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The construction industry has a significant function in improving the quality of life in the urban environment; meanwhile, greening the supply chain is becoming a seriously pressing issue in the construction industry. This paper seeks to select green suppliers in construction projects implemented at Iranian state universities via multicriteria decision-making (MCDM) models through rough set theory.

A mixed methodology design was conducted through a literature review of studies concerned with green suppliers to identify the related criteria and the rough MCDM techniques. As such, 15 criteria were finalized through content validity ratio (introduced by Lawshe, 1975). The weights of the criteria were calculated through the rough AHP and the suppliers were prioritized using the rough TOPSIS to contribute to the Construction Department.

The study proposed a hierarchical structure of the decision process for green supplier selection in construction projects. According to the weighting results, environmental awareness, green social responsibility and the environmental management system were the most important criteria.

Because the scope of the study was limited to state universities and the methods worked according to the experts' views, the results should be generalized with more caution. The validity of the results should be examined by applying the model to similar contexts.

The study conceptualizes green supplier selection in construction projects at state universities. Furthermore, the method used makes it possible to deal with the uncertainty arising from experts' limited awareness of only part of the problem rather than the whole system under investigation.

The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.

The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.

The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.

This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.

The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

Traditional risk assessment (RA) methodologies cannot model vagueness in risk and cannot prioritize corrective-preventive measures (CPMs) by considering effectiveness of those on risk types (RTs). These cannot combine and reflect accurately different subjective opinions and cannot be used in a linguistic manner. Risk factors (RFs) are assumed to have the same importance and interrelations between RFs are not considered. This study aims to overcome these disadvantages by combining fuzzy logic with multi-criteria decision-making in a dynamic manner.

This study proposes a novel three-stage fuzzy risk matrix-based RA integrating fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) and fuzzy multi-attributive border approximation area comparison (F-MABAC). At the first stage, importance weights of RFs are computed by F-DEMATEL. At the second stage, risk degrees of RTs are computed via using fuzzy risk matrix. At the third stage, CPMs are ranked by F-MABAC. Finally, a numerical example for RA in a warehouse is given.

Results show that developing instructions for material loading or unloading is the most important CPM and severity is the most important RF for the warehouse.

This study has originality in terms of having fuzzy dynamic structure. At first, RFs are assumed to be criteria sets then, RTs are assumed to be criteria set considering their risk degrees to rank CPMs in a fuzzy manner. Risk degrees of RTs are used for weights of RTs and effectiveness of CPMs are used for performance values of CPMs.

Social media platform, like Twitter, has increasingly become the mode of reporting civic issues owing to their vast and fast reachability. A tremendous amount of information on urban issues is shared every moment out of which some tweets may need immediate attention to save lives or avoid future disasters. Existing approaches are only limited to the identification of complaint tweets; however, its prioritization based on urgency is still unexplored. This study aims to decide the ranking of complaints based on its criticality derived using multiple parameters, like type of complaint, season, day or night, gender, holiday or working day, etc.

The approach proposes an ensemble of multi-class classification (MCC) and “two-level” multi-criteria decision-making (MCDM) algorithms, like AHP and TOPSIS, to evaluate the accurate ranking score of the tweet based on the severity of the issue. Initially, the MCC is applied to tweets to categorize the tweets into three categories, i.e. moderate, urgent and immediate. Further, the first level of MCDM algorithm decides the ranking within each complaint type, and the second level evaluates the ranking across all types. Integration of MCC and MCDM methods further helps to increase the accuracy of the result.

The paper discusses various parameters and investigates how their combination plays a significant role in deciding the priority of complaints. It successfully demonstrates that MCDM techniques are helpful in generating the ranking score of tweets based on various criteria.

This paper fulfills an identified need to prioritize the complaint tweet which helps the local government to take time-bound actions and save a life.

Selecting the most appropriate healthcare waste treatment technology (HCWTT) is an uncertain and complex decision-making problem because there exist more than one alternative and many conflicting qualitative and quantitative criteria. However, the use of fuzzy and comparative values, instead of specific crisp values, provides more accurate results, so that the alternatives may be evaluated in accordance with hesitant human nature. The purpose of this paper is to select the best HCWTT using a hesitant fuzzy linguistic term set (HFLTS).

Five main criteria were identified for HCWTT selection, such as economic, social, environmental, technical and ergonomic criteria. In total, 19 sub-criteria were examined, and the hierarchy of the criteria was formed. The criteria weights were determined using the multi-criteria hesitant fuzzy linguistic term set (MC-HFLTS). The selection processes of incineration (A₁), steam sterilization (A₂), microwave (A₃) and landfill (A₄) alternatives were carried out using the multi-attributive ideal-real comparative analysis (MAIRCA) and multi-attributive border approximation area comparison (MABAC) methods. In the comparative analyses, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to an ideal solution (TOPSIS) methods were used.

The comparison of the results of the MABAC and MAIRCA methods with the results of VIKOR and TOPSIS methods indicated that A₂ (steam sterilization) alternative was the best one and produced the same ranking of the technology alternatives (A₂ > A₃ > A₁ > A₄). As a result, the study concluded that these methods can be successfully used for HCWTT selection problems.

To the best of the authors’ knowledge, MC-HFLTS has not been used to select HCWTT in the existing literature. For the first time, MC-HFLTS&MAIRCA and MC-HFLTS&MABAC approaches were used in order to choose the best treatment method for healthcare waste under the effect of multiple conflicting hierarchical criteria. It has been provided that MABAC and MAIRCA select alternative choices by taking into consideration the hierarchical criteria. Unlike other studies, this study also considered ergonomic criteria that are important for people working during the process of using the treatment technology.

Innovative design and execution approaches are employed in infrastructure sectors and planning to enhance the integrated project delivery system, assure the sustainability of infrastructure projects, and meet the demands of the dynamic, changing environment. Delivery methods must incorporate new technologies. By combining digital technology, teamwork, and mass manufacturing, a greater degree of exceptional quality, sustainability, and resilience in the environment will be generated. As a result, a new approach does not rely on the reaction policy, but instead considers alternative scenarios and employs a simulation model to determine the best course of action.

In the paper, the system dynamics approach to construction management is validated in light of pertinent research. Additionally, it describes the difficulties facing the infrastructure projects' delivery system. Additionally, the strategy for system dynamics creation is described. This strategy includes a causal loop diagram, generates a stock-flow diagram, and simulates forecasts of model behavior over time. Next, the optimization model's validation process is used to create a system dynamics model for choosing the best infrastructure project delivery system project and controlling it to maximize sustainability, mass production, digital integration, and team integration. The dynamic complexity of project management is growing.

The primary goal is to present a system dynamics (SD) simulation to look at how well infrastructure projects perform in terms of choosing the best method for delivering infrastructure projects. One of the most ideal methods for delivering projects is integrated project delivery. An effective methodology for making strategic decisions on the choice of the best project delivery method. In order to enhance certain infrastructure project delivery system metrics for sustainability, mass production, digital integration, and team integration, the model included building strategy and sophisticated system dynamics simulation. According to the construction strategy, the outcomes have been satisfactory.

System dynamics research has been done to replicate the idea of contemporary construction in order to determine the best approach for delivering infrastructure. The government and decision-makers would benefit from understanding this research as they decide on the best delivery method for boosting the sustainability and productivity of infrastructure projects in Egypt.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

This paper aims to identify and analyze the barriers to the implementation of the United Nations Industrial Development Organization’s programxk for export consortia in the Iranian handmade carpet industry.

To accomplish its objectives, the study relied on a mixed (qualitative and quantitative) research method. Primarily, unstructured interviews were conducted to identify the effective barriers. Then, the validity of the barriers identified was evaluated through content validity ratio analysis. Finally, the interrelationships between the barriers were determined using the rough decision-making trial and evaluation laboratory technique (DEMATEL).

Results revealed that “a lack of leadership,” “a traditional business environment” and “a lack of awareness of consortium benefits” were the most significant causal barriers. Meanwhile, the most important effect barriers were “a lack of long-term vision,” “a lack of business ethics” and “a lack of motivation.”

The findings and results could help the stakeholders of the handmade carpet industry in Iran to improve existing export consortium programs, increasing the country’s share in the global markets in this industry.

This investigation seeks to fill an existing gap in the literature on export consortium formation in the handmade carpet industry by providing a network of barriers for the first time.

Regular surveys by the Malta Hotels & Restaurants Association indicate a substantial improvement in the financial performance of five-star hotels in Malta in recent years. Therefore, the purpose of this paper is to investigate if this positive performance is primarily due to customer centricity by management.

The assessment is based on the findings of a quantitative study that compared the results of a demand side (customer survey) with those of a supply side (management survey). In total, 24 decision-choice criteria were framed within the four perspectives (financial, customer, internal process and employee learning/growth) of the balanced scorecard. Actual and potential customers of five-star hotels and hotel managers were asked to rank the decision-choice criteria and the balanced scorecard perspectives. The multi-criteria decision analysis was carried out by means of the AHP.

The study showed that managers in the five-star hospitality sector in Malta are in harmony with customer expectations given the strong positive correlation between the results of the customer and management surveys.

This research is limited to the island of Malta but can be easily replicated for other touristic destinations.

This study has implication for hospitality customers, hotel managers and policy makers to help them to identify weak areas of hotel performance and improve them.

The paper has developed a Prioritised scorecard, a new hybrid balanced scorecard and AHP. Targets are therefore prioritised, which allow a better allocation of scarce resources.

The purpose of this paper is to research attribute reduction and decision making by gray dual‐information, taking into account the attribute reduction of attribute decision unknown for the interval gray numbers.

The authors obtain the attribute weights considering the consistency of experts’ judgment matrixes and the decision matrixes with gray information. They propose some experts’ attribute reduction ideas based on interval gray numbers of rough set. With the help of experts’ decision information, they consider attribute uncertainty ratio and attribute value ratio to reduce attribute. Finally, a numerical example shows its feasibility.

Some experts’ attribute reduction ideas are proposed based on interval gray numbers of rough set. With the help of experts’ decision information, attribute uncertainty ratio and attribute value ratio to reduce attribute can be considered.

Attribute reduction is keeping classified information systems under the same conditions and deleting redundant and irrelevant or unimportant attributes in order to solve the problem of decision making. This paper considers the attribute reduction based on gray dual‐information.