Effective management of healthcare waste (HCW) imposes a great challenge to all countries. Specially in the developing countries, it is often mixed with municipal waste, adversely affecting the health and safety of the medical personnel, general public and environment. Healthcare waste management (HCWM) basically deals with segregation, collection and storage, routing and transportation, treatment and safe disposal of HCW, while obeying some national legislation. In every stage of HCWM, there are several alternative choices/strategies to be evaluated against a set of conflicting criteria. Numerous multi-criteria decision-making (MCDM) methods have appeared to resolve the issue. This article reviews 101 articles available in Scopus and other scholarly databases on applications of MCDM techniques in solving HCWM problems. Those articles are classified into six groups: (a) selection of the most effective HCW treatment technology, (b) identification of the best HCW disposal site, (c) assessment of the best-performing healthcare unit adopting ideal HCWM strategies, (d) selection of third party logistics providers, (e) identification of HCWM barriers and (f) evaluation of specific HCWM plans. It is observed that the past researchers have mostly preferred to apply MCDM tools for solving HCW treatment technology selection problems, whereas analytic hierarchy process, decision-making trial and evaluation laboratory and best-worst method and fuzzy set theory have been the mostly favoured MCDM tool, criteria weight measurement techniques and uncertainty model, respectively. The outcomes of this article would help the healthcare personnel/policymakers in unveiling the current status of HCWM research, exploring extant research gaps and challenges and providing future directions leading to sustainable environment.

This paper presents a stochastic MILP model for the fair distribution of profits in a supply chain under ripple effect with risk-neutral primary suppliers and a robust manufacturer. The robustness is understood as the mean-risk fairness that aims at equitably efficient business-as-usual and worst-case performance of the manufacturer. The objective is to equitably optimise conditional profit-at-risk and expected profit of the manufacturer as well as expected profits of all primary suppliers. In addition, the backup suppliers are considered andrecovery supply portfolios optimised for each disruption scenario. To coordinate production across the entire supply chain, a collaborative partnership is applied enforcing the manufacturer's expected service level to be not less than the expected service level of each primary supplier. The findings indicated that if the manufacturer aims at equitably efficient maximisation of average and worst-case profit under collaborative partnership, the associated expected profits of all primary suppliers may also converge to their respective collaborative maxima, and the more reliable is supply chain environment, the closer to their maxima are the supplier's profits. The findings also demonstrated that the fair distribution of supply chain profits under collaborative partnership simultaneously enforces coordinated production of parts by primary suppliers and products by the manufacturer.abstract & iquest;Please edit the abstract down to no more than 200 words.

Purpose-Like all other service industries, evaluation of service quality in health-care units is a complex decision-making task involving multiple stakeholder groups with varying interest, conflicting qualitative criteria and competing health-care units. The past researchers have already attempted to solve this problem while integrating different uncertainty models with various multi-criteria decision-making (MCDM) tools. This paper aims to propose application of an MCDM method for evaluating service quality of health-care units in uncertain environment. Design/methodology/approach-This paper presents application of an integrated approach combining grey numbers with combined compromise solution (G-CoCoSo) method for appraising service quality of six Urban Primary Health Centers (UPHCs) in Kolkata, India, based on the opinions of three different stakeholder groups (health-care service recipients, medical officers and health-care administrators) against six subjective criteria (tangibles, responsiveness, service, assurance, empathy and hygiene). A sensitivity analysis is also performed to investigate the effect of varying values of lambda on the ranking performance of G-CoCoSo method. Findings-Based on the collective judgments of the three stakeholder groups expressed in grey numbers, tangibles is identified as the most important criterion, followed by responsiveness. On the other hand, assurance criterion has the least importance. The G-CoCoSo method singles out H3 as the best UPHC, followed by H1. On the contrary, H5 appears as the worst performing UPHC. The results of sensitivity analysis prove that this integrated method is insensitive to changing values of lambda. Similarly, a comparative study against other grey integrated state-of-the-art MCDM methods validates its solution accuracy. Originality/value-To the best of the authors' knowledge, G-CoCoSo is used for the first time in this paper to solve a health-care service quality evaluation problem demonstrating satisfactory results. It would assist both the health-care professionals and patients in identifying the relative strengths and weaknesses of each of the UPHCs under consideration.

Additive manufacturing (AM) is a disruptive technology that enables the fabrication of intricate geometries layer-by-layer by discretizing the given geometry into multiple slices. Overhangs are regions of these slices where the surface projection exceeds the underlying horizontal support. AM techniques, like material extrusion (MEX), require explicit support structures, which are added to ensure proper printability and dimensional stability. Although supports provide part balancing to avoid material sagging, they should be minimised as they increase the overall material usage, print time and associated costs. Limited studies have been done on the self-supporting capacity of thin-walled AM structures. This research presents a novel analytical model based on the beam bending principle to determine the material's limit to self-sustain overhangs. The model determines this limit in terms of an overhang angle (from the vertical) using part geometry, process parameters and material properties. It is found that the overhang angle has an inverse square root relation with an apparent number of layers, which can be linearly approximated as a function of the number of layers. The model is further extended to incorporate buckling effects in the extruder fibres. Analytical results showed that overhangs as high as 75o are possible without any external supports, as against the conventional 45 degrees limit. The presented model can alleviate the AM process by increasing the printing efficiency and reducing material wastage.

Environmental pollution is a pressing global issue, and the Internet of Things (IoT) offers transformative potential for its management through its application in advanced real-time monitoring and analytics. However, the heterogeneous and fragmented nature of IoT technologies poses challenges to seamless integration, limiting the efficacy of these solutions in addressing environmental impacts. This paper addresses these challenges by reviewing recent developments in IoT technologies, encompassing sensor networks, computing frameworks, and application layers for enhanced pollution management. A comprehensive analysis of 74,604 academic publications and 35,000 patent documents spanning from 2008 to 2024 is conducted using a textual analysis that combines quantitative bibliometric methods along with a qualitative analysis based on both scholarly research and patent innovations. This approach allows us to identify key challenges in IoT implementation for environmental monitoring-including integration, interoperability, and scalability issues-and to highlight corresponding architectural solutions. Our findings reveal emerging technology trends that aim to overcome a few of these challenges, and we present a scalable IoT architecture as key discussions that enhances system interoperability and efficiency for pollution monitoring. This framework provides targeted solutions for specific tasks in pollution monitoring while guiding decision-makers to adopt solutions effectively.

Column Generation (CG) is a well-established methodology for tackling large-scale real-world optimization problems. Nevertheless, as problem sizes increase, challenges like long-tail effects and degeneracy become more prevalent. Various strategies for stabilizing dual variables have demonstrated their effectiveness in mitigating these challenges. Generally, numerical tests are employed to identify the best parameter values for stabilized CG using different configurations for the same problem. This study introduces an innovative approach using machine learning (ML) to predict the best algorithm configuration, eliminating the need for extensive numerical experimentation. The core objective of this study is to predict optimal dual variables to generate improved bounds in the Restricted Master Problem of stabilized CG. By and large, this comprehensive approach represents a robust and flexible framework, optimizing algorithm configurations and expediting the convergence of the CG model. Extensive computational experiments confirm the efficacy of our ML-based approach inaccurately predicting optimal dual variables and outperforming conventional methods. The practical utility is exemplified in optimizing workforce scheduling, demonstrating significant reductions in computational time across problem instances. This real-world application highlights the remarkable benefits of the smart approach in enhancing the efficiency and effectiveness of CG-based optimization solutions.

This study focuses on the thermomechanical optimization of buckling resistance in laminated composite plate with a hole. The goal is to maximize the critical buckling load by identifying optimal fiber orientations within the layers using the Bonobo Optimizer Algorithm (BOA). The first-order shear deformation theory (FSDT) is employed to determine elastic buckling loads under combined thermomechanical loading. Numerical investigations are conducted for various parameters, including uniform temperature rises, edge loading conditions, support configurations, hole size ratios, load ratios, and geometric proportions. The results showed that these parameteres play a vital role in the the buckling load optimization of laminate composite plate with a hole. The study shows CCCC and SFSF boundary conditions yield the highest and lowest buckling loads, respectively. Critical buckling load decreases with temperature rise. Plates without cut-outs outperform those with cut-outs, and shorter plates under negative temperature rise achieve maximum buckling load. Uniform loading results in the lowest buckling capacity due to its larger loading area.

Purpose-The resource-intensive nature of the Construction Industry (CI) and resultant Environmental Impacts (EIs) calls for the adoption of a Circular Economy (CE) in construction demolition waste (CDW) management. In recent times, CDW management and CE have attracted attention from researchers and practitioners. However, there is a lack of comprehensive Scientometric analysis, specifically on CDW management in CI. The study aims to provide an in-depth Scientometric analysis to investigate a comprehensive overview of CE and CDW management studies in a global context. Further, the work aims to present various CE approaches and strategies for CDW management and emerging trends. Design/methodology/approach-To fill the literature gap, the present work provided an in-depth Scientometric analysis of the intersection of CDW management and CE. The analysis utilized the Scopus database for bibliographic data retrieval. R Studio Bibliometrix and VOSviewer were employed to generate and visualize bibliometric maps. A total of 982 publications retrieved from Scopus, covering the period from 2003 to 2023, were analyzed based on their titles, keywords and abstracts. Findings-The findings highlight three distinct clusters in the study of CDW in CE with emphasis on recycling-based research as the main cluster. They further point out to a weak connection between research on CE-compliant materials and the domains of waste management economics and absence of robust cross-country research collaboration networks. Trending topics include the use of LCA, BIM and machine learning as waste management techniques. Recommendations for future directions for research include LCA research on degenerating resources, application of CE principles, including circular business models and circular supply chains, and studies on Meso and Macro CE in CDW management. Originality/value-The originality primarily resides in its comprehensive mapping of the existing research landscape of CE in CDW using scientometric analysis for a broader time frame of 20 years. The uniqueness of this study lies in the fact that it provides a more granular view of research gaps through thematic clustering by coupling into three distinct themes and by pinpointing neglected areas such as facilitation of CE studies at meso and macro levels. The study also highlights the evolution of association of the concepts such as 3R and zero emissions over time, revealing weak linkages such as impact assessment on waste management economics. In addition, the study highlights the application of quantitative information to guide data-driven recommendations for decision-makers, practitioners and academic researchers in the frontier areas for CDW management and CE research.

In the realm of additive manufacturing, the quality and mechanical performance of 3D-printed structures are heavily influenced by the parametric settings used during the printing process. Some of these conditions include layer thickness, printing speed, infill density, filling pattern, printing substance, and so on. This research investigates the effect of printing factors on the strength characteristics of 3D-printed acrylonitrile butadiene styrene (ABS) as well as polylactic acid (PLA) structures. The parameters considered for the proposed study include material type, infill direction, infill density, and infill pattern. These four conditions are optimized using the machine-learning algorithm to obtain maximum strength in the resulting structures. The breaking load, extension, tensile strain, and tensile strength are the strength factors determined by the experiments. Furthermore, a deep neural network integrated with a walrus optimization algorithm (DNN-WOA)-based hybrid machine learning method is used in the experimental data to discover the optimal parametric conditions for constructing the structures with maximum strength. Based on the findings, infill density is a significant component in increasing both the tensile strength and elastic modulus of printed samples. Line and triangular-type infill patterns exerted the range of tensile strength with 10-20 MPa of deviations. In terms of tensile strain, the line pattern produced significantly more tensile strain than the triangle pattern. The highest breaking stress is observed at a 45 degrees raster angle, regardless of infill density. When compared to the strengths of ABS and PLA, the PLA with a triangle infill pattern outperformed the ABS structure printed with a line-type infill pattern. The optimum printing settings for a PLA structure with a line-type infill pattern are 0 degrees and 25% for raster angle and infill density, respectively. This parameter resulted in enhanced strength performance, with 45.81MPa of tensile strength, 12.13% of tensile strain, 359.34kgf of breaking load, 7.21mm of extension, and 24.12min of printing time.

The present study analyses the interplay between uncertainty and sustainability investment in the line of PMC (product market competition), and its impact on the firms' sustainable practices. Based on a sample of 2533 listed companies from 2011 to 2023, it was observed that uncertainty positively influences sustainable investment, and the PMC plays a moderating role in the case of G-20 countries. Furthermore, the research indicates that sustainable investment promotes long-term investment in G-20 countries during the study period and lessens the unfavorable outcome of uncertainty on a firm's value. We employed SGMM (System-generalized method of moments) to concern about the endogeneity issues and for robustness, which was consistent with the empirical results. The study's implications help investors, managers, and policymakers integrate sustainable investment practices with uncertainty alongside pushing sustainable development goals.

PurposeThe growing importance of crude palm oil (CPO) in India's overall edible oil imports encourages research on this commodity. India is one of the world's largest crude palm oil-consuming countries and an important destination market for Southeast Asian countries. This study identifies the critical determinants of crude palm oil imports into India and their impact on the import demand.Design/methodology/approachWe employed an autoregressive distributed lag (ARDL) model to analyse time series data from 1990 to 2021.FindingsThe findings reveal that the import price of crude palm oil, applied import duty and GDP have negative significant impact while exchange rate and openness have positive significant impact on crude palm oil imports in the long run. However, lagged import price, lagged applied import duty and openness have positive significant impact on CPO in the short run.Research limitations/implicationsThe traders, firms and industries dealing with crude palm oil may consider these factors for making their agribusiness strategies, while the policymakers at the government may leverage this for formulating the agri-trade policies that maximise the country's overall economic interests. Overall, this study provides some interesting policy implications and managerial lessons.Originality/valueOur study is a fresh attempt to model the determinants of crude palm oil import demand in India based on time series data. Indeed, our analysis helped us to identify the major factors that influence the crude palm oil import in India from international market.

PurposeThe digitalization of operations within small- and medium-sized enterprises (SMEs) remains a significant challenge, particularly in conceptualizing and assessing their readiness for such transformation in the context of Industry 4.0 (I4.0). While there is considerable literature on digitalization, limited attention has been paid to how SMEs can effectively create long-term value through digitalization. Hence, this study aims to identify the challenges for digitalizing operations and present an integrated framework to evaluate the level of preparation for I4.0 uptake for SMEs based on their capabilities to cross boundaries to create sustainable value.Design/methodology/approachThe majority of critical barriers concerning digitalization of operations have been derived from the literature and decided upon through fuzzy Delphi. Contextual interrelationships of the barriers, which have been derived, are established by mixing the Decision-making trial and evaluation laboratory method (DEMATEL) and Interpretive structural modeling (ISM) technique. Sensitivity analysis is conducted to verify the model's robustness.FindingsFear of job losses and absence of an attitude toward change are seen to be the principal obstacles to digitalization in the age of I4.0. Organizational culture, inadequate digital competences and poor financial resources are also seen to be key obstacles that prevent SMEs from harnessing digitalization for sustainable value creation. Moreover, structural obstacles like old technology infrastructure and absence of strategic alignment also hinder the advancement of digitization.Practical implicationsInsights from this work will help practitioners in strategy formation to identify and pinpoint significant barriers to digitalization. The framework developed offers a systematic approach to prioritizing these barriers based on their interdependence and causal relationships, enabling decision-makers to tackle the most critical issues first. This study provides a different aspect of digitalization for stakeholders to better understand the interrelationship between the barriers to the digitalization of operations for sustainable value creation in the era of I4.0. It offers specific guidance on overcoming resistance to change, upgrading digital skills and enhancing strategic focus within SMEs.Originality/valueOriginality of this work lies in identification of barriers from Indian perspectives for digitalization of operations in the I4.0 era for long-term sustainability and value creation. This study uniquely integrates ISM and DEMATEL methods to model the intricate relationships among various barriers. It offers a deeper understanding of the causal structures influencing digitalization. Moreover, it bridges a significant gap in the literature by highlighting the operationalization of I4.0 concepts within SMEs and addressing the challenges they face in creating sustainable value through digitalization.

PurposeThis study aims to examine the robustness and resilience of supply chain networks under various disruption scenarios, focusing on how these disruptions propagate through the network, a phenomenon known as the ripple effect.Design/methodology/approachA theoretical model is developed to assess product flow through a multi-echelon supply chain under demand uncertainties. Within this setting, numerical analysis is conducted to measure the customer fill rate and at the same time to assess the impact of disruptions at the final echelon, capturing the ripple effect from distant nodes.FindingsThe study provides insights into the types and intensities of risks faced by multi-echelon supply chain networks. It highlights the repercussions of disruptions and identifies recovery measures to minimize and manage their impact, enabling the system to regain stability.Originality/valueThis research contributes to a deeper understanding of supply chain risks and their management by exploring the ripple effect in multi-echelon supply chains and offering strategies to enhance network resilience and robustness.

The circular economy is crucial in promoting environmental justice by reducing the uneven burden of environmental hazards on marginalized communities and low-income individuals. The circular economy transition is increasingly a pivotal strategy for achieving carbon neutrality. Recent shifts in stakeholder focus towards carbon neutrality have made net-zero and environmental social and governance (ESG) hot topics to study, as they can lead to environmental justice. This research delves into the significance of net-zero policies and stakeholder pressure in integrating circular economy concepts into supply chains, aiming to establish a carbon-neutral supply chain within the environmental justice framework. Even though there is a lot of literature about the circular economy, not much is known about how it affects the adoption of the circular economy, the involvement of stakeholders, and net zero policy. Specifically, the role of innovation capability in this context has not been fully explored. This research aims to clarify the role of stakeholders in promoting circular economic practices in supply chains and their impact on carbon neutrality. It also examines how net-zero policies and innovation capabilities affect each other. To achieve this, we used structural equation modeling on data from 217 manufacturing firms from October 2023 to January 2024. Findings highlight a positive relationship between stakeholder pressure and circular economy integration, contributing to carbon-neutral supply chain outcomes. Results show net-zero policies moderate the stakeholder and circular economy relationship, with innovative capabilities acting as a partial mediator. This study adds to the literature by explaining the complex relationships between stakeholder pressures, circular economy practices, and the role of net zero policies and innovation capabilities in advancing carbon-neutral supply chains. This research makes a unique contribution to theory by examining the impact of circular economy practices on social well-being and environmental benefits, particularly regarding environmental justice.

Transparency encompasses the potential to monitor operations instantaneously so that required corrective actions can be taken as needed. Transparency entails the ability to track processes in real-time, enhance the visibility of the operations, and require a seamless network for improved communication for smart manufacturing systems. However, there is a lack of proper metrics to assess the transparency of smart manufacturing environments. This paper contributes to the assessment of transparency by proposing a metric for its evaluation. In doing so, we found that the assessment of transparency takes the quantification of traceability into account. Hence, a step-in assessment is conducted by initially developing a mathematical model for traceability, followed by a model for transparency. The model is validated by analysing the sensitivity and applicability through simulation-based experimentation. The results demonstrate the level of traceability followed by transparency with the implementation of smart manufacturing systems. A point of inflexion that determines the variability in the offerings of traceability at a given set of inputs was found. This is one of the few works that focus on the development of a metric for quantifying transparency through the traceability of smart manufacturing systems. Furthermore, it investigates the behaviour by analyzing the sensitivity of the model through simulation-based approaches, which is a unique addition to the realm of the smart manufacturing literature. Managers can refer to this study's findings to design the deployment of smart manufacturing systems with informative trade-offs to maintain their required traceability and transparency capabilities.

Manufacturing industries are under increased pressure to implement more sustainable practices to safeguard the environment and advance the welfare of humanity due to evolving ecological conditions. Industry 4.0 technologies are essential for transforming conventional manufacturing into sustainable practices by integrating Cyber-Physical Systems. Nevertheless, the manufacturing sector in developing countries has been notably slow to implement Industry 4.0, primarily due to the numerous associated risks. Specifically in the context of developing nations, the existing literature demonstrates a substantial gap in research that addresses these Industry 4.0 risks. Therefore, it is imperative to assess the potential risks linked with the adoption of Industry 4.0 and to examine viable alternatives or solutions. This paper attempts to identify and prioritize the most effective solutions for mitigating the risks associated with adopting Industry 4.0 in manufacturing industries in developing countries. Thirty-seven risks and eighteen solutions related to Industry 4.0 adoption have been considered for proposing the framework based on the Spherical fuzzy full consistency method (SF-FUCOM) and Spherical fuzzy multi-attributive border approximation area comparison (SF-MABAC) methods. The results emphasized several critical risks, including economic, social, environmental, technological, operational, and strategic, that are associated with adopting Industry 4.0. The proposed framework is a valuable reference aid for researchers, policymakers, and decision-makers, as it eases the implementation of Industry 4.0 in manufacturing. The present study also provides a clear path for developing nations to implement Industry 4.0 and assists them in prioritizing critical factors that are pertinent to their manufacturing industries.

The implementation of Industry 5.0 into demand-driven supply chains is crucial for organisations to navigate the complexities of the global marketplace. The purpose of this work is to identify supply chain performance factors that will provide insights into measuring supply chain complexity after Industry 5.0 implementation. This work integrated the fuzzy Delphi method along with the WINGS method to determine the causal linkages between identified factors. The findings suggest that customer responsiveness, proactive risk and mitigation, and agility emerged as the most important factors. This work helps organisations implement Industry 5.0 into their supply chain strategies. In addition, this initiative focuses on organizational readiness, technology acceptance, and the development of a responsive culture in order to fully realize Industry 5.0's promise for managing supply chain complexity. In an era of unprecedented challenges, the findings offer practitioners a road map for leveraging Industry 5.0 for strong and effective supply chain management.

By deploying human-like qualities to non-human entities, anthropomorphism offers users an interactive, cognitive, affective, and social experience. Emerging applications of conversational AI with a blend of anthropomorphism are changing the way businesses interact with customers. To take the field forward, this paper emphasizes the importance of anthropomorphism in AI agents and advocates for the need to systematize, integrate, and categorize existing efforts through a systematic literature review. The authors employ the SPAR-4-SLR protocol, which enables the investigation of a vast array of peer-reviewed journal articles. Our study focuses on articles published over 23 years, from 2000 to 2023, ensuring a comprehensive and up-to-date understanding of the subject matter. By meticulously analyzing 302 diverse documents, this study unveils the rapid emergence of anthropomorphism in AI. The authors additionally identify six pivotal knowledge clusters that shed light on the phenomenon: human interaction with anthropomorphic AI agents, technology acceptance toward anthropomorphic AI, anthropomorphism in customer service, anthropomorphism in AI-social companions, applications of anthropomorphism, and anthropomorphic AI for interactive marketing. The current review stimulates academicians and scholars to explore uncharted territories and develop novel theoretical frameworks encompassing anthropomorphism in marketing by posing intriguing and thought-provoking questions.

PurposeThe paper proposes the Digital and Sustainable Supply Chain Maturity Model (DSSCMM), a comprehensive framework for analysing and developing maturity levels in the supply chain.Design/methodology/approachThe maturity model was developed using a multi-method approach, utilizing Grey Influence Analysis (GINA) to evaluate the overall maturity of three Indian manufacturing companies.FindingsThe application of DSSCMM among three case organizations reveals that organizations with higher maturity levels require stronger operational capabilities, better collaboration and advanced digital and sustainable technologies. Successful initiatives should align with organizational strategy, enhancing operational, technological, sustainability, governance and compliance aspects. Case Organization A stands first with a maturity score of 3.39. Case Organization C is on the preliminary level with a score of 2.26, and Case Organization B has primitive level of maturity, which stands at 1.91.Research limitations/implicationsThe limitation of this study is that the model was tested and validated in only three case organizations within the Indian context. Further testing across diverse sectors and geographical regions is required to improve the generalisability.Practical implicationsThe model aids organizations in self-evaluation, enabling the creation of tailored strategies across various dimensions, benefiting practitioners, supply chain managers and academic researchers.Originality/valueThis paper focuses on quantifying digital and sustainable supply chain maturity, offering a comprehensive model focusing on cost savings, value creation and revenue growth.

The steel converter facility is an essential part of the steel manufacturing process and is responsible for converting molten iron into steel. The material movement within the facility depends on the effective overhead crane scheduling. However, the traditional approach of crane scheduling at these facilities leads to inefficiencies. To understand this need, we propose an integrated approach using Simulation modelling, Digital Twin (DT) and Reinforcement Learning (RL) to improve the crane scheduling operation of the steelmaking process. We developed a simulation model using discrete event and agent-based simulation and connected it with real-time data by applying the digital twin approach. Further, a Deep Reinforcement Learning (DRL) agent is trained in a stochastic environment with multiple feedback loops and complex interactions for crane scheduling. The DRL agent recommends the sequences of crane movement to reduce the wait time in bottleneck operation. The experimental results demonstrate that the proposed method gives results within 3.6% of the optimal on average. A case of the steel converter facility illustrates this approach, which is also applicable to other industries requiring complex scheduling, such as construction, automotive, and logistics, to improve operational efficiency.