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Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

With the continuous development of the wind power industry, wind power plant (WPP) has become the focus of resource development within the industry. Site selection, as the initial stage of WPP development, is directly related to the feasibility of construction and the future revenue of WPP. Therefore, the purpose of this paper is to study the siting of WPP and establish a framework for siting decision-making.

Firstly, a site selection evaluation index system is constructed from four aspects of economy, geography, environment and society using the literature review method and the Delphi method, and the weights of each index are comprehensively determined by combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the entropy weight method (EW). Then, prospect theory and the multi-criteria compromise solution ranking method (VIKOR) are introduced to rank the potential options and determine the best site.

China is used as a case study, and the robustness and reliability of the methodology are demonstrated through sensitivity analysis, comparative analysis and ablation experiment analysis. This paper aims to provide a useful reference for WPP siting research.

In this paper, DEMATEL and EW are used to determine the weights of indicators, which overcome the disadvantage of single assignment. Prospect theory and VIKOR are combined to construct a decision model, which also considers the attitude of the decision-maker and the compromise solution of the decision result. For the first time, this framework is applied to WPP siting research.

Social media is widely used to capture citizens' opinions and topics deemed important. The importance or interest social media users attribute to a topic is traditionally measured by tweet frequency. This approach is practical but overlooks other user engagement tools such as retweets, likes, quotes, and replies. As a result, it may lead to a misinterpretation of social media signals. This paper aims to propose a method that considers all user engagement indicators and ranks the topics based on the interest attributed by social media users.

A multi-criteria decision-making framework was proposed, which calculates the relative importance of user engagement tools using objective (information entropy) and subjective (Bayesian Best-Worst Method) methods. The results of the two methods are aggregated with a combinative method. Then, topics are ranked based on their user engagement levels using Multi-Objective Optimization by Ratio Analysis.

The proposed approach was used to determine citizens' priorities in transport policy, and the findings are compared with those obtained solely based on tweet frequency. The results revealed that the proposed multi-criteria decision-making framework generated more comprehensive and robust results.

The proposed method provides a systematic way to interpret social media signals and guide institutions in making better policies, hence ensuring that the demands of users/society are properly addressed.

This study presents a systematic method to prioritize user preferences in social media. It is the first in the literature to discuss the necessity of considering all user engagement indicators and proposes a reliable method that calculates their relative importance.

The value assessment process of the built heritage is a multipart procedure that includes diverse aspects with overlapping requirements. This process requires various measures to obtain better results, making it a multi-criteria decision-making (MCDM) process. In post-disaster cases, a management system is required to promptly evaluate the degree of risk and damage and to set the preservation priorities in order to effectively supervise and protect the heritage places. MCDM is utilized for configuring and solving decision and planning problems encompassing multiple criteria, to assist decision-makers.

This paper suggests an approach as a tool to be used in the development of the Built Heritage Record for Mosul Old City. It proposes a D&C technique as an MCDM effectual tool for solving multifaceted problems of heritage significance assessment process in post-conflict cities, adopting the conservation of the traditional houses of Mosul Old City as a case study.

The research results showed the need for a method to solve the complexity of the decision-making problem regarding the process of determining the value and significance of heritage buildings and sites in the old city of Mosul, and how to develop a methodology to facilitate decision-making within databases related to such a multi-criteria issue.

For an effective judgment of the expert or participant in the evaluation process, and to contribute to the decision-making more objectively, exploiting digital management programs including an interactive user interface with the ability to share on the Internet is the focal next intention of the project.

The paper shows that by using specific software, a database for heritage places of the old city of Mosul could be generated to apply the proposed system. The expert can utilize the software to calculate and define the total value of the place automatically according to the entered data.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

The work on bridge resilience assessment includes quantitative and qualitative approaches to compare the multiple bridges based on their resilience. But still, the bridge resilience obtained by these assessment approaches is inefficient when prioritising multiple bridges to improve their resilience. Therefore, this study aims to develop a methodology for prioritising the bridges to improve their resilience.

The research methodology follows three sequential phases. In the first phase, criteria importance through intercriteria correlation (CRITIC) technique is used to compute the criteria weights. The criteria considered are age, area, design high flood level, finish road level FRL and resilience index of bridges. While 12 river-crossing bridges maintained by one bridge owner are considered as alternatives. Then, in the second phase, the prioritisation of each bridge is evaluated using five techniques, including technique for order of preference by similarity to ideal solution, VIKOR (in Serbian, Visekriterijumska Optimizacija I Kompromisno Resenje), additive ratio assessment, complex proportional assessment and multi-objective optimisation method by ratio analysis. Finally, in the third phase, the results of all five techniques are integrated using CRITIC and the weighted sum method.

The result of the study enables bridge owners to deal with the particular bridge that requires resilience improvement. The study concluded that it is not enough to consider only the bridge resilience index to improve its resilience. The prioritisation exercise should consider various other criteria that are not preferred during the bridge resilience assessment process.

The proposed methodology is a novel framework based on the existing multi-criteria decision-making (MCDM) techniques for contributing knowledge in the domain of bridge resilience management. It can efficiently overcome the pitfall of decision-making when two bridges have the same resilience index score.

The aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.

The research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.

The proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.

The research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.

The model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.

The present study aims to demonstrate the application of newly developed combinative distance-based assessment (CODAS) approach for grading and selection of Tossa jute fibres, which possesses some unique features uncommon to other variants of multi-criteria decision-making (MCDM) method.

The CODAS method was used in this study to rank/grade ten candidate lots of Tossa fibres on the basis of six apposite jute fibre properties, namely, fibre defect, root content, fineness, strength, colour and density. These six fibre properties were considered as the six decision criteria, here, and they were assigned weights as determined previously by an earlier researcher using analytic hierarchy process. The grading of jute fibres was done based on a comprehensive single index known as the assessment scores (Hi), in descending order of magnitude.

Among the 10 Tossa jute lots, T2 was ranked 1 (top grade) because of the highest assessment score of 6.887, followed by T1 with Rank 2 (assessment score 1.830). Because of the least assessment score of −2.795, the candidate lot T4 was considered as the worst, and hence ranked 10. The overall ranking pattern given by the CODAS method was similar to the TOPSIS approach done by Ghosh and Das (2013). This study was supported by various sensitivity analyses to judge the efficacy of the present approach. No occurrence of rank reversal during the sensitivity analyses obviously corroborates the robustness and stability of the CODAS method.

Jute pricing is fixed solely by the quality for which grading of fibre is prerequisite. The traditional “Hand and Eye” method or Bureau of Indian Standards (BIS) system for jute grading is basically subjective assessment and need domain expertise. MCDM is reported as the most viable solution which gives due importance to the fibre parameters while grading the fibres based on a single index. The present study demonstrates the maiden application of CODAS to address the fibre grading problems for jute industries. This approach can also be extended to solve other decision problems of textile industry, in general.

CODAS is a recently developed exponent of MCDM. Uniqueness of the present study lies in the fact that this is the first ever application of CODAS in the domain of textile industry, in general, and jute industry, in particular. CODAS approach is very simple involving a few simple mathematical equations yet a potent tool of decision-making. This method possesses some features uncommon in other variants of MCDM. Moreover, the efficacy of CODAS method is investigated through various sensitivity analyses, which has been ignored in the earlier approaches.

This research paper aims to establish a comprehensive framework for the barriers to CER in the construction industry, assesses the barriers' relative degrees of hindrance and causal mechanisms.

Firstly, 26 carbon emission reduction (CER) barriers in the construction industry were identified based on a systematic literature review (SLR) and categorized into five dimensions: policy, economy, society, technology and organization (PEST + O model). Secondly, the Best–Worst Method (BWM) was used to clarify the degrees of hindrance of the CER barriers. Then, the Grey-Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL) was used to visualize the directional cause–result relationship network among prominent barriers. Finally, the Boston matrix model was used to propose differentiated strategies to address CER barriers in the construction industry.

The calculated centrality and causality of the prominent barriers indicated that the lack of relevant legal policies and normative guidelines, the poor binding force and enforcement of existing relevant policies, the lack of effective economic subsidies and incentives and the difficulty in the operation, transformation and upgrading of existing construction CER are the key barriers that CER needs to address first in the construction industry. Considering the order of priority and the optimal path, differentiated countermeasures are proposed to address key, driving, independent and effect barriers.

This study develops a BWM–Grey-DEMATEL integrated multi-criteria decision-making model. An innovative C-shaped strategic map for addressing CER barriers in the construction industry is proposed by integrating the dual dimensions of time and space. This will guide practitioners, policymakers and decision-makers in developing CER strategies.

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.