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In this study, an integrated decision-making model consisting of decision-making trial and evaluation laboratory (DEMATEL), best worst method (BWM) and a modified version of evaluation based on distance from average solution (EDAS) methods is proposed for supplier selection problem in a public procurement system considering sustainable development goals.

DEMATEL and BWM methods are used to determine weights of the criteria that are defined for the supplier selection problem. Weight aggregation method is applied to combine the weights obtained from these two methods. A modified version of EDAS method is then used in order to rank the alternative suppliers.

The proposed decision-making model is investigated for a supplier selection problem for a hospital in Spain. The validity of the results is checked using comparison with other decision-making methods and several performance analysis tests.

The proposed multi-criteria decision-making (MCDM) model contributes to the healthcare supply chain management (SCM) and aims to lead the policy makers in selecting the best supplier.

There is no such study that combines DEMATEL and BWM together for weight generation. The application of the modified EDAS method is also new. In real time situations, the decision experts may confront to the difficulty of using BWM while identifying the best and the worst criteria choices. The idea of using DEMATEL is to aid the experts to make them enable in distinguishing between the best/worst criteria and handle BWM easily.

Three-parameter interval grey numbers (TPIGNs) have been extensively studied as an extended form of interval numbers. However, most existing TPIGN multi-attribute decision-making methods only consider the similarity of positions, ignore the similarity of developmental directions and focus primarily on static evaluation. To address these limitations, in this study, the authors propose a dynamic technique for order preference by similarity to an ideal solution (TOPSIS) based on modified Jaccard similarity and angle similarity for TPIGNs.

First, the authors extend Jaccard similarity to a TPIGN environment to represent positional similarity. A simple example is provided to illustrate the limitations of the traditional Jaccard similarity. Then, the authors introduce an angle similarity measure to represent developmental directional similarity. Finally, a dynamic TOPSIS model is constructed by incorporating time-series data into conventional two-dimensional static data. Stage weights are obtained by an objective function designed to maximize the amount and minimize the fluctuation of decision information. A quadratic weighting method is adopted to derive the overall evaluation value of alternatives.

To evaluate the effectiveness of the proposed method, this study takes the pre-assessment of ice disaster and the selection of cooperative enterprises as examples. The authors then provide the results of comparative and sensitivity analyses, which demonstrate the effectiveness and flexibility of the proposed method.

The proposed TOPSIS method in a TPIGN environment can take a more holistic and dynamic perspective for decision-making, which helps mitigate the limitations of single-perspective or static evaluations.

China is a critical factor for constructing an all-round well-off society. Infrastructure construction, especially high-grade highways, in the western area is an essential component of the strategy for large-scale development of west China. It is crucial to evaluate investment projects for high-grade highways and select the best one. Testing investment projects and selecting the best one can be recognized as a multicriteria decision-making (MCDM) problem. In this process, decision-makers (DMs) usually face with uncertain information because of complicated decision environment or their limited knowledge.

A new Evaluation based on the Distance from Average Solution (EDAS) for PFS based on the DEMATEL is proposed: The authors offer a new score function and prove some properties for the score function. They put forward a novel Decision-making Trial and Evaluation Laboratory (DEMATEL) method for PFS to analyze the relations of criteria and get criteria weights. Considering the bounded rationality of DM, the authors propose a new EDAS method for PFS based on prospect theory. They apply their proposed approach to a western city's actual case in selecting a suitable project for building a high-grade highway.

By comparison, the authors can observe that our method has some traits: (1) considering bounded rationality of DM; (2) fewer computation; (3) having the ability to obtain the relation of criteria and finding the critical factor in the decision system.

In this paper, the authors propose a new EDAS method for PFS based on the DEMATEL technique. They transform PFS into crisp numbers by their proposed new score function for PFN to make the decision process more convenient. Then, the authors use the DEMATEL method to obtain the relationship between criteria and criteria weights. Furthermore, they propose a new EDAS method for PFS based on DEMATEL to reduce the computational complexity. Finally, they apply our method to a real case and compare our method with two traditional methods.

The purpose of this paper is to propose a new framework for assessing the risks of turnaround projects in upstream oil process plants.

This study represents a new hybrid framework for turnaround project risk assessment. First, according to experts’ opinions, the project risks were identified using interviews and brainstorming. The most important risks selected by experts and a hybrid multiple-attribute decision-making (MADM) method used to assess and prioritize them. The proposed MADM method uses fuzzy step-wise weight assessment ratio analysis (SWARA) and fuzzy evaluation based on distance from average solution (EDAS) methods based on trapezoidal fuzzy numbers.

In this research, 28 usual risks of turnaround projects are identified and 10 risks are then selected as the most important ones. The findings show, that among the risks of upstream oil industry turnaround projects from the perspective of experts, the risk of timely financing by the employer, with an appraisal score of 0.83, has the highest rank among the risks and the risk of machine and equipment failure during operation, with an appraisal score of 0.04, has the lowest rank.

The risk analysis based on inputs collected from the experts in the Iranian upstream oil industry, and so the generalization of the results is limited to the context of developing countries, especially oil producer ones. However, the proposed risk analysis methodology and key insights developed can be useful for researchers and practitioners in any other process industry everywhere.

A novel framework for risk assessment is introduced for turnaround projects in the oil industry using MADM methods. There is no literature on using MADM methods for turnaround project risk analysis in the oil and gas industries. Furthermore, this paper presents a hybrid fuzzy method based on SWARA and EDAS.

Due to the high rate of failure of most crowdfunding projects, knowing the most essential factor to obtain funding success on the crowdfunding platform is of great importance for fund seekers on the crowdfunding platform. The purpose of this study is to explore crowdfunding success factors to know the most essential success factor for stakeholders of the crowdfunding platform to make the best decision when seeking funds on the crowdfunding platform. This study identified and ranked crowdfunding success factors for stakeholders of crowdfunding platforms. Sixteen factors were identified and categorized under five broad headings. These were; project ideas, target capital, track records, geographical proximity and equity.

To rank the identified crowdfunding success factors and subfactors, this study used the Multi-Objective Optimization Based on Ratio Analysis (MULTIMOORA) integrated with the Evaluation based on Distance from Average Solutions (EDAS).

Target capital ranked first among the five categories—while duration involved in raising funds ranked first among the sixteen subfactors. An approach for analyzing how each success factor enhances a crowdfunding campaign was developed in this study. This study provides valuable insight to fund seekers on the crowdfunding platform on how funding success can be achieved by knowing which factor to consider essential when seeking funds on the crowdfunding platform.

This is the first study to explore crowdfunding success factors using the MULTIMOORA-EDAS method. The use of this method will help fund seekers on the crowdfunding platform to know which crowdfunding success factor is essential, thereby aiding fund seekers to make the best decision when seeking funds on the crowdfunding platform. Also, this study is particularly helpful for business owners, platform operators and policymakers when deciding how to allocate resources, plan campaigns and implement regulations.

The poor leadership style is a key obstacle to the effective implementation of Industry 4.0 technologies. To successfully apply the Industry 4.0 technologies, which can enhance the sustainability of firms, senior management needs to be inspiring and transformational. On the other hand, numerous factors can hinder the Industry 4.0 transition and “Circular Supply Chain (CSC)” transformation. Therefore, the main purpose of this study is to evaluate the related barriers of CSCs in the era of Industry 4.0 transition.

The current study developed an innovative decision-making approach with the help of the “Combined Compromise Solution (CoCoSo)” method and “Criteria Importance Through Intercriteria Correlation (CRITIC)” method on the “q-Rung Orthopair Fuzzy Sets (q-ROFSs).” CRITIC in this combined method was used to predict the importance or weighting degrees of the CSCs barriers in the age of Industry 4.0 transition.

The results of this study found that the absence of knowledge about the Industry 4.0 technologies and circular approaches was the first barrier followed by the problems associated with data security in relationship management in circular flows, the deficiency of knowledge regarding the data management among stakeholders and the lack of awareness about the potential benefits of autonomous systems in labor-oriented “End-of-Life (EOL)” activities for CSCs in the era of Industry 4.0 transition.

A limitation may be that despite the generalizability of the proposed framework, the results may differ when it is implemented in different sectors. By emphasizing the obstacles to sustainable operations of supply chains (SCs) in the context of circular economy (CE) and Industry 4.0, researchers working in the same domain may be encouraged to find ways to remove such obstacles in different settings. As suggested in this study, the priority of various barriers helps researchers suggest effective strategies for the sustainable development of companies within the current dynamic business atmosphere.

The findings of this paper can aid industry practitioners in fixing their attention on the digitization or automation of their systems in the context of sustainability or resource circularity. Note that within the current context of CE, one of the crucial issues is how to conserve the existing resources; the answer to this question can save the environment.

The current paper proposed a new multi-criteria decision-making method using q-ROFSs to analyze, rank and evaluate the CSC barriers in the age of Industry 4.0 transition. To this end, a new decision-making approach with the help of CRITIC and CoCoSo methods on q-ROFSs called q-ROF-CRITIC-CoCoSo was introduced to evaluate the CSCs barriers in the era of Industry 4.0 transition.

This paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).

First, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.

Comparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.

The problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.

The “Internet of Things (IoT)” is a platform for involving smart devices via the Internet at a worldwide scale. It supports the “supply chain (SC)” and “information and communication technology (ICT)” infrastructure to be well integrated into an organization and externally with customers and suppliers. The “sustainable supply chain (SSC)” is currently unavoidable if a company seeks to satisfy the aggressive change in its customers' requirements. Numerous studies have confirmed that manufacturing firms have to accelerate the shift of their focus toward sustainability and the implementation of novel technologies, such as IoT, to accomplish their organizational goals most effectively. Although the literature consists of many theoretical approaches to IoT and numerous studies that have extremely concentrated upon the IoT technology and its potential applications, it lacks research with a focus on the challenges that arise when applying IoT to the “sustainable supply chain management (SSCM)”.

The present study proposes an integrated framework using the “Criteria Importance Through Intercriteria Correlation (CRITIC)” and “VlseKriterijumska optimizcija I kaompromisno resenje in Serbian (VIKOR)” models and employs to evaluate the IoT challenges to implement the SSCM. For estimating the criteria weights, the CRITIC tool is utilized. The organization's prioritization is obtained by the VIKOR procedure, which delivers simple mathematical procedures with precise and consistent outcomes.

To exhibit the practicality of the introduced model, a case study is taken to evaluate the IoT challenges to implement the SSCM within the “q-Rung Orthopair Fuzzy Sets (q-ROFSs)” environment. Moreover, the authors exhibit a sensitivity investigation over given parameter values, examining the stability of developed approach. Finally, the authors draw attention to a comparison between developed q-ROF-CRITIC-VIKOR decision-making approach with an existing q-ROF-TOPSIS method to show its superiority and potency.

The outcome of the study lies in observing the top benefits of individual businesses, and their entire SSCs can be found by implementing IoT. This paper investigates the most important challenges that individual firms and entire SSCs might while applying IoT. It provides a deep insight regarding the effects of IoT upon SSCM and the issues every firm need to contemplate when it is to apply IoT solutions.

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

Despite attempts to close the expectation gap, researchers in the USA and other countries continue to demonstrate the existence of differences between auditors’ beliefs about their responsibilities and the public’s understanding of the audit function. This study considers how ambiguous language in auditor communications, and the context in which it occurs, contribute to the expectation gap. An experiment examined auditors’ and bankers’ understandings of the phrase “substantial doubt” used in a going concern audit report. It was predicted and found that auditors and bankers had different general understandings of the meaning of “substantial doubt” and their understandings were conditioned on ownership structure, a contextual variable. The results demonstrate the potentially crucial role of context in ambiguous communications with users. Implications of these results are discussed, as are suggested areas for future research.