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Governments around the world instituted guidelines for calculating energy efficiency of vehicles not only by models, but by the whole universe of new vehicles registered. This paper compiles Multi-criteria decision-making (MCDM) studies related to automotive industry. We applied a Systematic Literature Review on MCDM studies published until 2015 to identify patterns on MCDM applications to design vehicles more fuel efficient in order to achieve full compliance with energy efficiency guidelines (e.g., Inovar-Auto). From 339 papers, 45 papers have been identified as describing some MCDM technique and correlation to automotive industry. We classified the most common MCDM technique and application in the automotive industry. Integrated approaches were more usual than individual ones. Application of fuzzy methods to tackle uncertainties in the data was also observed. Despite the maturity in the use of MCDM in several areas of knowledge, and intensive use in the automotive industry, none of them are directly linked to car design for energy efficiency. Analytic Hierarchy Process was identified as the common technique applied in the automotive industry.

The purpose of this paper is to provide a new approach to solving the interval-valued intuitionistic fuzzy stochastic multi-criteria decision-making (MCDM) problems.

To transform the interval-valued intuitionistic fuzzy number (IVIFN) into a computational numerical value, a new precision score (P-score) function is developed based on the degrees of membership, non-membership and hesitation. The prospect decision-making matrix is derived by applying P-score function and Prospect theory. A new criteria weighting model is put forward based on the least square method, the maximizing deviation method and Prospect theory. Consequently, combined criteria weighting model with the prospect decision-making matrix, the integrated prospect value is derived which presents a measurement scale for ranking the order of alternatives.

As a result, the method of the interval-valued intuitionistic fuzzy stochastic MCDM is suggested. In this method, the new P-score function responses the comprehensive information of the criteria. The prospect decision-making matrix can reflect the risk attitude of the decision maker. The new criteria weighting model can express both the subjective considerations of the decision maker and the objective information meaning.

The research results may lack generalizability for other fuzzy decision making because of the chosen research approach for IVIFN decision making. Therefore, researchers are encouraged to test the proposed propositions further.

The developed approach can be applied in many decision-making fields such as selection of renewable energy alternatives, assessment of flexible manufacturing system alternatives and human resource alternatives performance evaluation, etc. where the evaluation values are IVIFNs.

This paper succeeds in studying the interval-valued intuitionistic fuzzy MCDM based on Prospect theory, which has not been reported in the existing academic literature.

This paper proposes a new multi-criteria decision-making method, called the vital-immaterial-mediocre method (VIMM), to determine the weight of multiple conflicting and subjective criteria in a decision-making problem.

The novel method utilizes pairwise comparisons, vector-based procedures and a scoring approach to determine weights of criteria. The VIMM compares alternatives by the three crucial components, namely the vital, immaterial and mediocre criteria. The vital criterion has the largest effect on the final results, followed by the mediocre criterion and then the immaterial criterion, which is the least impactful on the prioritization of alternatives. VIMM is developed in two forms where the first scenario is designed to solve one-goal decision-making problems, while the second scenario embraces multiple goals.

To validate the method’s performance and applicability, VIMM is applied to a problem of sustainable supplier selection. Comparisons between VIMM, analytic hierarchy process (AHP) and best-worst method (BWM) reveal that VIMM significantly requires fewer comparisons. Moreover, VIMM works well with both fractional and integer numbers in its comparison procedures.

As an implication for research, we have added the development of the VIMM under fuzzy and grey environments as the direction for optimization of the method.

As managerial implications, VIMM not only provides less complex process for the evaluation of the criteria in the managerial decision-making process, but it also generates consistent results, which make VIMM a reliable tool to apply to a large number of potential decision-making problems.

As a novel subjective weighting method, there exist five major values that VIMM brings over AHP and BWM methods: VIMM requires fewer comparisons compared with AHP and BWM; it is not sensitive to the number of criteria; as a goal-oriented method, it exclusively takes the decision-making goals into account; it keeps the validity and reliability of the Decision-Makers’ (DMs’) opinions and works well with integer and fractional numbers.

The research paper presents analysis and prioritization of barriers influencing the improvement in the effectiveness of manufacturing system. The purpose of this paper is to develop an integrated fuzzy-based multi-criteria decision-making (F-MCDM) framework to assist management of the case company in the selection of most effective manufacturing system. The framework helps in prioritizing the manufacturing systems on the basis of their effectiveness affected by the barriers.

In this paper, on the basis of experts’ opinion, five barriers have been identified in a brain-storming session. The problem of prioritization of manufacturing system is a multi-criteria decision-making (MCDM) problem and hence is solved by using the F-MCDM approach using dominance matrix.

Manufacturing systems’ effectiveness for Indian industries is influenced by barriers. The prioritization of manufacturing systems depends on qualitative factor decision-making criteria. Among the manufacturing systems, leagile manufacturing system is given the highest priority followed by lean manufacturing system, agile manufacturing system, flexible manufacturing system and cellular manufacturing system.

The selection of an appropriate manufacturing system plays a vital role for sustainable growth of the manufacturing company. In the present work, barriers which influence the effectiveness of manufacturing system have been identified. On the basis of degree of influence of barriers on the effectiveness of the manufacturing system, five alternative manufacturing systems are prioritized. The framework will help the management of the case company to take reasonable decision for the adoption of the appropriate manufacturing system.

The results of the research work are very useful for the manufacturing companies interested in analyzing the alternative manufacturing systems on the basis of their effectiveness and their sensitivity toward various barriers. The management of Indian manufacturing company will take decision to adopt a manufacturing system whose effectiveness is least sensitive toward barriers. Effectiveness of such manufacturing system will improve with time without having retardation due to barriers. With improved effectiveness of the manufacturing system, the manufacturing company would be able to survive with global competition. The result of the present work is based on the inputs from the case company and may vary for the other manufacturing company. In the present work, only five alternative manufacturing systems and five barriers have been considered. To obtain the better result, MCDM approach with more number of alternative manufacturing systems and barriers might be considered.

The research work is based on the fuzzy analytic hierarchy process framework and on the case study conducted by the authors. The work carried out is original in nature and based on the real-life case study.

The purpose of this paper is (1) to review, analyze and assess the existing literature on lean tools selection studies published from 2005 to 2021; (2) to identify the limitations faced by previous studies; and (3) to suggest future works that are necessary to facilitate the selection of lean tools.

A systematic approach was used in order to identify, collect and select the articles. Several keywords related to the selection of lean tools were used to collect articles from different Scopus indexed journals. Next, the study systematically reviewed and analyzed the selected papers to identify the lean tools' selection method and discussed its features and limitations.

An analysis of the results showed that previous studies have adopted two types of methods for selecting lean tools. First, there are various traditional methods being used. Second, multi-criteria decision-making (MCDM) methods were commonly used in previous studies, such as the multi-objective decision-making method (MODM), single multi-attribute decision-making (MADM) methods and hybrid (MCDM). Moreover, the study revealed that the lean tools' selection methods in previous studies were based on evaluating the relationship between either lean tools and performance metrics or lean tools and waste, or both.

In terms of its theoretical value, the study is considered as an extension of the previous researches performed on this topic by determining and analyzing the features of the most selection methods of lean tools. Unlike previous review papers, this review had considered discussing and analyzing the characteristics and limitations of these methods. Section 2.2 of this paper reviewed some of the categories of MCDM methods as well as some of the traditional methods used in the selected previous studies. Section 2.1 of this paper explained the concept of lean management and its application benefits. Further, only three sectors were covered by the previous studies in this review paper. This study also provided recommendations for future research. Therefore, it provided researchers with a good conception of how to conduct the studies on lean tools selection. Besides, knowing the methods used in previous studies can help researchers develop new methods to select the best set of lean tools. That is, this study provided and advanced the existing knowledge base for researchers concerning lean tools selection, especially there is limited availability of review papers on this topic. Moreover, the study showed researchers the importance of the relationship between lean tools and indicators or/and performance indicators to determine the appropriate set of lean tools so that the results of future studies will be more realistic and acceptable.

Practically, manufacturers face a significant challenge when selecting proper lean tools. This study may enhance managers, manufacturers and company's knowledge to identify most of the methods used to choose the best set of lean tools and what are the advantages, disadvantages and limitations of these methods as well as the latest studies that have been adopted in this topic. That means this study can direct companies to prioritize the application of lean tools depending on either the manufacturing performance metrics or/and manufacturing wastes so that they avoid incorrect application of lean tools, which will add more non-value added activities to operations. Therefore companies can decrease the time and cost losses and enhancing the quality and efficiency of the performance. Correctly implementing the best set of lean tools in companies will lead in general to correctly applying lean management in corporations. Therefore, these lean tools can boost the economic aspect of companies and society through reducing waste, improving performance indicators, preserving time and cost, achieving quality, efficiency, competitiveness, boosting employee income and improving the gross domestic product. The correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. Besides, the correct lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right selection of lean tools will have positive effects on both the environment and society. The study may also encourage manufacturers and researchers to adopt studies on lean tools selection in small- and medium-sized companies because the study referred to the importance and participation of these kinds of companies in a large proportion of the economy of developing countries. Further, the study may encourage some countries that have not previously adopted this type of study, academically and industrially to conduct lean tools selection studies.

As mentioned previously, the correct lean tool selection reduces customer complaints and employee stress and improves work conditions, health, safety and labor wellbeing. The proper lean tools selection improves materials usage, energy usage, water usage and decreases liquid wastes, solid wastes and air emissions. As a result, the right choice of lean tools will positively affect both the environment and society.

The study expanded the efforts of previous studies concerning lean management features. It provided an accurate review of most lean tools selection studies published from 2005 to 2021 and was not limited to the manufacturing sector. It further identified and briefly described the selection methods concerning lean tools adopted in each paper.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

Robot selection is one of the critical decision-making tasks frequently performed by various industries in order to choose the best suited robot for specific industrial purposes. In recent marketplace, the number of robot manufacturers has increased remarkably offering a wide range of models and specifications; thus, robot selection has become indeed confusing as well as complicated task. Selection of an appropriate robot is a sensitive process; it may result massive letdown, if not chosen properly. Therefore, for unravel the selection problem; the purpose of this paper is to explore the preference ranking organization method for enrichment evaluation (PROMETHEE) II method.

Apart from a large variety of robotic systems, existence of various multi-criteria decision making (MCDM) tools and techniques may create confusion to the decision makers’ in regards of application feasibility as well as superiority in performance to work under different decision-making situations. In this context, the PROMETHEE II method has been found as an efficient decision-making tool which provides complete ranking order of all available alternatives prudently, thus avoiding errors in decision making.

In this context, the present paper highlights application potential of aforesaid PROMETHEE II method in relation to robot selection problem subjected to a set of quantitative (objective) evaluation data collected from the available literature resources. Advantages and disadvantages of PROMETHEE II method have also been reported in comparison to other existing MCDM approaches.

The study bears significant managerial implications. Proper evaluation and selection of appropriate candidate robot would be helpful for the industries in order to improve product quality as well as to increase productivity. Proper utilization of resources could be ensured. Functioning would be accurate with reduced timespan. As a consequence, company can increase its profit margin in long run.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

The purpose of this paper is to present the adapted model per phases of the creative problem solving (CPS) process, where multi‐criteria decision making (MCDM) methods are used in the decision‐making phase. Also, to adapt and complete the steps of the six‐question technique, in order to establish the criteria's importance.

The framework procedure of MCDM, together with the Dialectical Systems Theory's guidelines when solving complex problems has already been introduced. The procedure was well‐verified in practice, but lacked the support of creative qualitative techniques in defining problems, and in generating and choosing alternatives. To eliminate this deficiency, in terms of prescriptive approach, the authors adapted the phases of the CPS process, where MCDM methods are used when choosing alternatives, and completed the steps of the six‐question technique to establish the criteria weights. The discrete Choquet integral was used to consider interactions among criteria.

The article shows that creative approaches are not limited to merely problem definitions and problem structuring. They can also be used in typically analytical steps in the framework procedure.

The completed and adapted phases of the CPS process can allow the mutual assistance of creative and decision‐making methods when solving problems – a step forward to holism.

This article develops and introduces the use of the six‐question technique, in the establishment of criteria weights.

The innovative aspect of this article is that it adapts and completes the CPS process so that MCDM methods can be used when choosing alternatives. It extends the use of creative approaches to typically analytical steps of MCDM, where synergies and redundancies among criteria are considered.

The purpose of this article is to address the measure selection problem and to propose the use of a multi‐criteria approach to address the problem more effectively. The main objective of this research is to propose a methodology (not a new performance measurement framework) that will support existing measurement framework(s) during the process of performance measurement systems' design, implementation and use, and to advance the decision‐making process.

Conforming to the most favoured approach, the balanced scorecard is adopted to illustrate the proposed methodology. This paper briefly illustrates the application of the proposed methodology. This illustration is based on a real case study from a Greek financial institution, which has considered the proposed methodology in order to select appropriate measures. The paper begins with a brief literature review on the balanced scorecard, the theory of MCDM and smart technique. In section three the proposed methodology is presented and each of the stages involved. The paper then illustrates the proposed methodology.

The greatest significance of the methodology suggested here is that it provides a structure to guide decision makers through the process of measure selection. Criteria must be identified and considered systematically, as must alternatives (i.e. measures).

While the smart was chosen to select appropriate measures for the balanced scorecard, the basic approach used in formulating the problem serves also as a framework for the application of other multi‐criteria approaches to this problem as well as to other performance measurement frameworks. Ultimately, better quality decisions will result; both as a consequence of the support provided by the multi‐criteria tools and as a result of a structure that will help the decision makers to better understand the issues associated with the problem involved.