A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.

A review of the emerging technologies and systems to mitigate food fraud in supply chains.

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties.

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

A Holistic Framework for Evaluating Food Loss and Waste Due to Marketing Standards across the Entire Food Supply Chain.

This paper addresses the critical and urgent need to reduce food losses and waste (FLW) resulting from stringent marketing standards. It proposes a comprehensive and actionable framework grounded in the three pillars of sustainability-environmental, economic, and social-to effectively evaluate FLW across the entire food supply chain. The paper involves a thorough review of existing marketing standards, including research on FLW due to marketing standards, and proposes the implementation of targeted key actions within four key food sectors: fruits, vegetables, dairy, and cereals. The study provides a detailed analysis of the significant impact marketing standards have on FLW at various stages of the supply chain, including primary production, processing, retail, and consumption. By focusing on these critical points, the research underscores the necessity of addressing marketing standards to achieve meaningful reductions in FLW. The proposed framework aims to foster improved business practices and drive the development of innovative, sector-specific solutions that balance sustainability goals with economic viability. The holistic approach followed for this research lays the foundation for ensuring that the proposed framework is adaptable and practical, leading to measurable improvements in reducing FLW and promoting sustainability across the food industry.

Developing Food Consumer Attitudes towards Ionizing Radiation and Genetic Modification.

BACKGROUND/OBJECTIVES: This study investigates consumer perceptions and acceptance of ionizing radiation (IoR) as a perishable food stabilisation technology. Consumers' preferences influence the success of emerging food technologies. Therefore, a comprehensive understanding of consumers' behavioural responses and their development over time is essential. METHODS: This research employs a mixed-methods approach, surveying 313 young adults in New Zealand on their views of both irradiated (IoR) and genetically modified (GM) highly perishable foods. This study explored both participants' attitudes towards these two technologies and also their willingness to consume these foods. RESULTS: The qualitative research revealed a preponderance of "affective" associations over "cognitive" associations with regard to both IoR and GM technologies. The quantitative research indicated that where consumers were given time to reflect, evaluations of GM improved, while those of IoR did not (p < 0.01). There was a gender divide, with females being more positively inclined towards GM and males towards IoR (p < 0.01). Both technologies were significantly disfavoured compared to non-treated products (p < 0.01). There was a significant discrimination when the two technologies were presented as concepts and as products. GM was more favourably received as a concept than as a product (p < 0.01), while IoR was disfavoured in either form. The two food neophobia scales that were tested showed a divergence in performance, with the more affectively based scale showing a higher level of correlation with behaviour. CONCLUSIONS: This research reveals that a largely affective (visceral) distrust of both IoR and GM exists within this young food consumer sample. As it is affective in nature, this position will be very resistant to education efforts, particularly if they are "cognitively" based. However, a significant softening of these affective attitudes towards GM products indicates that such efforts may be effective, given time and investment.

Dynamics analysis of green supply chain under the conditions of demand uncertainty and blockchain technology.

This research investigates the implications of incorporating blockchain technology into the process of making decisions for green supply chains, particularly under conditions of demand uncertainty. A model was formulated to encompass both environmentally friendly products enabled by blockchain technology and those without such enabling technology. The study further explores the optimal method of introducing green input in a duopoly market using game theory. It also examines how consumer uncertainty about green products and acceptance of the technical parameters of blockchain influence this strategy. The findings suggest that increased consumer uncertainty can, in some instances, motivate manufacturers to enhance the eco-friendliness of their products and improve supply chain performance. However, the universal adoption of blockchain does not necessarily ensure better results; on the contrary, it may compromise product sustainability while enhancing supply chain profitability. Moreover, research has indicated that products enabled by blockchain typically have lower prices, thereby offering potential benefits to consumers when acceptance of sustainable energy solutions is high or uncertain. Additionally, this paper analyzes the impact of changes in green supply chain decision-making on system reliability. This involves exploring the relationship between decision parameters and consumer reluctance towards sustainable products and the adoption of blockchain technology. To ensure stable market competition in dynamic complex systems, research shows that feedback control technology can effectively regulate unpredictable behavior.

Approach Based on the Ordered Fuzzy Decision Making System Dedicated to Supplier Evaluation in Supply Chain Management.

The selection of suppliers represents a pivotal aspect of supply chain management and has a considerable impact on the success and competitiveness of the organization in question. The selection of a suitable supplier is a multi-criteria decision making (MCDM) problem based on a number of qualitative, quantitative, and even conflicting criteria. The aim of this paper is to propose a novel MCDM approach dedicated to the supplier evaluation problem using an ordered fuzzy decision making system. This study uses a fuzzy inference system based on IF-THEN rules with ordered fuzzy numbers (OFNs). The approach employs the concept of OFNs to account for potential uncertainty and subjectivity in the decision making process, and it also takes into account the trends of changes in assessment values and entropy in the final supplier evaluation. This paper's principal contribution is the development of a knowledge base and the demonstration of its application in an ordered fuzzy expert system for multi-criteria supplier evaluation in a dynamic and uncertain environment. The proposed system takes into account the dynamic changes in the value of assessment parameters in the overall supplier assessment, allowing for the differentiation of suppliers based on current and historical data. The utilization of OFNs in a fuzzy model then allows for a reduction in the complexity of the knowledge base in comparison to a classical fuzzy system and makes it more accessible to users, as it requires only basic arithmetic operations in the inference process. This paper presents a comprehensive framework for the assessment of suppliers against a range of criteria, including local hiring, completeness, and defect factors. Furthermore, the potential to integrate sustainability and ESG (environmental, social, and corporate governance) criteria in the assessment process adds value to the decision making framework by adapting to current trends in supply chain management.

Sustainability and circularity assessment of biomass-based energy supply chain.

Climate change and other environmental consequences of socio-economic activities require a more sustainable and circular growth. At the same time, the limitation of the earth resource demands industries to improve resource efficiency and increase the rate of recycling of materials. There are several sustainable and circular alternatives that the industries may adopt. However, the question is that among these alternatives, which one should be selected for implementation for the highest sustainable and circular benefits. This study introduces a novel tool for assessing the sustainability and circularity of biomass-based energy supply chains, integrating multi-criteria decision-making methods with life cycle thinking approach. It evaluates five alternatives using a sustainability and circularity indicators, offering new insights into the deloyment of circular business models at companies in biomass-based energy supply chain. The tool is also applied to a specific rice straw supply chain in Italy, to assess the sustainability and circularity of five alternatives and outrank them. The results indicated that not all the alternatives are better in terms of supporting sustainable development and circular economy, compared to the baseline business model. In this supply chain, the extended lifetime for digestate from the aerobic digestion plant is the most 'sustainable and circular' alternative, while the capture of carbon dioxide from the same plant and its use for microalgae cultivation is the least 'sustainable and circular' alternative. A sensitivity analysis was conducted on different weighting sets during the assessment. It indicated that the priority of the decision makers can slightly change the outrank of the alternatives and the magnitude of the outranks.

Evaluation of risk strategies for supply chain sustainability with interval-valued neutrosophic fuzzy EDAS.

Sustainability means the most effective and efficient use of existing resources to meet the needs of future and current generations. Nowadays, with the increase in global environmental problems as well as social and economic problems, sustainability in the supply chain of the automotive industry has become increasingly important. In our study, after conducting an extensive literature review in the automotive industry, consulting with experts, and identifying 35 different Sustainability Risks across 5 sustainability dimensions, we proposed a total of 40 risk strategies to mitigate these risks. Using the evaluations of automotive supply chain experts from one of Turkey's leading logistics companies, risk strategies were ranked using the Interval-Valued Neutrosophic Fuzzy Evaluation based on Distance from Average Solution (IVN Fuzzy EDAS) method, and the best strategies were determined. Accordingly, it has been determined that the optimal approach for companies is to establish and implement legal compliance procedures and programs while adhering to prevailing regulations and policies. It aims to make a useful contribution to the literature in terms of covering this subject for the first time with fuzzy EDAS method, adding new sustainability risk dimensions, and developing broad-level risk and risk prevention strategies on the subject. In this study, the interval-valued neutrosophic fuzzy EDAS method was applied for the first time to the issue of sustainability risks in the automotive industry. At the same time, this study aims to contribute to the literature by adding new dimensions to sustainability.

Role of supply chain digitalization and global supply chain in decarbonization of natural resources sector supply chain.

Environmental management and decarbonization are becoming increasingly critical as industries seek sustainable development paths that align with global climate targets. As businesses aim to reduce their carbon footprint, integrating effective environmental strategies with technological advancements is essential. This necessitates a clear understanding of digital technologies and environmental policies to drive decarbonization efforts. In this context, this study explores the role of digitalization in reducing carbon emissions in the natural resources sector supply chain. Furthermore, it examines the role of supply chain mapping as a mediator in the relationship between supply chain digitalization and supply chain decarbonization efforts, providing a novel perspective on how technology can intersect to foster environmental sustainability. This research targets the metal sector supply chain, a significant contributor to global carbon emissions. The data for the study was gathered through a questionnaire administered to 246 employees from various levels of the metal industry in China. We employed Covariance-based Structural Equation Modelling (CB-SEM) using AMOs for analysis. Our results show that level digitalization of supply chain processes in the metal sector has a profound and direct impact on its supply chain decarbonization. Digital technologies facilitate more efficient resource use, improved energy efficiency, and the adoption of cleaner production methods. Importantly, the study identifies supply chain mapping as a crucial mechanism through which digitalization exerts its positive influence on decarbonization. Through detailed mapping, organizations can gain visibility over their entire supply chain, identifying carbon hotspots and opportunities for improvement that digital solutions can address. The novelty of this study lies in its systematic exploration of how supply chain mapping serves not just as a tool for transparency and efficiency but as a strategic facilitator of the beneficial impacts of digitalization on decarbonization.

Forced randomization: the what, why, and how.

BACKGROUND: When running a randomized controlled trial (RCT), a clinical site may face a situation when an eligible trial participant is to be randomized to the treatment that is not available at the site. In this case, there are two options: not to enroll the participant, or, without disclosing to the site, allocate the participant to a treatment arm with drug available at the site using a built-in feature of the interactive response technology (IRT). In the latter case, one has employed a "forced randomization" (FR). There seems to be an industry-wide consensus that using FR can be acceptable in confirmatory trials provided there are "not too many" instances of forcing. A better understanding of statistical properties of FR is warranted. METHODS: We described four different IRT configurations with or without FR and illustrated them using a simple example. We discussed potential merits of FR and outlined some relevant theoretical risks and risk mitigation strategies. We performed a search using Cortellis Regulatory Intelligence database (IDRAC) ( www.cortellis.com ) to understand the prevalence of FR in clinical trial practice. We also proposed a structured template for development and evaluation of randomization designs featuring FR and showcased an application of this template for a hypothetical multi-center 1:1 RCT under three experimental settings ("base case", "slower recruitment", and "faster recruitment") to explore the effect of four different IRT configurations in combination with three different drug supply/re-supply strategies on some important operating characteristics of the trial. We also supplied the Julia code that can be used to reproduce our simulation results and generate additional results under user-specified experimental scenarios. RESULTS: FR can eliminate refusals to randomize patients, which can cause frustration for patients and study site personnel, improve the study logistics, drug supply management, cost-efficiency, and recruitment time. Nevertheless, FR carries some potential risks that should be reviewed at the study planning stage and, ideally, prospectively addressed through risk mitigation planning. The Cortellis search identified only 9 submissions that have reported the use of FR; typically, the FR option was documented in IRT specifications. Our simulation evidence showed that under the considered realistic experimental settings, the percentage of FR is expected to be low. When FR with backfilling was used in combination with high re-supply strategy, the final treatment imbalance was negligibly small, the proportion of patients not randomized due to the lack of drug supply was close to zero, and the time to complete recruitment was shortened compared to the case when FR was not allowed. The drug overage was primarily determined by the intensity of the re-supply strategy and to a smaller extent by the presence or absence of the FR feature in IRT. CONCLUSION: FR with a carefully chosen drug supply/re-supply strategy can result in quantifiable improvements in the patients' and site personnel experience, trial logistics and efficiency while preventing an undesirable refusal to randomize a patient and a consequential unblinding at the site. FR is a useful design feature of multi-center RCTs provided it is properly planned for and carefully implemented.

A dynamic management model for sustainable drug supply chain in hospital pharmacies in Iran.

BACKGROUND: Sustainable supply chain management encompasses the strategic coordination and control of material, information, and financial flows, as well as the collaborative efforts among the entities engaged in the medicinal supply chain. This research proposes a dynamic and sustainable supply chain management model tailored explicitly for the inpatient pharmacies of Medical Centers and Hospitals affiliated with Iran University of Medical Sciences. METHODS: This is a quantitative study in terms of research objective and a qualitative study based on the stages in the conceptual development of the model. Therefore, the current study can be considered a mixed-methods approach. After identifying the key factors influencing the sustainability of the medicine supply chain, we conducted a dynamic analysis of the problem using system dynamics methodology. In order to simulate the system's behavior over 24 months, we utilized a combination of existing documentary information and expert opinions. The developed model was implemented using Vensim PLE software, allowing us to simulate and analyze the impact of various policies on the system. RESULTS: Medicine disposal exhibited an upward trend, particularly during the second 12-month period. Conversely, the trend of medicine expirations remained relatively stable in the initial months but showed an upward trajectory after that. The cost associated with disposed medicine experienced a consistent increase, with a higher rate observed during the second 12-month period. In contrast, sales of low-consumable medicine experienced a significant initial surge followed by a slower growth rate. Finally, the pharmacy's profit demonstrated an overall increasing trend, although the rate of increase was higher during the first 12 months. CONCLUSION: Among the various scenarios considered, namely "increasing the adequacy of human resources," "increasing the speed of response," and "utilizing pharmacists in the drug prescribing team," it was found that these interventions had a substantial effect on both enhancing the pharmacy's profit and reducing medication waste. Therefore, these scenarios were identified as having the most significant impact. The proposed model can serve as a valuable tool for forecasting and informing policy-making, providing insights into addressing the challenges associated with the sustainable drug supply chain in hospital pharmacies.

Fuel for collective action: A SWOT analysis to identify social barriers and drivers for a local woody biomass supply chain in an Italian alpine valley.

The role of woody biomass in the clean energy transition is substantial in the EU. Forest residues are one of the main biomass sources that can be used for energy production, but their use to support the energy transition is still limited for several reasons. Research has shown that the use of forest residues in energy production can be effectively stimulated through collective actions that aim to develop short and local supply chains. This study aims to identify the barriers and drivers for the development of a local supply chain for forest residues in an Italian alpine valley, gathering and analysing the perspectives of all involved local actors, that is, (i) suppliers - the communities that own the forest resources, (ii) intermediaries - the forest professionals providing extension and advice services to owners and the harvesting companies; and (iii) the final consumers, in this case the local municipalities and hospitality enterprises. Data are analysed using a SWOT analysis. The results show that the suppliers identified opportunities especially, while the final consumers focused more on strengths, weaknesses, and threats. The SWOT categories in terms of the number of different factors were weaknesses (37 %), strengths (27 %), threats (18 %), and opportunities (17 %). Opportunities and strengths were considered as drivers, while threats and weaknesses were barriers. Several drivers emerged, such as a general predisposition toward the development of a local supply chain for forest residues, social homogeneity in terms of knowledge and management of the land, and common challenges. Barriers also emerged in the form of limited know-how on the supply chain potential, but also in limited availability to concede control between different forest owners over their property. The SWOT results are useful to design strategies to support the development of the supply chain: four possible strategies, amongst which flexible cooperation processes between different categories of stakeholders, and the organisation of a buying group of the hospitality enterprises, were suggested.

Pricing and production R&D decisions in power battery closed-loop supply chain considering government subsidy.

With the continuous promotion of electric vehicle applications, the recycling of power battery is urgent. Some batteries, although not suitable for continued use in electric vehicles, can be recycled for echelon utilization or remanufacturing. Thus, this study considers an electric vehicle power battery closed-loop supply chain consisting of a manufacturer and a retailer. We develop three strategies: no production research and development effort strategy, production research and development effort strategy, and government subsidy for production research and development strategy. Optimal pricing and production research and development effort decisions are derived based on Stackelberg game. Results indicate that production research and development positively impact the recycling of waste electric vehicle power batteries, with government subsidy further amplifying this effect by offering higher buyback and recycling prices. Government subsidy encourages manufacturer to increase production research and development effort and lowers the market pricing of electric vehicle power batteries, making these batteries more accessible to consumers. A sizable consumer base can offset the increased costs of production research and development, enabling manufacturer and retailer to achieve greater profitability. Both manufacturer and retailer can benefit from production research and development and government subsidy, ultimately enhancing the profitability of the entire closed-loop supply chain.

Drug shortage stewardship: A novel solution for an old problem.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Vaccine development using artificial intelligence and machine learning: A review.

The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.

The dynamic nexus between economic growth, renewable energy use, urbanization, industrialization, tourism, green supply chain management, and CO2.

Since 2012, China has pursued an "ecological civilization" policy to promote green energy, increase environmental protection, and transition to more sustainable growth models. The complicated positive trends in energy consumption, more sustainable economic growth, and ecological management are obscured by China's persistent, significant dependence on fossil fuels, particularly coal. The study aims to analyze how renewable energy use in China affects carbon dioxide emissions and how those impacts change over time, as well as urbanization, industrialization, tourism, and green supply chain management. The DOLS dynamic system method used historical data from 1995 to 2022. The DOLS results show a positive and statistically significant economic growth coefficient in the long term, suggesting that an increase of only one percent in CO2 emissions rise would be proportional to a surge in economic growth. Furthermore, using renewable energy sources correlates with long-term sustainability negatively and significantly. The results show reducing CO2 emissions and boosting renewable energy use by 1 %. Furthermore, the long-run coefficients for industrialization and urbanization are positive and statistically significant, indicating that a 1 % increase in either component results in a comparable increase in CO2 emissions. Sustainable logistics and tourism have negative and statistically significant coefficients, meaning that a one percent increase will gradually decrease carbon dioxide emissions. The estimated findings hold up when using other estimators, such as the commonly used co-integrating regression (CCR) strategy and fully modified least squares (FMOLS). When Granger causality is coupled, the test also catches the variables' causal link. test. To achieve environmental sustainability, the essay suggests using robust regulatory policy tools to curb ecological deterioration.

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance.

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutical life cycle. It discusses the application of machine learning algorithms, data analytics, and predictive modeling to accelerate drug discovery processes, optimize formulation development, enhance manufacturing efficiency, ensure stringent quality control measures, and revolutionize post-market surveillance methodologies. By describing the advancements, challenges, and future prospects of harnessing AI in the pharmaceutical landscape, this review offers valuable insights into the evolving dynamics of drug development and regulatory practices in the era of AI-driven innovation.

A thorough overview of the literature on waste recycling in the circular economy: current practices and future perspectives.

To develop a circular economy (CE) and protect the environment, waste recycling (WR) is crucial. This study examines WR research conducted over the past two decades to identify the most significant advancements and promising areas for future research. The following challenges were handled through text mining, content, and bibliometrics analysis: How has CE influenced the evolution of WR research? What are the CE's most important WR research trends and themes? What directions could future research on WR take regarding the CE transition? Using 1118 articles from the Scopus database journal, bibliometric networks were made and analyzed. Hence, five critical CE-related problems needing further research were recognized: waste recycling is the first cluster, followed by technology, the CE transformation, plastic waste, and waste management (WM). Examining WM and inclusive waste reduction practices and their distinct highlight patterns may impact future research fields and serve as a transitional tool to CE (which aims to minimize waste generation). Forthcoming research targets contain waste reduction and incorporation of WR into the CE framework.