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Sustainable supplier selection is of vital importance in sustainability decision of supply chain under carbon neutrality. Multi-criteria decision-making approaches are widely used in sustainable supplier selection and generally classified the involved criteria into three sustainable development (SD) dimensions: Environmental, Social and Economic. During the assignment of appropriate weighted scores to the criteria, most of the methods considered mutually exclusive criteria. However, some criteria cover multidimensions since ambiguity vagueness makes them difficult to classify into one dimension exclusively. The purpose of this paper is to find proper approaches addressed to multidimensional overlapping criteria in the evaluation of suppliers’ sustainability performance.

This study proposes three approaches to resolve the multidimensional overlapping criteria issue by data envelopment analysis (DEA) methods. The first approach uses all dimensional criteria and “dimensional overlapping criteria” in a single DEA model. The second approach consists of two-stage DEA. The first stage is to find SD dimensional performances, which are used in the second stage. The third approach uses an aggregate weight-constrained DEA model with additional constraints. Such approaches are applied to an empirical case study with six dimensions.

Results indicate that the third approach is better than the first two approaches in balancing the development among all dimensions instead of focusing on the superiority dimension to obtain high performance.

Discussing overlapping criteria in the context of sustainable supplier evaluation and other multi-criteria evaluation have a noticeable impact on evaluation systems, but appropriate approaches for this issue are currently under-researched.

The existing concept in the building rating scheme especially in developing countries was more focused on the environment, economic, social and culture. The new approach of the classroom condition index (CCI) assessment scheme has its uniqueness in environmental and social aspects because of high building performance in secondary school buildings. The requirements set by the Ministry of Education Malaysia include providing a conducive learning environment, especially for students who are considered as the main users of classrooms in school buildings. Currently, the school administration needs to manually record the condition of the classroom to increase its comfort level. The lack of a structured scheme for classroom assessment makes it difficult for school administration to focus on the overall classroom condition (physical environmental aspect) in the school building. The purpose of this study is to develop a framework for classroom conditions by proposing a CCI assessment scheme for a secondary school building in Malaysia.

Mixed methods were used to carry out the study. The first stage of this study concentrates on developing a system for CCI that relates to physical elements in the classroom. This is done by reviewing the literature on the classroom physical performance, as well as a comparison between several building rating systems locally and abroad. The structure of the proposed CCI scheme is grouped into four main themes, namely, space management quality (SMQ), building condition (BC), indoor environmental quality (IEQ) and teaching and learning quality (TLQ). In addition, there are 12 categories and 23 indicators listed under this theme. The second stage focuses on formulating assessment categories with their relevant performance indicators. This phase undergoes a validation process by conducting a survey (questionnaire) toward the classroom’s main users, which are students and teachers. This is to ensure the accuracy of classroom conditions in the school building. A semi-structured interview was also conducted among building experts. They are building surveyors, building engineers, building designers and building performance experts to support the main findings in the second stage. Relative importance (RI) index approach has been applied to show the indicators weighting and ranking are used as data collections method by using Statistical Package of Social Science software to examine the RI of each category and indicator, respectively.

The findings show that prominent RI and balanced weights are formed from these four main themes. They are SMQ (19.9%), BC (26.6%), IEQ (33.2%) and TLQ (20.3%). The outcome of this study will contribute to a detailed assessment scheme for CCI at the secondary school building. The contribution of the CCI Assessment Scheme is more comprehensive and holistic than the conventional assessment process for BCs. It focuses specifically on classroom space as it is the most important area to achieve a high level of comfort comparing to other spaces in the school building. This holistic approach encompasses all types of classrooms. The concept of one tool fits all is seen as no longer a relevant adaptation in this context. This proposed tool is to be used only for the classroom (as the name CCI implies) and it cannot be used for the other types of spaces, for instance, teacher room, library, meeting room, toilet, canteen and, etc. This is because different spaces represent different physical indicators to be classified. This, in turn, contributes to a conducive learning environment for students in the school.

This paper provides the current information, knowledge and findings related to the classroom physical indicators in developing the assessment scheme for the classroom environment. It will assist both technical and non-technical experts to clarify the current condition of classroom physical performance that ideally may affect the students’ learning environment. The novelty of CCI development is not only on the adopted method but it also includes the ideas on next generation model of rating system that ideally need specific indicators and weighting to be generated into an intelligent computerized system.

The paper aims to craft a non-parametric composite value for the board quality of Indian banks where the weights can be assigned endogenously.

The study employed a non-parametric data envelopment analysis (DEA)-based novel extension known as the benefit of doubt approach. To measure the strength of the Indian bank corporate board in terms of board efficiency (BEF), the study used a mixed approach, i.e. first, the study calculates the percentile ranks of the five attributes that the study assumes are the characteristics of the strong board including board size, number of outside directors, frequency of meetings, non-duality leadership and board gender diversity. Thereafter, the study performs the benefit-to-doubt approach to finally measure the efficiency of the board.

The findings of the study establish that the methodological framework present in the study to measure the strength of the board in terms of BEF has been a much superior method over the other weighted and non-weighted linear average methods.

This methodology aids the shareholders, investors and regulatory bodies in rating the Indian banks based on their strength in terms of better monitoring boards and ensuring a smooth agent–owner relationship.

The benefit of doubt approach has been a unique and novel methodology to craft the composite value for any multidimensional phenomenon. One of the major benefits of using this approach is that it assigns the weights endogenously to each dimension and thereafter collectively determines the efficiency of such a phenomenon.

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 aim of this research was to evaluate the maturity level of strategic communication management implemented by Brazilian startups.

This study employed the analytic hierarchy process (AHP), survey and Grey Fixed Weight Clustering modeling techniques. Three experts with extensive academic and practical experience in the subject participated in the AHP process, providing their opinions on the relative importance of eight variables associated with the topic under investigation, thus enabling their prioritization. Concurrently, data were collected through a survey from 23 respondents who have extensive knowledge about the realities of Brazilian startups. The weights derived from the AHP and the survey data were utilized in the Grey Fixed Weight Clustering modeling.

Based on the opinions of the 23 respondents, the level of implementation of practices related to strategic management, brand management, external image management and internal communication management is superficial. In addition, according to the majority of experts, Brazilian startups exhibited a medium level of maturity to address the key challenges related to communication management. Furthermore, this study reveals that the variables “financial resources allocation,” “stakeholder relationship” and “brand management” were deemed the most significant for the model.

The contributions presented herein can be beneficial for both researchers and startup managers seeking to enhance communication strategies in their organizations. This research also contributes by highlighting how grey systems theory can be extremely useful for conducting decision-making analyses in the context of startups, which is characterized by uncertainty and imprecise information.

This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios.

To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method.

This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method.

This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

The goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).

Using Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.

According to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.

The findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.

The current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.

This paper focuses on multi-objective order allocation with product substitution for the vaccine supply chain under uncertainty.

The weighted-sum minimization approach is used to find a compromised solution between three objectives of minimizing inefficiently vaccinated people, postponed vaccinations, and purchasing costs. A mixed-integer formulation with substitution quantities is proposed, subject to capacity and demand constraints. The substitution ratios between vaccines are assumed to be exogenous. Besides, uncertainty in supplier reliability is formulated using optimistic, most likely, and pessimistic scenarios in the proposed optimization model.

Covid-19 vaccine supply chain process is studied for one government and three vaccine suppliers as an illustrative example. The results provide essential insights for the governments to have proper vaccine allocation and support governments to manage the Covid-19 pandemic.

This paper considers the minimization of postponement in vaccination plans and inefficient vaccination and purchasing costs for order allocation among different vaccine types. To the best of the authors’ knowledge, there is no study in the literature on order allocation of vaccine types with substitution. The analytical hierarchy process structure of the Covid-19 pandemic also contributes to the literature.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

The purpose of this paper is to create a numerical technique to tackle the challenge of selecting software reliability growth models (SRGMs).

A real-time case study with five SRGMs tested against a set of four selection indexes were utilised to show the functionality of TOPSIS approach. As a result of the current research, rating of the different SRGMs is generated based on their comparative closeness.

An innovative approach has been developed to generate the current SRGMs selection under TOPSIS environment by blending the entropy technique and the distance-based approach.

In any multi-criteria decision-making process, ambiguity is a crucial issue. To deal with the uncertain environment of decision-making, various devices and methodologies have been explained. Pythagorean fuzzy sets (PFSs) are perhaps the most contemporary device for dealing with ambiguity. This article addresses novel tangent distance-entropy measures under PFSs. Additionally, numerical illustration is utilized to ascertain the strength and authenticity of the suggested measures.