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In the last few years, environmental issues have become a matter of survival. In this sense, e-waste management is among the major problems since it may be a way of mitigating mineral depletion. In this context, the literature lacks e-waste supply chain studies that systematically map supply chain challenges and risks concerning material recovery.

Given this context, the authors' paper conducted a systematic literature review (SLR) to build a framework to identify the constructs of e-waste supply chain risk management.

The paper revealed the theoretical relationship between important variables to achieve e-waste supply chain risk management via a circular economy (CE) framework. These variables include reverse logistics (RL), closed-loop supply chains (CLSC), supply chain risk management, supply chain resilience and smart cities.

The literature contributions of this paper are as follows: (1) a complete list of the risks of the e-waste supply chains, (2) the techniques being used to identify, assess and mitigate e-waste supply chain risks and (3) the constructs that form the theoretical framework of e-waste supply chain risk management. In addition, the authors' results address important literature gaps identified by researchers and serve as a guide to implementation.

This study aims to address the inherent uncertainties within closed-loop supply chain (CLSC) networks through the application of a multi-objective approach, specifically focusing on the optimization of integrated production and transportation processes. The primary purpose is to enhance decision-making in supply chain management by formulating a robust multi-objective model.

In dealing with uncertainty, this study uses Pythagorean fuzzy numbers (PFNs) to effectively represent and quantify uncertainties associated with various parameters within the CLSC network. The proposed model is solved using Pythagorean hesitant fuzzy programming, presenting a comprehensive and innovative methodology designed explicitly for handling uncertainties inherent in CLSC contexts.

The research findings highlight the effectiveness and reliability of the proposed framework for addressing uncertainties within CLSC networks. Through a comparative analysis with other established approaches, the model demonstrates its robustness, showcasing its potential to make informed and resilient decisions in supply chain management.

This study successfully addressed uncertainty in CLSC networks, providing logistics managers with a robust decision-making framework. Emphasizing the importance of PFNs and Pythagorean hesitant fuzzy programming, the research offered practical insights for optimizing transportation routes and resource allocation. Future research could explore dynamic factors in CLSCs, integrate real-time data and leverage emerging technologies for more agile and sustainable supply chain management.

This research contributes significantly to the field by introducing a novel and comprehensive methodology for managing uncertainty in CLSC networks. The adoption of PFNs and Pythagorean hesitant fuzzy programming offers an original and valuable approach to addressing uncertainties, providing practitioners and decision-makers with insights to make informed and resilient decisions in supply chain management.

This paper aims to propose a model for democratization of Islamic home financing to tackle the issue of sustainability of homeownership affordability.

A conceptual framework and fractional equity model (FEM) are developed to incorporate big data analytics, artificial intelligence and blockchain technology in an ecosystem for affordability and sustainability of homeownership via the proposed financing model. In addition, the FEM adopts the simulation approach to show its validity in terms of liquidity when compared with traditional home financing. In this regard, this paper is focused on developing and demonstrating the feasibility of a new financing model, rather than testing specific hypotheses or relationships. This is to propose the democratization model for Islamic Home Financing that will not benefit the prospective home buyers without compromising the profitability of the financial institutions.

The findings indicate that the proposed end-to-end solution within the financing ecosystem can lead to more efficient matching market between the buyers and sellers of houses, reduced transaction costs, greater transparency and enhanced efficiency which in the end could lead to lower costs of owning homes and sustained financial resilience among house owners. The findings indicate that the FEM model is able to increase homeownership with more elements of liquidity, marketability and sustainability for homebuyers.

This research highlights the potential of big data and blockchain technology in democratizing Islamic home financing and evidence that the transfer of ownership is possible through tokenization. However, this will require a mature financing environment to adapt the technology for practical application.

The model proposes a solution to propagate shared prosperity among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies. The proposed FEM model provides alternative home financing that is more marketable, flexible and sustainable for households/buyers and financiers.

It is hoped that with the proposed financing ecosystem to promote affordability and sustainability of homeownership via big data analytics, artificial intelligence and blockchain technology can lead to greater financial resilience for homeowners which can then be translated to enhanced well-being, increased productivity and can further promote economic growth.

This research is a concept paper based on academic research and industry collaboration with a technology provider.

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 is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

Efforts to implement supplier selection and order allocation (SSOA) approaches in small and medium-sized enterprises (SMEs) are quite restricted due to the lack of affordable and simple-to-use strategies. Although there is a huge amount of literature on SSOA techniques, very few studies have attempted to address the issues faced by SMEs and develop strategies from their point of view. The purpose of this study is to provide an effective, practical, and time-tested integrated SSOA framework for evaluating the performance of suppliers and allocating orders to them that can improve the efficiency and competitiveness of SMEs.

This study was conducted in two stages. First, an integrated supplier selection approach was designed, which consists of the analytic hierarchy process and newly developed measurement alternatives and ranking using compromise solution to evaluate supplier performance and rank them. Second, the Wagner-Whitin algorithm is used to determine optimal order quantities and optimize inventory carrying and ordering costs. The joint impact of quantity discounts is also evaluated at the end.

Insights derived from the case study proved that the proposed approach is capable of assisting purchase managers in the SSOA decision-making process. In addition, this case study resulted in 10.89% total cost savings and fewer stock-out situations.

Criteria selected in this study are based on the advice of the managers in the selected manufacturing organizations. So the methods applied are limited to manufacturing SMEs. There were some aspects of the supplier selection process that this study could not explore. The development of an effective, reliable supplier selection procedure is a continuous process and it is indeed certainly possible that there are other aspects of supplier selection that are more crucial but are not considered in the proposed approach.

Purchase managers working in SMEs will be the primary beneficiaries of the developed approach. The suggested integrated approach can make a strategic difference in the working of SMEs.

A practical SSOA framework is developed for professionals working in SMEs. This approach will help SMEs to manage their operations effectively.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

Growing concern over sustainability adoption has presented an array of challenges to businesses. While vital to an economy's success, banking is not immune to societal, environmental and economic consequences of business practices. The study has examined the sustainable performance of banking institutions on the suggested multidimensional framework comprising economic, environmental, social, governance and financial dimensions and 52 sustainability indicators. The study benchmarks the significant performance indicators of leading banks indispensable to sustainable banking performance. The findings attempt to address research questions concerning the extent of sustainable banking performance, ranking the sustainability dimensions and indicators and standardizing sustainability adoption metrics.

To determine the responsiveness of the banking industry to sustainability dimensions, content analysis was conducted using NVivo software for the year 2021–2022. Furthermore, a hybrid multicriteria decision-making (MCDM) approach is used by integrating entropy, the technique for order preference by similarity to ideal solution (TOPSIS) and VlseKriterijumska Optimizacija KOmpromisno Resenje (VIKOR) to provide relative weights to performance indicators and prioritize banks based on their sustainable performance. Sensitivity analysis is used to ensure the robustness of results.

In the context of the Indian banking industry, the pattern of sustainability reporting is inconsistent and concentrated on addressing environmental and social concerns. The results of the entropy methodology prioritized “Environmental” sustainability over other selected dimensions while “Financial” dimension has been assigned the least priority in the ranking order. The significant sustainable performance indicators delineated in this study should be used as standards to ensure the accountability and credibility of the sustainable banking industry. Additionally, the research findings will provide valuable inputs to policymakers and regulators to assure better contribution of the banking sector in meeting sustainability goals.

Considering the paucity of studies on sustainable banking performance, this study makes two significant contributions to the literature. First, the suggested multidimensional disclosure model integrating financial and nonfinancial indicators would facilitate banking institutions in addressing the five aspects of sustainability. As one of the first studies in the context of the Indian banking industry, the findings would pave the way for better diffusion of sustainability practices. Second, the inclusion of MCDM techniques prioritizes the significance of sustainability indicators and benchmarks the performance of leading banks to achieve better profits and more substantial growth.

The relationship between the various existing smell taxonomies and the smell impacting factors has been established. The ideology is to identify the most critical smell influencing factors in the vicinity of various software development environments.

To fulfill the said task, the utilization of the amalgamation of two multicriteria decision-making techniques, namely, Entropy method and CODAS method, is presented.

Through this article, the most critical smell impacting criteria with respect to the smell taxonomies is identified. Furthermore, the behaviour of 4 software development principles was then analysed, and their working state has been successfully assessed.

The ideology to study design-related smells in the software system has been studied by a lot of researchers. Some of them have worked upon their detection and the corresponding refactoration process with the help of several algorithms like machine learning and artificial intelligence. But how and to what extent these design-related smells impact the software development environment has remained out of the limelight till now. Through this article, this research gap has been identified, and an attempt to fill it has been made.

The study aims to propose a decision-support system to determine the location of a regional disaster logistics warehouse. Emphasizing the importance of disaster logistics, it considers the criteria to be evaluated for warehouse location selection. It is aimed to determine a warehouse location that will serve the disaster victims most efficiently in case of a disaster by making an application for the province of Izmir, where a massive earthquake hit in 2020.

The paper proposes a fuzzy best–worst method to evaluate the alternative locations for the warehouse. The method considers the linguistic evaluations of the decision-makers and provides an advantage in terms of comparison consistency. The alternatives were identified through interviews and discussions with a group of experts in the fields of humanitarian aid and disaster relief operations. The group consists of academics and a vice-governor, who had worked in Izmir. The results of a previously conducted questionnaire were also used in determining these locations.

It is shown how the method will be applied to this problem, and the most effective location for the disaster logistics warehouse in Izmir has been determined.

This study contributes to disaster preparedness and brings a solution to the organization of the logistics services in Izmir.