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The purpose of this study is to present Industry 4.0 technologies for advancing the circular economy (CE) adaption in manufacturing industry’s supply chain (SC) network. To pursue the same, Industry 4.0 technological aspects were recognized as solution measures to overcome the challenges for CE implementation in SC.

A new hierarchical framework containing 13 leading CE challenges and eight promising Industry 4.0 technological aspects had been proposed, representing their mutual relationship. The proposed framework was analysed using a hybrid approach of analytic hierarchy process (AHP) and combinative distance-based assessment (CODAS) under interval-valued intuitionistic fuzzy (IVIF) environment. The IVIF-AHP was used to acquire the priority weights of the CE challenges, whereas the IVIF-CODAS was used to attain the preference order of the proposed technological aspects.

The key findings of the present work indicate that “Information disruptions among the SC members due to multiple channels” and “Manpower inability to handle the toxic materials” are the two most critical challenges hindering the adoption of CE practices in SC. Along with, the results also demonstrate that to overcome these challenges, “Smarter equipment to empower flexibility and mass customization” and “Big data driven decision-making system” are the two most significant Industry 4.0 technological solutions, adoption of which might encourage the organizations to align their operations with CE philosophies.

The sample size of the experts engaged in work was limited; however, big data studies could be conducted in future to capture more insights of the stated topic. In addition to this, to understand the implication of CE on Industry 4.0-based manufacturing, a separate study can be synthesised in future.

The proposed work facilitates a new framework consolidating various perspectives associated with CE implementation into a manufacturing industry considering the scenario of Indian rubber industry. This study enables the decision-makers to recognize the challenging factors for CE implementation into their organizations and up-taking the proposed Industry 4.0 practices as technological measures for improving the organization overall performance.

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.

Enterprise Resource Planning (ERP) software which is a knowledge-based design on the interconnective communication of business units and information share, ensures that business processes such as finance, production, purchasing, sales, logistics and human resources, are integrated and gathered under one roof. This integrated system allows the company to make fast and accurate decisions and increases its competitiveness. Therefore, for an enterprise, choosing the suitable ERP software is extremely important. The aim of this study is to present new research on the ERP software selection process by clarifying the uncertainties and find suitable software in a computational way.

ERP selection problem design includes uncertainties on the expert opinions and the criteria values using intuitionistic fuzzy set theory and interval grey-numbers to MACBETH multi criteria decision making method. In this paper, a new interval grey MACBETH method approach is proposed, and the degree of greyness approach is used for clarifying the uncertainties. Using this new approach in which grey numbers are used, it is aimed to observe the changes in the importance of the alternatives. Moreover, the intuitionistic fuzzy set method is applied by considering the importance of expert opinions separately.

The proposed method is based on quantitative decision making derived from qualitative judgments. The results given under uncertain conditions are compared with the results obtained under crisp conditions of the same methods. With the qualitative levels of experts reflected in the decision process, it is clearly seen that ERP software selection problem area has more effective alternative decision solutions to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during ERP software selection process.

This study contributes to the relevant literature by (1) utilizing the MACBETH method in the selection of the ERP software by optimization, and (2) validating the importance of expert opinions with uncertainties on a proper ERP software selection procedure. So, the findings of this study can help the decision-makers to evaluate the ERP selection in uncertain conditions.

Due to the current pandemic, the importance of logistics functions and decisions is well understood both at the level of companies and users. Logistics systems and related decisions are of vital importance in making supply chains effective, efficient and without disruption. Logistic pressure factors may emerge at different points along the logistics process, and given the role of logistics decisions as one of the important indicators of competitiveness, the determination of the logistics pressures that are likely to increase the costs of business, and their causative factors are a vital aspect of the logistics decision-making process. The study aims to provide assistance in the selection of the most ideal logistics decision by ranking the pressure factors affecting the logistics system, especially during the pandemic period for logistics enterprises operating in Ordu and Giresun provinces and which have a corporate identity.

In this study, it is aimed to make the most ideal logistics decision selection by ranking the pressure factors affecting the logistics system, especially during the pandemic period for the logistics enterprises operating in Ordu and Giresun provinces and having a corporate identity. For that purpose interval-valued Pythagorean fuzzy (IVPF)–analytic hierarchy process (AHP) based combinative distance-based assessment (CODAS) methodology was used. Additionally sensitivity and comparison analysis were discussed.

Competitive pressure was found as the most important pressure factor affecting the logistics system during the pandemic period. Change in regulatory rules was the pressure factor found to have the least effect on the logistics system. Using the weights of logistics pressure factors, “Operational Decisions” was found to be the most ideal logistics decision selection.

The findings provide support for the evaluation of logistical pressures and decision options by presenting a decision model capable of processing ambiguous information. During a pandemic or similar period, the study assists decision makers in determining a new route. The findings will also call business managers' attention to logistical pressure factors and lead them toward more realistic and feasible practices in the logistics decision-making process.

This study provided an effective and applicable solution to a decision-making problem in the logistics sector including logistics pressure factors and the selection of logistics decisions. In this context, a methodology was presented that will allow businesses to self-evaluate their own logistics pressure factors and the selection of optimal solutions.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

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.

Supplier selection along with continuous evaluation of their performance is a crucial activity in healthcare supply chain management for effective utilization of scarce resources while providing quality service at an affordable price, and minimizing chances of stock-out, avoiding serious consequences on the illness or fatality of the patients. Presence of both qualitative and quantitative evaluation criteria, set of potential suppliers and participation of different stakeholders with varying interest make healthcare supplier selection a challenging task which can be effectively solved using any of the multi-criteria decision making (MCDM) methods.

To deal with various qualitative criteria, like cost, quality, delivery performance, reliability, responsiveness and flexibility, this paper proposes integration of grey system theory with a newly developed MCDM tool, i.e. mixed aggregation by comprehensive normalization technique (MACONT) to identify the best performing supplier for pharmaceutical items in a healthcare unit from a pool of six competing alternatives based on the opinions of three healthcare professionals.

While assessing importance of the six evaluation criteria and performance of the alternative healthcare suppliers against those criteria using grey numbers, and exploring use of three normalization procedures and two aggregation operations of MACONT method, this integrated approach singles out S5 as the most compromised healthcare supplier for the considered problem. A sensitivity analysis of its ranking performance against varying values of both balance parameters and preference parameters also validates its solution accuracy and robustness.

This integrated approach can thus efficiently solve healthcare supplier selection problems based on qualitative evaluation criteria in uncertain group decision making environment. It can also be deployed to deal with other decision making problems in the healthcare sector, like supplier selection for healthcare devices, performance evaluation of healthcare units, ranking of physicians etc.

The study aims at evaluating the most appropriate transport project which is one of the critical concerns of transport infrastructure scheduling. This process will be applied considering a set of criteria and discussed alternatives with sustainable perspectives.

In this paper, a complex proportional assessment (COPRAS) framework is discussed to handle the sustainable transport investment project (STIP) assessment problem within a single-valued neutrosophic set (SVNSs). To form the procedure more useful in handling with uncertain features, a SVNS is applied as a valuable procedure to handle uncertainty. First, a new discrimination measure for SVNSs is introduced and discussed some elegant properties to determine the significance degree or weight values of criteria with the sustainabality perspectives. Second, an integrated approach is introduced based on the discrimination measure and the COPRAS method on SVNSs and named as SVN-COPRAS.

A case study of an STIP evaluation problem is used to confirm the practicality and effectiveness of the SVN-COPRAS framework. Lastly, comparative discussion and sensitivity investigation are illustrated to prove the strength and solidity of the proposed framework.

The SVNSs enrich the essence of linguistic information when a decision expert (DE) vacillates among different linguistic values (LVs) to measure a sustainable transport project alternative problem. The utilization of SVNSs provides a more stable procedure to describe DEs' evaluations. So, an elegant methodology is developed to incorporate the DEs' awareness and experience for electing the desired STIPs. The introduced methodology has higher operability than the single-valued neutrosophic set technique for order preference by similarity to an ideal solution (SVN-TOPSIS) procedure during the larger numbers of attribute(s) or option(s). For the SVN-COPRAS methodology, there is no need to estimate the single-valued neutrosophic ideal solution (SVN-IS) and single-valued neutrosophic anti-ideal solution (SVNA-IS). The outcomes are calculated with handling the realistic data, which elucidates that the introduced model can tackle more intricate and realistic multi-criteria decision-making issues.

Science and technology parks (STPs) have a limited capacity, which can create challenging conditions for applicants. This makes the location selection a multi-criteria decision-making (MCDM) problem to find and apply for the most appropriate STP with the highest accordance with the startup's requirements. This research aims to select the most appropriate STP to locate an international entrepreneurial pharmaceutical startup under uncertainty. Since drugs are generally produced domestically in developing countries such as Iran, the access of pharmaceutical startups to the resources provided by STPs can lead to overcoming competitors and improving the country's health system.

In this research, the factors or attributes effective on startup location were extracted through a two-round Delphi method, which was performed among 15 experts within three groups. Subsequently, the determining factors were used to select the location of a pharmaceutical startup among possible STPs. In this regard, decision-makers were allowed to use different types of numbers to transfer their opinion. Afterward, the heterogeneous weighted aggregated sum product assessment (HWASPAS) method was applied to calculate the score of each alternative and rank them to place the studied startup successfully.

The results indicated that Tehran STP stands in the first place; however, if the decision was made based on single criterion like cost, some other STPs could be preferable, and many managers would lose this choice. Furthermore, the results of the proposed method were close to other popular heterogeneous MCDM approaches.

A heterogeneous WASPAS is developed in this article for the first time to enable international entrepreneurs to imply their opinion with various values and linguistic variables to reduce the emphasis on accurate data in an uncertain environment.

The companies are struggling to collect invoices due to the decrease in the economic growth. This global trend does not only affect undeveloped countries, but it also has a strong impact on the developed countries. Improving the debt collection process become a significant element to maintain financial stability. The institutions that are specialized on collecting payments, debt collection agencies and their call centers, with their expertise in the field can improve the payment process. Yet, managing evaluating the performance of debt collection agencies is a very hard process that involves uncertainty and imprecision. Performance measurement (PM) is a combination of numerically expressed characteristics which give insight about the success or degree of accomplishment of an activity. PM can be handled in various levels such as individual, team, department or company. The aim of this study is to present a systematic and objective PM method for call centers.

In this study, first an exploratory approach is used to understand the call center measurement problem. Several meetings are done with the representatives of both call center firms and the parent firms that outsource debt collection process. Simultaneously, a broad literature review is conducted. An iterative approach is selected to reach deeper knowledge on the process. New meetings are planned and scope of the literature review has changed based on this iterative approach. After these steps, the problem has been considered as the multi-criteria decision-making problem since more than one criteria should be considered for evaluating the performances of call centers. The result of the literature review and the meetings with experts show that defining the weights for the criteria is very crucial for evaluating the performances accurately. Collecting human judgment for defining the weights of call center criteria necessitates dealing with vagueness and uncertainty. The intuitionistic fuzzy sets excellent tools for representing uncertainty. Interval valued intuitionistic fuzzy sets can easily represent the human judgments. Thus, in this study, an intuitionistic fuzzy multi-criteria decision making approach is used to design the proposed methodology. Incomplete interval-valued intuitionistic preference relations are used to determine the weights of the indicators aggregating linguistic evaluations of the decision makers.

The proposed approach provides an objective calculation of performance measurement. In order to provide objectivity, indicator performance functions are proposed for the first time in this study. Nine different functions and related parameters are defined to objectively measure indicator performances.

The paper proposes an objective and easy-to-modify approach for call-center PM, which can be used by call center managers. It presents a new fuzzy multi-criteria decision-making (MCDM) method for call center performance evaluation, which can consider the multi-experts' judgments under vagueness and impreciseness, which may be conflicting and incomplete interval-valued intuitionistic fuzzy preference relations. Also nine new functions are defined for indicator performance.