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Many developed countries have been using environmental taxes in their economic systems for many years. These taxes have a great impact on reducing the environmental damages of companies and individuals in society. But many developing countries have not used this tool effectively yet, and some countries face barriers to the effective implementation of environmental taxes that make it difficult and unsuccessful. To increase the effectiveness of the implementation of environmental taxes, governments must prioritize barriers and solutions to overcome its barriers. The identified knowledge gap of the pre-literature review is that an overview of the identification which completely considers all barriers and solutions of environmental taxes implementation does not exist. In response to this knowledge gap, this study aims to identify and prioritize the barriers and solutions of environmental taxes implementation.

Ranking the barriers and solutions is a complicated multi-criteria decision making (MCDM) problem that requires consideration of multiple feasible alternatives and conflicting tangible and intangible criteria. This study addresses the prioritization of solutions of Environmental Taxes implementation by proposing hybrid MCDM methods based on the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) and the Fuzzy Technique for order preference by similarity to an ideal solution (Fuzzy-TOPSIS) under fuzzy environment. Fuzzy AHP is used to determine the weight of each barrier using a pairwise comparison, and fuzzy TOPSIS is used to finalize the ranking of solutions for more effective implementation of environmental taxes.

The results showed that environmental tax reform (ETR) (S3) has the highest value among the solutions for more effective implementation of environmental taxes. The result of the proposed model is validated by performing sensitivity analysis.

This study could foster research on the discussion of these barriers and precise ways of implementing solutions to pay more attention to environmental taxes.

Ratings of solutions can be a guide and help governments to improve the implementation of environmental taxes or even develop this policy by being aware of the ranking of barriers and solutions.

This paper creates a new perspective on the effective implementation of environmental taxes, which is closely related to improving environmental performance and increasing social welfare through improving the tax system.

For the first time, this study comprehensively identifies barriers and solutions for more effective implementation of environmental taxes and ranks them using two MCDM techniques.

In this paper, two popular multiple-criteria decision-making (MCDM) methods with hesitant fuzzy logic approach; hesitant fuzzy analytic hierarchy process (hesitant F-AHP) and hesitant fuzzy the technique for order preference by similarity to ideal solution (HF-TOPSIS) are integrated as HF-AHP-TOPSIS to evaluating a set of enterprise resource planning (ERP) alternatives and rank them by weight to reach to the ultimate one that satisfies the needs and expectations of a company.

Selecting the best ERP software package among the rising number of the options in market has been a critical problem for most companies for a long time because of the reason that an improper ERP software package might lead to many issues (i.e. time loss, increased costs and a loss of market share). On the other hand, finding the best ERP alternative is a comprehensive MCDM problem in the presence of a set of alternatives and several potentially competing quantitative and qualitative criteria.

In this integrated approach, the hesitant F-AHP is used to determine the criteria weights, as the hesitant F-TOPSIS is utilized to rank ERP package alternatives. The proposed approach was also validated in a numerical example that has five ERP package alternatives and 12 criteria by three decision-makers in order to show its applicability to potential readers and practitioners.

If the number of the alternatives and criteria are dramatically increased beyond reasonable numbers, the reaching to final solution will be so difficult because of the great deal of fuzzy based calculations. Therefore, the number of criteria and alternatives should be at reasonable numbers.

The proposed approach was also validated in a illustrated example with the five ERP package options and 12 criteria by the three decision-makers in order to show its applicability to potential readers and practitioners.

Furthermore, in literature, to the best of our knowledge, the authors did not come cross any work that integrates the HF-AHP with the HF-TOPSIS for ERP software package selection problem.

To develop a multi‐attribute decision making model for evaluating and selecting among logistic information technologies.

First a multi‐attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented.

Reviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone for future business; policy issues as risk and necessity level; resources as costs and completion time. Presents a methodology that is developed for the complex, uncertain and vague characteristics of the problem.

Comparisons with other multi‐attribute decision making techniques such as AHP, ELECTRE, PROMETHEE and ORESTE under fuzzy conditions can be done for further research.

This article is a very useful source of information both for logistic managers and stakeholders in making decisions about logistic information technology investments.

This paper addresses the logistic information technology evaluation and selection criteria for practitioners and proposes a new multi‐attribute decision making methodology, hierarchical fuzzy TOPSIS, for the problem.

The purpose of this paper is to present one modification of the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and to develop a corresponding computer program which could be used for the multicriteria decision making for problems in practice.

This method is based on the uncertainties and probabilities of input data for ratings of alternatives with respect to criteria and weights of criteria that are presented by triangular fuzzy numbers as probabilistic fuzzy values. These input data are transformed in the procedure into output data that are relevant for the ranking of alternatives and decision making.

The proposed method is based on the generalized mean and spread of fuzzy numbers that are calculated according to probability of fuzzy events due to Zadeh. Ranking of alternatives for relevant criteria performs according to relative expected closeness, coefficient of variation and relative standard deviation of distance of alternatives to the ideal solutions. The most acceptable rule is related to the minimal value of the expected relative distance to positive ideal solution, especially when the coefficient of variation of distance to this solution is small. The attached example, related to a real project, confirms these findings.

This paper proposes three novel contributions in this area. Unlike the methods proposed by other authors, the weighted fuzzy decision matrix is expressed by the matrix of generalized expected values and matrix of generalized variances. To compute elements of these two matrices, exact formulae are derived and then the modified fuzzy TOPSIS procedure is carried out.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This paper aims to presents a novel integrated fuzzy decision support system for analyzing the issues related to failure of a milk process plant unit.

Process failure mode effect analysis (PFMEA) approach was implemented to list failure causes under each subsystem/component and fuzzy ratings for three risk criteria, i.e. probability of failure occurrence (O_f), severity (S) and non-detection (O_d) are collected against the listed failure causes through experts feedback. A new doubly technique for order of preference by similarity to ideal solution (DTOPSIS) approach was implemented within fuzzy PFMEA tool for ranking of listed failure causes. The proposed decision support system overcomes the restrictions of classical PFMEA and IF-THEN rule base PFMEA approaches in an effective way.

Failure causes such as electrical winding failure (RM4), high pressure in plate region (C1), communication problem in supervisory control and data acquisition control (MS3), insulation problem (ST2), lever breakage (B2), gasket problem (D3), formation of holes (PHE5), cavitations (FP7), deposition of milk particle inside the pipeline because of improper cleaning (MHP2) were acknowledged as the most critical one with the application of proposed decision support system.

The analysis results are based on subjective judgments of the experts and therefore correctness of risk ranking results are totally dependent upon the quality of input data/information available from these experts. However, the analyst has taken proper care for considering the vagueness of the raw data by incorporating fuzzy set theory within the proposed decision support system.

The proposed fuzzy decision support system has been presented with its application on milk pasteurization plant of a milk process industry. The analysis based ranking results have been supplied to maintenance manager of the plant and a consent was shown by him with these results. Once the top management of the plant took decision for the implementation of these results, the detailed robustness of the proposed decision support system could be evaluated further.

The analysis result would be highly useful for minimizing sudden breakdowns and operational cost of the plant which directly contributes to plant's profitability. With the decrease in the chances of sudden breakdowns there would be high safety for the people working on/off the plant's site. Further, with increase in availability of the considered plant the societal daily demand related to dairy products could be easily fulfilled at reasonable prices.

The performance and proficiency of the proposed decision support system has been evaluated by comparing the ranking results with classical TOPSIS and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approaches based results.

In this paper, the four popular multiple-criteria decision-making (MCDM) methods in fuzzy environment are utilized to reflect the vagueness and uncertainty on the judgments of decision-makers (DMs), because the crisp pairwise comparison in these conventional MCDM methods seems to be insufficient and imprecise to capture the right judgments of DMs. Of these methods, as Fuzzy analytic hierarchy process (F-AHP) is used to calculate criteria weights, the other methods; Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Grey relational analysis (F-GRA) and Fuzzy Preference Ranking Organization METhod for Enrichment of Evaluations (F- PROMETHEE II) are used to rank alternatives in the three different ways for a comparative study.

The demand for green products has dramatically increased because the importance and public awareness of the preservation of natural environment was taken into consideration much more in the last two decades. As a result of this, especially manufacturing companies have been forced to design more green products, resulting in a problem of how they incorporate environmental issues into their design and evaluate concept options. The need for the practical decision-making tools to address this problem is rapidly evolving since the problem turns into an MCDM problem in the presence of a set of green concept alternatives and criteria.

The incorporation of fuzzy set theory into these methods is discussed on a real-life case study, and a comparative analysis is done by using its numerical results in which the three fuzzy-based methods reveal the same outcomes (or rankings), while F-GRA requires less computational steps. Moreover, more detailed analyses on the numerical results of the case study are completed on the normalization methods, distance metrics, aggregation functions, defuzzification methods and other issues.

The designing and manufacturing environmental-friendly products in a product design process has been a vital issue for many companies which take care of reflecting environmental issues into their product design and meeting standards of recent green guidelines. These companies have utilized these guidelines by following special procedures at the design phase. Along the design process consisting of various steps, the environmental issues have been considered an important factor in the end-of-life of products since it can reduce the impact on the nature. In the stage of developing a new product with the aim of environmental-friendly design, the green thinking should be incorporated as early as possible in the process.

The case study was inspired from the previous work of the author, which was realized in a hot runner systems manufacturer, used in injection molding systems in a Canada. In a new product development process, the back- and front-ends of development efforts mainly determine the following criteria: cost, risk, quality and green used in this paper. The case study showed that the three fuzzy MCDM methods come to the same ranking outcomes. F-GRA has a better time complexity compared to the other two methods and uses a smaller number of computational steps. Moreover, a comparative analysis of the three F-MCDM methods; F-PROMETHEE II, F-TOPSIS and F-GRA used in ranking for green concept alternatives using the numerical results of the case study. For the case study; as seen in table 20, the three F-MCDM methods produced the numerical results on the rankings of the green concept alternatives as follows; {Concept A-Concept C–Concept B–Concept D}.

Inclusion of environmental-related criteria into concept selection problem has been gaining increasing importance in the last decade. Therefore, to facilitate necessary calculations in applying each method especially with its fuzzy extension, it can be developed a knowledge-based (KB) or an expert system (ES) to help the DMs make the required calculations of each method, and interpret its results with detailed analysis.

The objective of the research was to propose a F-AHP based F-MCDM approach to green concept selection problem through F-PROMETHEE II, F-TOPSIS and F-GRA methods. As the F-AHP is used to weight evaluation criteria, the other methods are respectively used for ranking the concept alternatives and determine the best concept alternative.

Supplier selection in the supply chain network (SCN) has strategic importance and involves multiple factors. The multi-criteria nature of the problem coupled with environmental uncertainty requires several procedures and considerations. The issue of decision-making in selecting the best among various qualified suppliers remains the major challenge in the pharmaceutical industry. This study investigated the multi-criteria multi-supplier decision-making process and proposed a model for supplier selection problems based on mixed-integer linear programming.

The concept of principal component analysis (PCA) was used to reduce data dimensionality, and the four best criteria have been considered and selected. The result is subjected to decision-makers’ (DMs’) reliability test using the concept of a triangular fuzzy number (TFN). The importance of each supplier to each measure is established using fuzzy technique for order preference by similarity to an ideal solution approach, and the suppliers have ranked accordingly.

This study proposes a mixed integer linear programming model for supplier selection in a pharmaceutical company. The effectiveness of the proposed model has been demonstrated using a numerical example. The solution shows the model's applicability in making a sound decision in pharmaceutical companies in the space of reality. The model proposed is simple. Readily commercial packages such as LINDO/LINGO and GAMS can solve the model.

This research contributed to the systematic manner of supplier selection considering DMs’ value judgement under a fuzzy environment and is limited to the case study area. However, interested researchers can apply the study in other related manufacturing industries. However, the criteria have to be revisited to suit that system and might require varying ratings based on the experts' opinions in that field.

This work suggests more insights practically by considering a realistic and precise investigation based on a real-life case study of pharmaceutical companies with six primary criteria and twenty-four sub-criteria. The study outcome will assist organizations and managers in conducting the best decision objectively by selecting the best suppliers with their various standards and terms among many available contenders in the manufacturing industry.

In this paper, the authors attempted to identify the most critical attributes to be preserved by the top managers (DMs) while selecting suppliers in pharmaceutical companies. The study proposed an MILP model for supplier selection in the pharmaceutical company using fuzzy TOPSIS.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

This study aims to propose strategies to address the identified major barriers for giving the public open access to government data. The study adopts fuzzy analytical hierarchy process and technique for order performance by similarity to ideal solution (AHP-TOPSIS) to weigh the barriers and strategies, and it subsequently involves experts to identify and weigh the barriers and strategies. A case of Indonesia is used to contextualize the study.

The data were collected using fuzzy AHP-TOPSIS-based questionnaires given to several government representatives who had been working with data and information. The respondents were given sets of pairwise comparisons of which they were asked to compare the level of importance using one to nine fuzzy numbers between barriers and strategies. The data were then calculated using the fuzzy AHP-TOPSIS formula to obtain each weight of the barriers and strategies. The weight is used to prioritize the barrier and strategies.

In total, five barrier categories in the order of importance, namely, legal and privacy; government culture; social; technical; and economic, were identified from 27 barriers. In total, ten strategies of open government data (OGD) adoption were identified and ranked in the order of importance, and they can be grouped into five priorities. Priority 1 is to involve stakeholders in OGD planning and establish an OGD competence center. Priority 2 is to develop a legal compliance framework. Priority 3 is to adopt OGD gradually. Priority 4 is to create a collaboration feature on the portal for stakeholder communication and raise public awareness of OGD. Priority 5, finally, is to conduct training for government officials, develop standard operating practice for OGD management, use standard data formats and provide metadata.

This study provides a perspective from the government’s view. One suggestion for future research is to conduct a study from the public’s perspective to formulate strategies based on the identified citizens’ barriers in using OGD. In addition, cross-country (of different characteristics) studies were required to generalize the findings.

The first strategy of the first priority implies that government institutions should be able to develop a preliminary plan to involve relevant stakeholders in OGD planning, which includes identifying relevant stakeholders and continuously engaging them to participate in the planning phase of OGD. The second strategy in the first priority entails that government institutions should realize an OGD competence center by creating a virtual team whose members are from various backgrounds and who are very knowledgeable about OGD and how to manage OGD in government institutions.

This research provides key strategies to address the main barriers to giving the public open access to government data.