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The global warming, caused by the anthropogenic greenhouse gases, has been one of the major worldwide issues over the last decades. Among them, carbon dioxide (CO₂) is the most important one and is responsible for more than the two-third of the greenhouse effect. Currently, greenhouse gas emissions and CO₂ emissions – the root cause of the global warming – in particular are being examined closely in the fields of science and they also have been put on the agenda of the political leaders. The purpose of this paper is to predict the energy-related CO₂ emissions through using different discrete grey models (DGMs) in Turkey and total Europe and Eurasia region.

The proposed DGMs will be applied to predict CO₂ emissions in Turkey and total Europe and Eurasia region from 2015 to 2030 using data set between 1965 and 2014. In the first stage of the study, DGMs without rolling mechanism (RM) will be used. In the second stage, DGMs with RM are constructed where the length of the rolling horizons of the respected models is optimised.

In the first stage, estimated values show that non-homogeneous DGM is the best method to predict Turkey’s energy-related CO₂ emissions whereas DGM is the best method to predict the energy-related CO₂ emissions for total Europe and Eurasia region. According to the results in the second stage, NDGM with RM (k=26) is the best method for Turkey while optimised DGM with RM (k=4) delivers most reliable estimates for total Europe and Eurasia region.

This study illustrates the effect of different DGM approaches on the estimation performance for the Turkish energy-related CO₂ emission data.

The purpose of this paper is to develop a systematic and comprehensive project selection model utilizing fuzzy multi-objective linear programming (FMOLP) that deals with the imprecise data in IS projects and uncertain judgment of decision makers.

First, projects are prioritized by considering both quantitative and qualitative factors. A fuzzy analytical hierarchical process (FAHP) is used in order to obtain weights of each project that indicates their priorities. At the second step, project selection decision is completed by using FMOLP. Then, the sensitivity analysis is performed to evaluate the robustness of the proposed integrated model.

The result of this study indicates that an integrated approach utilizing FAHP and FMOLP can be used as a supportive tool for project selection in IS context. It decreases the uncertainty caused from uncertain judgment of decision makers.

Future studies are suggested to design models having fuzzy constraints such as budget and resources. Moreover, for future studies, non-linear membership functions can be used.

Actual projects are provided from the Turkish IS company for prioritizing process and a hypothetical mathematical model is demonstrated using illustrative data.

This study contributes to the relevant literature by proposing a comprehensive model considering many conflicting ideas of decision makers on quantitative and qualitative criteria, and evaluating projects in an integrated way including FAHP and FMOLP.

The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network.

The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted.

The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas.

In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other expert systems such as artificial neural networks can be integrated into fuzzy systems.

An application at an e-recycling facility is conducted for clarifying how the method is used in a real decision process.

It is the first study which aims to model an alternative forecasting by utilizing fuzzy expert system. Furthermore, a comprehensive factor list which includes predictors of the system is defined. Then, a dimension redundancy analysis is developed to reveal factors having significant impact on the return process and eliminate the rest.

The aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.

First, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.

This paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.

The main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

Despite being a low-tech industry, woodwork manufacturing industry that includes furniture and cabinet making, witnessed technological leaps in production technologies due to technical developments in computer numerical control (CNC) machining processes. The managers of this industry have attached high importance to the selection of efficient machines as their decisions directly affect the quality and performance of products produced by the firms. Improper selection process can result in a significant decrease in productivity and flexibility. Therefore, a systematic decision-making procedure is needed to prevent inaccurate investments on machines. The purpose of this paper is to purpose a hesitant fuzzy analytic hierarchy process (HFAHP) based multi-criteria decision making (MCDM) system for CNC router selection in small- and medium-sized enterprises (SMEs) in woodwork manufacturing.

The study proposes a hierarchical model consisting of 4 main criteria and 11sub-criteria for woodwork manufacturing. Technical, personnel, economic and vendor aspects constitute the main criteria. Because of the hierarchical structure of the model, HFAHP is utilized to define the importance weights of the criteria, and to select the most appropriate CNC alternative for a manufacturing company under focus. In a selection procedure, the judgments of decision makers may have vagueness to specify the importance of criteria affecting the decision process. In the literature, the fuzzy set theory has been utilized to deal with such uncertainties. However, when the ideas of the managers have high potential to fall into contradiction in pairwise comparisons, a novel approach is needed to overcome the obstacles. HFAHP allows the membership degree having a set of possible values. It is specifically useful in compromised decisions where experts cannot agree on a single value and prefer to come up with an interval of linguistic variables.

It is revealed that for SMEs in woodwork manufacturing, the most important criterion in selecting the CNC routers is the technical aspects. It may seem counter intuitive that they do not refrain finding the technical criteria superior to the economic aspects, even though they have limited budgets compared to large-scale firms. This demonstrates that in current competitive environment, SMEs understand the need for high-quality production strategy. The weights of the remaining two criteria (personnel and vendor aspects) are relatively low because they expect that they can easily overcome the problem of adapting the workers by training, and all vendors have quality standard qualifications so they can offer a satisfactory service and supplementary systems.

The ready-to-use model proposed is specialized for SMEs in woodwork manufacturing. However, to make it an easily adaptable model for every company in the woodwork industry regardless of its size, the calculation process of the priority weights is illustrated in detail with a numerical example. Any company can follow the process using their own preferences to end up with a specific model that will perfectly reflect their own specific priorities. For demonstrating the application of the model, a case study is conducted in a woodwork manufacturing SME to select the best CNC router among three alternatives.

The originality and value of the paper is twofold. First, to the best of our knowledge, this is the first study that proposes a woodworking-specific CNC router selection for SMEs. Second, to handle the high uncertainty in the judgements, and to facilitate consensus among the experts during face to face meetings to develop compromised matrices, a very recently developed method, HFAHP is used.

ERP selection is a multi-faceted process and needs to be successful in dealing with high uncertainty. The purpose of this paper is to propose a novel multi-criteria decision making (MCDM) approach, titled as cloud-based design optimization (CBDO), for ERP selection problem to handle high uncertainty with a computationally effective way.

CBDO has been utilized as an alternative method to fuzzy set theory and stochastic programming, and proposes robust findings for worst case scenario. In order to assess the proposed methodology, a numerical study is conducted by taking into account existing state-of-the-art study on the ERP selection problem for the small medium enterprises. The outputs of the existing state-of-the-art study are assumed as uncertain and varying across time as it is expected in real life; therefore, different scenarios are created in order to reveal the effect of uncertainty on decisions.

In the methodology, the results given under uncertain conditions are compared with the results obtained under stable conditions. It is clearly seen that ERP system selection problem area has high sensitivity to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during the ERP system selection process, especially within volatile economies.

This study contributes to the relevant literature by utilizing CBDO as a MCDM tool in the selection of the ERP software as a first time, and validating the impact of unsteadiness on the ERP selection procedure. It is the first CBDO-based study that validates the effect of distributional differences on uncertainties in the ERP selection processes.

The purpose of this paper is to determine which criteria should be taken into account while choosing face masks for pandemic times and to what extent their effects are.

Nine face mask alternatives were evaluated based on the assessments of their performance with respect to twelve attributes. Seven experts were asked to evaluate the mask alternatives and the influences among attributes. In gathering expert judgments, spherical fuzzy number-based linguistic terms were utilized in the study to provide a more comprehensive representation domain to them.

According to the results, the most important attributes are found as material type, cost and bacteria–virus protection level. The best face mask is N95, which is followed by respirators and surgical masks.

The implication of the research is to evaluate face masks in terms of criteria such as physical, performance, protection and cost to decide on what basis they were selected as a personal protective equipment (PPE) based on expert assessments. This is useful in selection of the right face mask with optimum performance and provides guidance to the general public and profession specific groups for this purpose. The face mask companies might be also benefitted from the implications of the present study in their design and research and development (R&D) operations.

The preference ranking of the face mask alternatives has not been studied in detail yet in the literature. Focusing on this issue, the present study provides a comprehensive assessment of the selection criteria of face masks in the pandemic era.

As environmental protection and sustainable development become more widely recognized, greater emphasis has been placed on the significance of green supplier selection (GSS), which can support businesses both upstream and downstream in enhancing their environmental performance while preserving their strategic competitiveness. Therefore, this paper aims to propose a new framework to study GSS.

Firstly, this paper establishes a GSS evaluation criteria system including product competitiveness, green performance, quality of service and enterprise social responsibility. Secondly, based on the spherical fuzzy sets (SFSs), the Average Induction Ordered Weighted Averaging Operator-Criteria Importance Through Inter Criteria Correlation (AIOWA-CRITIC) method is used to determine the subjective and objective weights and the combination of weights are determined by game theory. In addition, the GSS framework is constructed by the Cumulative Prospect Theory-Technique for Order Preference by Similarity to Ideal Solution (CPT-TOPSIS) method. Finally, the validity and robustness of the framework is verified through sensitivity comparative and ablation analysis.

The results show that Y3 is the most promising green supplier in China. This study provides a feasible guidance for GSS, which is important for the greening process of the whole supply chain.

Under spherical fuzzy sets, AIOWA and CRITIC are used to determine weights of indicators. CPT and TOPSIS are combined to construct a decision model, considering the ambiguity and uncertainty of information and the risk attitudes of decision-makers.

Comprehensive project portfolio risk (PPR) analysis is essential for the success and sustainable development of project portfolios (PPs). However, project interdependency creates complexity for PPR analysis. In this study, considering the interdependency effect among projects, the authors develop a quantitative evaluation model to analyze PPR based on a fuzzy Bayesian network.

In this paper, the primary purpose is to comprehensively evaluate project portfolio risk considering the interdependency effect using a systematical model. Accordingly, a fuzzy Bayesian network (FBN) is developed based on the existing studies. Specifically, first, the risks in project portfolios are identified from the project interdependencies perspective. Second, a fuzzy Bayesian network is adopted to model and quantify the interaction relationships among risks. Finally, the model is implemented to analyze the occurrence situation and characteristics of risks.

The interdependency effect can lead to high-stake risks, including weak financial liquidity, a lack of cross-project members and project priority imbalance. Furthermore, project schedule risks and inconsistency between product supply and market demand are relatively sensitive and should also be prioritized. Also, the validity of this risk evaluation model has been proved.

The findings identify the most sensitive risks for guaranteeing portfolio implementation and reveal interdependency effect can trigger some specific risks more often. This study proposes for the first time to measure and analyze project portfolio risk by a systematical model. It can help systematically assess and manage the complicated and interdependent risks associated with project portfolios.