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A Multistage Stochastic Optimization Model for Resilient Pharmaceutical Supply Chain in COVID-19 Pandemic Based on Patient Group Priority

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... The escalating degree of globalization and interdependence among businesses has firmly entrenched the supply chain as an integral component of the business environment [1]. Consequently, upcoming competition is poised to shift from an organizational level to a supply chain level [2]. Hence, the application of a comprehensive perspective that addresses diverse concerns within a supply chain becomes imperative. ...
... Step 1: The normalization process utilizing Eqs. (1) and (2). ...
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The performance of supply chains is directly impacted by overarching strategies and management paradigms. Success in the contemporary business landscape necessitates a comprehensive perspective that caters to the diverse needs of the market. The LARG (Lean-Agile-Resilient-Green) paradigm stands out as a versatile solution capable of addressing various concerns within the supply chain. This study introduces an innovative integrated framework for the implementation of the LARG supply chain, drawing upon insights from literature and expert knowledge, and encompassing 14 key factors. Subsequently, employing the grey-DEMATEL (decision-making trial and evaluation laboratory) method, we quantitatively measure the interrelations among these factors, culminating in the development of a structural model. The research findings underscore the significance of a technological approach as the most impactful factor in facilitating the LARG paradigm within the supply chain.
... The development of vaccines against diseases involves complex research and engineering efforts to create effective immunological responses, which can significantly reduce the impact of infectious diseases on public health. However, these efforts often face challenges such as ethical considerations, logistical complexities, and the need for tailored strategies to manage outbreaks effectively [45][46][47]. Research in digital health explores how technological advancements, such as digital games and online interactions, influence mental health, highlighting both the potential therapeutic applications and the risks of addiction and other negative impacts [48][49]. Research in healthcare systems and medical interventions, such as cochlear implants in young children, critically assesses the impact of health policies and treatment timing on patient outcomes, revealing the intricate connections between policy decisions, healthcare delivery, and individual health advancements [50]. ...
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Overview: Oral cancer poses significant health challenges with high mortality and systemic side effects from conventional chemotherapy. This study developed folic acid-conjugated nanoniosomes for targeted delivery of bleomycin, leveraging folate receptor overexpression on oral cancer cells to enhance drug delivery and reduce off-target effects. Methods: Nanoniosomes were synthesized via the thin-film hydration method using cholesterol, nonionic surfactants, and folic acid. Bleomycin was loaded into the hydrated lipid film, and the suspension was sonicated to achieve uniformity. Dynamic light scattering (DLS) characterized particle size, PDI, and zeta potential, while scanning electron microscopy (SEM) assessed morphology. Cytotoxicity was evaluated using the MTT assay on folate receptor-positive oral cancer cells treated with varying bleomycin concentrations over 24, 48, and 72 hours. Results: The nanoniosomes averaged 230 ± 15 nm in size with a PDI of 0.21 ± 0.03 and a zeta potential of -28 ± 2 mV, indicating stability. SEM revealed spherical, smooth particles. Cytotoxicity tests showed a time- and dose-dependent reduction in cell viability, with IC50 values decreasing from 20 µM at 24 hours to 10 µM at 72 hours. Conclusion: Folic acid-conjugated nanoniosomes demonstrated effective targeted delivery of bleomycin, enhancing cytotoxicity and minimizing systemic toxicity. These findings support further investigation for clinical applications in oral cancer therapy.
... [1], [6], [29], [95], [97] Provide fair service C31 ...
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The integration of artificial intelligence and blockchain in healthcare promises a significant transformation in data management, service quality improvement, and increased patient data security. Blockchain, by offering a decentralized and transparent platform, enhances the reliability and security of information. Meanwhile, artificial intelligence, with its ability to analyse and process data, helps identify patterns and predict treatment outcomes. The aim of this study is Evaluation and prioritization of artificial intelligence integrated blockchain factors in the healthcare supply chain using F-AHP and F-DEMATEL. Following a review of previous studies, four criteria and 23 sub-criteria were identified. In the first step, these criteria were ranked using the F-AHP method. In the second step, relationships among the sub-criteria were determined through F-DEMATEL, identifying causal and effect criteria. The F-AHP results show that among the 23 sub-criteria identified from previous studies, "integration of treatment processes (C32)", "Provide fair service (C31)", "health monitoring (C12)", "security of medical data (C34)", and "clinical decision support (C21)" ranked first to fifth, respectively. The F-DEMATEL results indicate that sub-criteria are divided into causal and effect categories, with "stakeholder participation (C42)" and "technology acceptance (C44)" being the most important causal sub-criteria, while "monitoring the treatment process (C15)" and "patient-centered treatment strategies (C22)" were identified as the most important effect sub-criteria. These findings suggest that the use of AI-blockchain integration in healthcare can lead to significant improvements in managing healthcare systems.
... The success of deep learning in managing complex data across various domains, such as images, text, and audio signals, motivates its application to EEG-based emotion recognition [52,53]. Deep learning is currently being used in hot topics such as COVID-19 [54,55], speculative hype [56], password meter [57], social media [58], music [59], tackling domain shifts [60], modeling analysis [61], image and attention detection [62,63], postoperative intensive care unit [64], predictive [65,66], mathematical modeling [67], risk behavior [68,69], peer assessment [70], optimizing [71], multi objective [72,73], human decision [74]. ...
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In recent years, significant advancements have been made in the field of brain–computer interfaces (BCIs), particularly in the area of emotion recognition using EEG signals. The majority of earlier research in this field has missed the spatial–temporal characteristics of EEG signals, which are critical for accurate emotion recognition. In this study, a novel approach is presented for classifying emotions into three categories, positive, negative, and neutral, using a custom-collected dataset. The dataset used in this study was specifically collected for this purpose from 16 participants, comprising EEG recordings corresponding to the three emotional states induced by musical stimuli. A multi-class Common Spatial Pattern (MCCSP) technique was employed for the processing stage of the EEG signals. These processed signals were then fed into an ensemble model comprising three autoencoders with Convolutional Neural Network (CNN) layers. A classification accuracy of 99.44 ± 0.39% for the three emotional classes was achieved by the proposed method. This performance surpasses previous studies, demonstrating the effectiveness of the approach. The high accuracy indicates that the method could be a promising candidate for future BCI applications, providing a reliable means of emotion detection.
... The increasing reliance on data-driven strategies and the growing availability of electronic health records highlight how critical ML is to enhancing clinical outcomes as the healthcare landscape evolves. By streamlining the decision-making process and improving diagnostic accuracy, this technological integration ultimately improves patient care [48,50]. ...
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The gut microbiota significantly impacts human health, influencing metabolism, immunological responses, and disease prevention. Dysbiosis, or microbial imbalance, is linked to various diseases, including cancer. It is crucial to preserve a healthy microbiome since pathogenic bacteria, such as Escherichia coli and Fusobacterium nucleatum, can cause inflammation and cancer. These pathways can lead to the formation of tumors. Recent advancements in high-throughput sequencing, metagenomics, and machine learning have revolutionized our understanding of the role of gut microbiota in cancer risk prediction. Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. Additionally, the investigation of the microbiome has been transformed by next-generation sequencing (NGS), which has made it possible to fully profile both cultivable and non-cultivable bacteria and to understand their roles in connection with cancer. Among the uses of NGS are the detection of microbial fingerprints connected to treatment results and the investigation of metabolic pathways implicated in the development of cancer. The combination of NGS with machine learning opens up new possibilities for creating customized medicine by enabling the development of diagnostic tools and treatments that are specific to each patient’s microbiome profile, even in the face of obstacles like data complexity. Multi-omics studies reveal microbial interactions, biomarkers for cancer detection, and gut microbiota’s impact on cancer progression, underscoring the need for further research on microbiome-based cancer prevention and therapy.
... Oral squamous cell carcinoma (OSCC) is a common and impactful form of oral cancer with widespread health consequences globally [75]. Apart from cancer, the COVID-19 pandemic can be mentioned as it endangered the lives of people worldwide [76]. For example, the coronavirus has created numerous challenges for healthcare teams, especially for nurses [77]. ...
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Overview: Oral cancer remains a significant health challenge due to its aggressive nature and limited treatment options. This study investigates the use of niosome nanoparticles to deliver a combination of curcumin and cisplatin, a natural anti-inflammatory and anti-cancer agent and a widely used chemotherapeutic drug, respectively. Methods: Niosome nanoparticles were formulated and optimized for encapsulation efficiency and stability. The physicochemical properties of the nanoparticles were characterized, including particle size, zeta potential, and polydispersity index (PDI). In vitro cytotoxicity assays were conducted using oral cancer cell lines to evaluate the efficacy of the combined treatment. Results: The niosome formulations with a mean particle size of approximately 150 nm, a favorable zeta potential of 24.6 ± 3.2 mV, and a low PDI of 0.23 ± 0.05. The release profile showed a controlled and sustained release of both curcumin and cisplatin over 48 hours, with a cumulative release of 51% for curcumin and 48% for cisplatin. In vitro studies revealed that the combined treatment significantly reduced cell viability compared to individual treatments, with a synergistic effect observed at specific concentrations. Conclusion: The findings suggest that niosome nanoparticles can effectively deliver a combination of curcumin and cisplatin, enhancing the therapeutic potential against oral cancer. This innovative approach may pave the way for more effective treatment strategies, ultimately improving patient outcomes in oral cancer therapy
... One example of drug delivery systems is exosomes, which have emerged as a novel alternative for transporting small molecules [55]. Another solution for addressing disease and its related factors is modeling [56]. In medicine, modeling is also used in brain cancer diagnosis, which has significantly addressed this [57]. ...
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Overview: The targeted administration of anticancer therapies, particularly through folate receptor (FR)-mediated targeting, enhances treatment effectiveness while minimizing side effects. This study assesses the therapeutic potential of folate-targeted liposomal bleomycin (FL-BLM) against traditional forms in treating human ovarian carcinoma and oral cancer. Methods: FL-BLM was created using the thin film hydration technique with folic acid integration for active targeting. Its efficacy was compared to non-targeted liposomal bleomycin (L-BLM) and traditional bleomycin (BLM) using the MTT assay and flow cytometry to measure G2/M phase cell cycle arrest. Results: FL-BLM demonstrated significantly greater effectiveness in reducing cell viability and inducing G2/M phase arrest in oral cancer cells (HN cells, OECM-1) and ovarian cancer cells (A2780CP) compared to L-BLM and BLM, indicating successful folate-mediated targeting. Conclusions: FL-BLM effectively targets and inhibits FR-overexpressing cancer cells, particularly in both cancers. This supports the potential of folate-mediated targeting in liposomal drug delivery systems for improving drug delivery and reducing toxicity. Future research should further explore FR-targeted therapies across various cancer types
... The development of effective cancer treatments is hindered by the limited understanding of the complex interactions between tumor-derived exosomes, such as those enriched with miRNA-211a, and cancer cells, such as B16F10 cells, highlighting the need for innovative drug delivery strategies that can target and modulate these interactions to improve treatment outcomes [67]. The COVID-19 pandemic, a global health crisis that affected millions of lives worldwide, has been successfully contained and overcome through concerted international efforts, advancements in medical technology, and the resilience of humanity [68]. One of the front-line defenses against this challenge were the healthcare workers, particularly nurses, who bore the brunt of the stress and pressure, working tirelessly to care for the infected and save countless lives [69]. ...
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Background: Oral squamous cell carcinoma remains challenging to treat effectively with conventional chemotherapy, leading researchers to explore synergistic combinations to enhance therapeutic outcomes. Combining curcumin with platinum-based drugs, such as cisplatin and carboplatin, has demonstrated potential in enhancing cytotoxic effects. However, limited studies have explored these combinations’ efficacy and sustained release profile in liposomal form specifically for oral cancer. This study investigates the enhanced cytotoxic effects of cisplatin-curcumin and carboplatin-curcumin nanoliposome formulations on CAL 27 oral cancer cells. Methods and Materials: Nanoliposomes encapsulating cisplatin or carboplatin with curcumin were formulated and characterized by particle size, zeta potential, and polydispersity index (PDI) to ensure optimized delivery properties. Particle sizes of the cisplatin and carboplatin nanoliposomes ranged from 175 to 187 nm, with a zeta potential greater than -30 mV, indicating good stability, and PDI values less than 0.48, suggesting uniform particle size distribution. In vitro cytotoxicity was assessed using the MTT assay at 24, 48, and 96 hours across different curcumin concentrations. Results: Cisplatin-curcumin and carboplatin-curcumin nanoliposome formulations demonstrated significantly increased cytotoxicity in CAL 27 cells compared to control groups. Drug release studies indicated a sustained release profile, with approximately 22% of cisplatin and 28% of carboplatin released over 52 hours, which may prolong therapeutic effects by maintaining drug availability within the cancer cells. Conclusion: The findings suggest that cisplatin-curcumin and carboplatin-curcumin nanoliposomal formulations enhance the cytotoxic effects of these chemotherapeutic agents while providing a stable, sustained release profile.
... One example of drug delivery systems is exosomes, which have emerged as a novel alternative for transporting small molecules [55]. Another solution for addressing disease and its related factors is modeling [56]. In medicine, modeling is also used in brain cancer diagnosis, which has significantly addressed this [57]. ...
... The development of effective cancer treatments is hindered by the limited understanding of the complex interactions between tumor-derived exosomes, such as those enriched with miRNA-211a, and cancer cells, such as B16F10 cells, highlighting the need for innovative drug delivery strategies that can target and modulate these interactions to improve treatment outcomes [67]. The COVID-19 pandemic, a global health crisis that affected millions of lives worldwide, has been successfully contained and overcome through concerted international efforts, advancements in medical technology, and the resilience of humanity [68]. One of the front-line defenses against this challenge were the healthcare workers, particularly nurses, who bore the brunt of the stress and pressure, working tirelessly to care for the infected and save countless lives [69]. ...
Article
Full-text available
Background: Oral squamous cell carcinoma remains challenging to treat effectively with conventional chemotherapy, leading researchers to explore synergistic combinations to enhance therapeutic outcomes. Combining curcumin with platinum-based drugs, such as cisplatin and carboplatin, has demonstrated potential in enhancing cytotoxic effects. However, limited studies have explored these combinations’ efficacy and sustained release profile in liposomal form specifically for oral cancer. This study investigates the enhanced cytotoxic effects of cisplatin-curcumin and carboplatin-curcumin nanoliposome formulations on CAL 27 oral cancer cells. Methods and Materials: Nanoliposomes encapsulating cisplatin or carboplatin with curcumin were formulated and characterized by particle size, zeta potential, and polydispersity index (PDI) to ensure optimized delivery properties. Particle sizes of the cisplatin and carboplatin nanoliposomes ranged from 175 to 187 nm, with a zeta potential greater than -30 mV, indicating good stability, and PDI values less than 0.48, suggesting uniform particle size distribution. In vitro cytotoxicity was assessed using the MTT assay at 24, 48, and 96 hours across different curcumin concentrations. Results: Cisplatin-curcumin and carboplatin-curcumin nanoliposome formulations demonstrated significantly increased cytotoxicity in CAL 27 cells compared to control groups. Drug release studies indicated a sustained release profile, with approximately 22% of cisplatin and 28% of carboplatin released over 52 hours, which may prolong therapeutic effects by maintaining drug availability within the cancer cells. Conclusion: The findings suggest that cisplatin-curcumin and carboplatin-curcumin nanoliposomal formulations enhance the cytotoxic effects of these chemotherapeutic agents while providing a stable, sustained release profile.
... In addition to optimizing important factors like battery capacity and motor control strategy, applying GA to the optimization of electric vehicle power systems also allows for full adaptation to the unique requirements of electric vehicles. This application notably enhances vehicle performance, cruising range, and energy efficiency [7,8]. Banani Ardecani et al. (2023) [9] highlighted that accurate estimation of travel time information was one of the fundamental tasks of urban traffic control. ...
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Soft computing technology has attracted extensive attention in computer engineering and automatic control domains because it can deal with uncertainties, fuzziness, and complex practical problems. This study adopts a Genetic Algorithm (GA) in soft computing technology to realize the cooperative optimization of electric vehicle's dynamic and economic performance. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness, and complex practical problems, making GA an effective tool for solving complex optimization problems. Firstly, the electric vehicles' power system structure and energy management strategy are investigated and analyzed. Secondly, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Thirdly, NSGA-II is used to construct the electric vehicles' power and energy configuration, with power and economic performance as the main optimization objectives. Meanwhile, a fuzzy logic controller is designed to adjust the parameters of GA online, so that the optimization process is closer to the actual operating conditions. Finally, the relevant variables are selected to achieve the optimization goal, the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the optimized electric vehicle's acceleration time is remarkably reduced, the dynamic performance is improved by more than 7 %, and the power loss is reduced by 5 %. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. This study provides a new idea and method for the performance optimization of electric vehicles. Moreover, it offers a valuable reference for the innovation and development of electric vehicle technology in the intelligent manufacturing field. This study indicates that electric vehicles could be more efficient, energy-saving, and environmentally friendly to serve people's travel needs in the future.
... Access to electricity serves as a crucial measure of a country's development status. Currently, it is believed that the entire population is connected to the country's electrical grid [15][16][17]. The electrical supply is inconsistent, and peak demand remains unmet. ...
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This research is dedicated to exploring and identifying the most effective design for an energy source tailored specifically to meet the electricity demands of a residential community. In an era where energy efficiency and sustainability are paramount, this study emphasizes the importance of technical and economic considerations in energy sourcing. It posits that any viable solution must not only be efficient in its energy production and consumption but also reliable in its delivery and financially feasible for the residents who will depend on it. To address this multifaceted challenge, the study proposes the innovative use of a rotation-invariant coordinate convolutional neural network in conjunction with binary battle royale optimization techniques. These advanced methodologies are selected for their potential to enhance the modelling and optimization processes involved in energy source design. The primary goal of employing these methods is to minimize two critical factors: the net present cost of the energy system and the overall energy cost incurred by the residents. By focusing on these objectives, the research aims to ensure that the proposed energy solutions are not only cost-effective but also sustainable over the long term. To rigorously test the proposed model and evaluate its performance, the research is conducted using the MATLAB platform. The study employs established methodologies and performance metrics to assess the outcomes of the model, ensuring that the findings are both credible and applicable to real-world scenarios. Through comprehensive testing and detailed analysis, this research aims to provide significant insights and actionable recommendations for the optimal design of energy sources in residential areas. By contributing to the ongoing discourse on sustainable energy solutions, the study seeks to inform policymakers, energy planners, and community stakeholders about effective strategies for meeting residential energy demands while promoting environmental sustainability. Ultimately, the findings of this research could play a crucial role in shaping the future of energy sourcing in residential communities, paving the way for more resilient and sustainable energy systems.
... The revised McDonald criteria stipulate that the identification of two or more OCBs in the CSF serves as a key parameter for the diagnosis of MS (10). Nonetheless, this technique has several constraints that include susceptibility to subjective interpretation, being costprohibitive, possessing qualitative rather than quantitative characteristics, as well as requiring considerable labor investments (11)(12)(13). Moreover, the specificity of OCB is low, since the intrathecal synthesis of IgG bands occurs in many other neuroinflammatory diseases not just in MS (14). ...
... Accordingly, plug-in empirical likelihood (PEL) can be used to more accurately estimate parameters in models that are sensitive to data distribution [25]. Machine learning can be used in a wide variety of applications, including digital social entrepreneurship [26], digital devices and online accounts [27], digital password design [28], additive manufacturing [29], numerical model design [30], smart car design [31], object detection [32], classification, routing location [33], prediction [34], Baffle-Enhanced Scour Mitigation [35], Financial markets [36], image processing [37,38] and etc. ...
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Emotion is a complex state caused by the functioning of the human brain in relation to various events, for which there is no scientific definition. Emotion recognition is traditionally conducted by psychologists and experts based on facial expressions.—the traditional way to recognize something limited and is associated with errors. This study presents a new automatic method using electroencephalogram (EEG) signals based on combining graph theory with convolutional networks for emotion recognition. In the proposed model, firstly, a comprehensive database based on musical stimuli is provided to induce two and three emotional classes, including positive, negative, and neutral emotions. Generative adversarial networks (GANs) are used to supplement the recorded data, which are then input into the suggested deep network for feature extraction and classification. The suggested deep network can extract the dynamic information from the EEG data in an optimal manner and has 4 GConv layers. The accuracy of the categorization for two classes and three classes, respectively, is 99% and 98%, according to the suggested strategy. The suggested model has been compared with recent research and algorithms and has provided promising results. The proposed method can be used to complete the brain-computer-interface (BCI) systems puzzle.
... A previous study revealed that plasma NT1 tau is a specific marker of AD, which is elevated early in the disease and may prove useful as a first-round screen to identify individuals at risk of developing AD (32,35). COVID-19 has accelerated AD research, revealing potential connections between the virus and neurodegeneration (36). Studies indicate that COVID-19-induced inflammation and vascular damage may exacerbate Alzheimer's pathology, necessitating urgent exploration of therapeutic strategies targeting both conditions for improved patient outcomes. ...
... Antioxidants help mitigate oxidative stress in Alzheimer's disease, while marital satisfaction provides essential emotional support for affected individuals (33,34). The COVID-19 pandemic has significantly influenced AD research, highlighting potential links between viral infections and neurodegenerative processes (35). Emerging evidence suggests that COVID-19 may exacerbate Alzheimer's symptoms through inflammatory responses and vascular damage, prompting critical investigations into long-term neurological impacts. ...
... Regulatory agencies, such as the FDA and EMA, require extensive evidence of efficacy, safety, and quality before approving new treatments. Researchers and developers must ensure that clinical trial designs meet regulatory standards and that all data are robust and reproducible (108). Additionally, commercial considerations, including patent protection, manufacturing scalability, and market acceptance, are crucial for the successful translation of fisetin into a widely available therapeutic option. ...
... 1. Define Objectives: Establish selection criteria based on the strategic objectives of the organization [19,20]. ...
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In the construction industry, Project Portfolio Selection (PPS) is crucial for enhancing resource allocation, maximizing returns, and minimizing risks. This paper presents an in-depth analysis of PPS using optimization techniques. By leveraging mathematical models and decision-making frameworks, we emphasize the significance of optimization in achieving strategic objectives. The study reviews existing literature, identifies key factors influencing project selection, and proposes a comprehensive methodology that integrates quantitative and qualitative criteria. Numerical simulations demonstrate the effectiveness of the proposed approach. The findings indicate that utilizing optimization can significantly improve project outcomes and align them with organizational goals.
... Furthermore, implementing robust optimization models is essential for managing uncertainties and ensuring the continuity of supply chains under crisis conditions. For example, Mahdavimanshadi et al. [104] developed a multistage stochastic optimization model tailored to enhance the resilience of pharmaceutical supply chains during the pandemic, emphasizing patient group priority. Soyster [105] first addressed uncertainty within a convex set for linear optimization [105]. ...
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In an era where sustainability and efficient resource utilization are paramount, the closed-loop supply chain (CLSC) emerges as a critical approach, particularly in the context of perishable goods. The perishability of products adds a layer of complexity to supply chain management, necessitating innovative strategies for maximizing product life and minimizing waste. This comprehensive review article delves into the integration of perishable products within the framework of CLSC. The study thoroughly examines existing research to identify gaps and outline future research directions. It emphasizes the unique challenges and complexities of managing perishable goods, a crucial but often overlooked component in sustainable supply chain practices. The review highlights the balance between efficiency and sustainability, underscoring the importance of reverse logistics and circular economy principles in enhancing supply chain resilience. By synthesizing various methodologies and findings, the article presents a holistic view of the current state of perishable product management in CLSCs, offering valuable insights for academia and industry practitioners. The study not only contributes to the theoretical understanding of CLSCs, but also proposes practical approaches for their optimization, aligning with broader sustainability goals.
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This research paper presents a comprehensive mathematical model for designing resilient supply chain networks under uncertainty. The model incorporates various factors that contribute to supply chain resilience, such as demand variability, disruptions, and supply chain complexity. By considering these elements, the model aims to optimize the network structure and decision-making processes to enhance its ability to withstand shocks and maintain operations during challenging times. The study begins with a thorough literature review that examines existing research on supply chain resilience and mathematical modeling techniques. Subsequently, a novel mathematical model is developed, incorporating key resilience metrics and decision variables. The model is then solved using advanced optimization algorithms to determine optimal network configurations. Numerical experiments are conducted to evaluate the performance of the proposed model. Different scenarios are simulated to assess the model's sensitivity to various uncertainties and its effectiveness in mitigating risks. The results demonstrate the model's capability to design resilient supply chains that can adapt to changing conditions and minimize disruptions. Finally, the paper concludes by summarizing the essential findings and contributions of the research. The limitations of the study are also discussed, along with potential avenues for future research.
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Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production-distribution-inventory-allocation-location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.
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