutting speed of 80 m/min, feed of 0.1 mm/tooth, depth of cut of 1 mm, and Cryo-MQL medium are the best parametric settings for improving the sustainability of Inconel 690 machining. Furthermore, a sensitivity analysis of the criteria weights for ranking the machining experiments demonstrates the robustness of the proposed sustainability evaluation method.

Sustainable supplier selection (SSS) is gaining popularity as a practical method to supply chain sustainability among academics and practitioners. However, in addition to balancing economic, social, and environmental factors, the emergence of the COVID-19 pandemic has affected the selection of long-term suppliers to ensure sustainable supply chains, recover better from the pandemic and effectively respond to any future unprecedented crises. The purpose of this study is to assess and choose a possible supplier based on their capability to adapt to the COVID-19 epidemic in a sustainable manner. For this assessment, a framework based on multi-criteria decision making (MCDM) is provided that integrates spherical fuzzy Analytical Hierarchical Process (SF-AHP) and grey Complex Proportional Assessment (G-COPRAS), in which spherical fuzzy sets and grey numbers are used to express the ambiguous linguistic evaluation statements of experts. In the first stage, the evaluation criteria system is identified through a literature review and experts’ opinions. The SF-AHP is then used to determine the criteria weights. Finally, the G-COPRAS method is utilized to select sustainable suppliers. A case study in the automotive industry in Vietnam is presented to demonstrate the proposed approach’s effectiveness. From the SF-AHP findings, “quality”, “use of personal protective equipment”, “cost/price”, “safety and health practices and wellbeing of suppliers”, and “economic recovery programs” have been ranked as the five most important criteria. From G-COPRAS analysis, THACO Parts (Supplier 02) is the best supplier. A sensitivity study was also conducted to verify the robustness of the proposed model, in which the priority rankings of the best suppliers are very similar. For long-term development and increased competitiveness, industrial businesses must stress the integration of response mechanisms during SSS implementation in the COVID-19 epidemic, according to the findings. This will result in significant cost and resource savings, as well as reduced environmental consequences and a long-term supply chain, independent of the crisis.

Despite several methods available for the treatment of solid wastes, the management of municipal solid waste is still a crucial and complex process. The available methods for waste treatment range from advanced to conventional techniques. The identification of a proper method for municipal solid waste management involves several techno-eco and environmental considerations. To solve the real-world problems of municipal waste management, the research proposed an integrated q-rung orthopair fuzzy number-based stepwise weight assessment ratio analysis-complex proportional assessment (SWARA-COPRAS) mathematical model to rank the waste treatment techniques. The research aimed to develop a systematic approach for a suitable selection of waste treatment methods. Ten (10) different alternatives for waste treatments were ranked against seven (07) different techno-eco and environmental criteria. The ambiguity in the decision was handled by the q-rung orthopair fuzzy numbers. The proposed integrated model has identified upcycling and recycling of waste having priority values of 100% and 99.9%, respectively, as the suitable practices for the successful management of generated solid wastes, whereas landfilling has obtained a minimum priority value of 66.782% and, therefore, is least preferable for waste management. The ranking of the alternatives followed the sequence as upcycling > recycling > pyrolysis > hydrolysis > biotechnological > core plasma pyrolysis > incineration > composting > gasification > landfilling. The comparison between the rankings of the proposed model with other techniques has revealed that the values of Spearman’s rank correlation coefficient are in the range of 0.8545 to 0.9272; thereby, the robustness of the proposed model is verified. Sensitivity analysis for the criteria weight has showed that the ranking results are influenced significantly by the change in criteria weights and suggested that an accurate estimation of the criteria weight is decisive in determining the overall ranking of the alternative. The study has provided a framework for decision-making in the technology selection for solid waste management. Graphical Abstract

Sustainable development is one of the most important preconditions for preserving resources and balanced functioning of a complete supply chain in different areas. Taking into account the complexity of sustainable development and a supply chain, different decisions have to be made day-to-day, requiring the consideration of different parameters. One of the most important decisions in a sustainable supply chain is the selection of a sustainable supplier and, often the applied methodology is multi-criteria decision-making (MCDM). In this paper, a new hybrid MCDM model for evaluating and selecting suppliers in a sustainable supply chain for a construction company has been developed. The evaluation and selection of suppliers have been carried out on the basis of 21 criteria that belong to all aspects of sustainability. The determination of the weight values of criteria has been performed applying the full consistency method (FUCOM), while a new rough complex proportional assessment (COPRAS) method has been developed to evaluate the alternatives. The rough Dombi aggregator has been used for averaging in group decision-making while evaluating the significance of criteria and assessing the alternatives. The obtained results have been checked and confirmed using a sensitivity analysis that implies a four-phase procedure. In the first phase, the change of criteria weight was performed, while, in the second phase, rough additive ratio assessment (ARAS), rough weighted aggregated sum product assessment (WASPAS), rough simple additive weighting (SAW), and rough multi-attributive border approximation area comparison (MABAC) have been applied. The third phase involves changing the parameter ρ in the modeling of rough Dombi aggregator, and the fourth phase includes the calculation of Spearman’s correlation coefficient (SCC) that shows a high correlation of ranks.

This paper presents a new approach to the treatment of uncertainty and imprecision in multi-criteria decision-making based on interval rough numbers (IRN). The IRN-based approach provides decision-making using only internal knowledge for the data and operational information of a decision-maker. A new normalized weighted geometric Bonferroni mean operator is developed on the basis of the IRN for the aggregation of the IRN (IRNWGBM). Testing of the IRNWGBM operator is performed through the application in a hybrid IR-DEMATEL-COPRAS multi-criteria model which is tested on real case of selection of optimal direction for the creation of a temporary military route. The first part of hybrid model is the IRN DEMATEL model, which provides objective expert evaluation of criteria under the conditions of uncertainty and imprecision. In the second part of the model, the evaluation is carried out using the new interval rough COPRAS technique.

Project risk management which has been rarely considered, especially in software projects, is a crucial process to complete projects successfully. This paper aims to propose a novel project risk management framework both to evaluate the project risks and to predict the success or failure of software projects based on their risk. This new framework uses Machine Learning (ML) and Multi-Attribute Decision-Making under a cloud model to effectively manage uncertainty. Based on the proposed framework, in the first stage, the important risks of the software projects are identified by an organized approach. Then, the risks are evaluated based on their probability and impacts on time, cost, and quality. In the second stage, the obtained results of the previous stage are entered into a new COPRAS method under the cloud model to rank the risks. Then, the risks are classified into various groups according to their rank. It helps project managers to gain a profound awareness of their high-priority project risks. In this paper, data on risks for fifty software projects has been collected. All the steps of the second stage are implemented on these projects in order to assess their risks. As a result, a data set whose features are nine types of software project risks and the label of success or failure of the projects is created. To recognize the pattern between risks’ values and the success or failure of the projects, various efficient ML algorithms such as Naive Bayes, Logistic regression, Decision Tree, Bagging, Random Forest, and AdaBoost are applied. This framework can predict the success or failure of software projects based on their risks with good accuracy. The results depict that the Naive Bayes algorithm has the best results compared to others.

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is one of the successful extensions of the hesitant fuzzy linguistic term set used for describing the uncertain information in a more prominent manner for solving the group decision-making problems. In DHHFLTS, the membership functions are represented in terms of linguistic membership degrees which are a flexible structure for preference elicitation and enrich the ability for rational decision-making with complex linguistic expressions. Driven by the flexibility of DHHFLTS, in this paper, a new decision framework is developed for solving decision-making problems, which provides scientific and rational decisions based on the preference information. For it, initially, a new aggregation operator is proposed for aggregating decision-makers’ preferences. Later, the importance of the attribute weights in the problems is determined by formulating a mathematical model and the COPRAS method is extended to the DHHFLTS context for prioritizing alternatives. The applicability of the presented approach is demonstrated through a numeric example related to green supplier selection. A comparative analysis with existing studies is also administered to test the effectiveness and verify the method.

Facilitation of suitable accommodation for different travellers is the prime concern of travel agencies. Travel agencies must keep themselves competitive and sustain a good pace of growth to continue raising profits by attracting and retaining as many tourists as possible through meeting their various prospective needs. To achieve this, the agencies must prepare well-organised data for hotels and destinations from a quality control perspective. Initially, the hotels are ranked and evaluated according to performance across several criteria from the tourists' viewpoint. The relative importance of each criterion is mainly subjective and depends on the assessor's judgement. Additionally, hotels' rankings vary across different websites, resulting in inconsistencies. To handle such inconsistencies and subjectivity, this paper presents a collective decision-making evaluation framework by integrating a weighted interval rough number (WIRN) method and a WIRN-based complex proportional assessment (COPRAS) model to evaluate and rank hotels. An empirical example and a real-world case study from the Indian tourism industry are presented to validate the applicability of the proposed framework. Finally, a comparison and sensitivity analysis are performed to examine the validity and robustness of the proposed model.

In the past decade, global competition are forcing firms to increase their level of outsourcing for raw or semi-finished products and building long term relationship with their supply chain partners. The objective is to present a wide-ranging decision making technique for ranking supplier alternatives in view of the effect of selected criteria. A proposed method is developed aiming the usage of Fuzzy-Rasch model applying five point Likert scale for criteria weight and Grey based COmplex PRoportional ASsessment (COPRAS-G) method for evaluating and ranking the potential alternatives, as per criteria. The applicability of the induced methodology for supplier selection problem in all environments is shown through a case study in telecommunication sector. A sensitivity analysis is performed based on changing weight patterns of criteria to show the stability in ranking result of the proposed approach. Further, a comparative analysis between the ranking results of proposed method done with existing grey multi-attribute decision-making methods viz. VIKOR-G, ARAS-G and TOPSIS-G using spearman’s correlation coefficient for checking the reliability of the ranking result.