Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, and robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs of efficient order processing and system stability. This study focuses on three key stages of RMFSs: order allocation and sorting, shelf selection, and coordinated robot scheduling. For each stage, mathematical models are established and the corresponding solutions are proposed. Unlike traditional methods, DRL technology is introduced to solve these problems, utilizing a Genetic Algorithm and Ant Colony Optimization to handle decision making related to large-scale orders. Through simulation experiments, performance indicators-such as shelf access frequency and the total processing time of the RMFS-are evaluated. The experimental results demonstrate that, compared to traditional methods, our algorithms excel in handling large-scale orders, showcasing exceptional superiority, capable of completing approximately 110 tasks within an hour. Future research should focus on integrated decision-making modeling for each stage of RMFSs and designing efficient heuristic algorithms for large-scale problems, to further enhance system performance and efficiency.

UNASSIGNED: Effective supply chain management (SCM) of point-of-care (POC) tests for diseases like severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) requires active participation from diverse stakeholders, government entities, and regulatory bodies. The responsibility for overseeing various aspects of POC tests, including procurement, quality assurance, storage, inventory management, distribution, and human resource capacity, lies with national, provincial, and local levels of government. This study aimed to collaboratively develop an innovative approach to enhance SCM for SARS-CoV-2 POC diagnostic services in resource-limited settings, using the Mopani District in Limpopo province, South Africa, as a case study.
UNASSIGNED: Key stakeholders were invited to participate in an online workshop using purposive sampling. The study employed the nominal group technique (NGT) for data collection, which consisted of two phases. Phase 1 focused on identifying barriers in the supply chain of COVID-19 rapid tests, while phase 2 aimed to devise strategies to overcome the priority barriers identified in phase 1. Participants used a Likert scale of 1-5 to rank barriers and strategies, and an overall ranking score was calculated for each. The participants were provided with the results of the ranking exercise for their feedback.
UNASSIGNED: Eleven key stakeholders from national (n = 1), provincial (n = 4), and local government (n = 2) levels, research entities (n = 3), and non-governmental organizations (n = 1) took part in the study. Participants identified significant barriers in the supply chain, such as the availability of testing kits, unknown demand, information on SCM during a pandemic, methods of controlling stock, and procurement processes. Strategies suggested by key stakeholders included monitoring stock levels and optimizing stock visibility systems to improve test availability, enhancing information visibility and consistent data updates to address unknown demand and improve SCM during a pandemic, employing data capturing and digitization for effective stock control, and implementing demand planning and standardized procurement processes at the national level to enhance stock procurement.
UNASSIGNED: The successful collaboration with key stakeholders, facilitated by the NGT, resulted in the co-creation of a novel approach to enhance SCM for COVID-19 diagnostic services in resource-limited settings. This study holds the potential to support the provision of COVID-19 diagnostic services in such settings. A recommended follow-up study would assess the feasibility of implementing this approach.

In the context of sustainable development, market competition is intensifying, and financial constraints have emerged as a significant hindrance to green project investment. Green Supply Chain Finance (GSCF), characterized by long-term collaboration, has emerged as a crucial financial approach to mitigate corporate financial limitations and channel capital flows into environmentally friendly industries. We propose a two-echelon supply chain with one supplier and two competing retailers over a single period and investigate ordering, sales, and financing decisions simultaneously under competition. Retailers constrained by financial considerations may secure GSCF or traditional bank financing (BF) loans. This study investigates the influence of competition on pricing and sales strategies during the selling season. The results demonstrate that retailers select between clearance and responsive selling strategies based on the level of market competition. During the ordering season, retailers share the product market equally when interest rates are uniform, and the supplier formulates a supply chain contract while considering the financing interest rate. In the presence of differential interest rates, retailers may not always opt for the GSCF, even when they offer an interest rate advantage, due to the comprehensive impacts of operational and financial strategies. Remarkably, competitive retailers do not choose the GSCF when their initial green investment capital surpasses a certain threshold.

Avocado production is mostly confined to tropical and subtropical regions, leading to lengthy distribution channels that, coupled with their unpredictable post-harvest behavior, render avocados susceptible to significant loss and waste. To enhance the monitoring of \'Hass\' avocado ripening, a data-driven tool was developed using a deep learning approach. This study involved monitoring 478 avocados stored in three distinct storage environments, using a 5-stage Ripening Index to classify each fruit\'s ripening phase based on their shared characteristics. These categories were paired with daily photographic records of the avocados, resulting in a database of labeled images. Two convolutional neural network models, AlexNet and ResNet-18, were trained using transfer learning techniques to identify distinct ripening indicators, enabling the prediction of ripening stages and shelf-life estimations for new unseen data. The approach achieved a final prediction accuracy of 88.8% for the ripening assessment, with 96.7% of predictions deviating by no more than half a stage from their actual classifications when considering the best side of the samples. The average shelf-life estimates based on the attributed classifications were within 0.92 days of the actual shelf-life, whereas the predictions made by the models had an average deviation of 0.96 days from the actual shelf-life.

In the context of regular epidemic prevention and control, this paper considers a two-stage tourism supply chain consisting of a scenic spot that attracts tourists through advertising and a travel agency that invests in service improvement and epidemic prevention. By establishing theoretical game models of a tourism supply chain, we investigate how the service level and advertising level can affect the retail price, product service level, and profits of the supply chain. The results show that the service level of travel agencies could improve consumers\' preferences, expand the market demand for tourism products, and improve the efficiency of the supply chain to achieve a win-win situation and increase the profits of the scenic spot and the travel agency. The retailer price, service level, promotion level, and supply chain profit all increase as the service coefficient and advertising coefficient increase, and the speed of the increase is higher for the centralized model than for other models. Some valuable information could be provided for supply chain enterprises to develop collaborative strategies and promote tourism supply chain management practices.

This comprehensive bibliometric study investigates Sustainable Supply Chain Innovation (SSCI) research, examining its evolution, identifying key contributors, and unveiling emerging trends. Analyzing 1158 English-language SSCI articles using the robust Scopus dataset exposes noteworthy journals, authors, institutions, and global contributions. The findings suggest a consistent increase in research output since 1999, with a notable surge in the past decade. Network analysis and density-based spatial clustering identified six SSCI research clusters: Sustainability and Responsibility in Business, Navigating Innovation and Disruption, Sustainable Business Strategies, Environmental Sustainability and Innovation, Sustainable Food Systems and Environmental Impact, and Sustainable Business Dynamics. These clusters highlight the diverse nature of the evolving Sustainability and Supply Chain Management (SCM) field, contributing to a thorough understanding of the SSCI research landscape and emphasizing interconnections between sustainability and SCM themes, potentially leading to more comprehensive theoretical models. Furthermore, this understanding aids businesses in anticipating emerging trends and implementing optimal practices in SSCI. Moreover, recognizing active institutions and global contributors provides practical insights for fostering strategic collaborations.

Outsourcing is an essential tool in supply management. This study examines the current outsourcing application areas of the public health sector in Türkiye. Application examples are presented, together with the prominent theories in outsourcing (Transaction Cost Economics Theory (TCE), Core Competencies Theory (CCT), Agency Theory (AT), Resource-Based View Theory (RBVT), Relational Theory (RT), and Social Change Theory (SCT), Turkish public procurement legislation, Ministry of Health outsourcing practices, and public hospitals are discussed for review and example. The examination shows that foreign dependency on the public health sector is increasing, and more than one theory is applied in some procurement processes. As a result of this study, examining and comparing which theories come to the fore in outsourcing in other countries is recommended.

Background Case studies have highlighted labour rights abuse in the manufacture of several healthcare products, but little is known about the scale of the problem or the specific products involved. We aimed to quantify and compare the overall and product-specific risks of labour rights abuse in the manufacture of healthcare products supplied to high-income settings using multiple datasets on the product country of origin (COO). Methods Public procurement data from South-Eastern Norway (n=23,972 products) were compared to datasets from three other high-income settings: procurement data from Cambridge University Hospitals, trade data from UN Comtrade, and registry data from the US Food and Drug Administration (FDA). In each dataset, the product COO was matched to the International Trade Union Confederation risk rating for labour abuse and deemed high-risk when rated 4, 5, or 5+. Results In the Norway data, 55.4% of products by value had a COO declared, 49.1% of which mapped as high-risk of labour rights abuses. COO was identified for 70/100 products in the Cambridge data, with COO matching high-risk at 59.9% by value. The level of risk for specific medical product categories varied between the Norway, US FDA, and UN Comtrade datasets, but those with higher proportional risk included medical/surgical gloves and electrosurgical products. Conclusion Evidence of high-risk of labour rights abuse in the manufacture of healthcare products present in these data indicates a likely high level of risk across the sector. There is an urgent need for global legislative and political reform, with a particular focus on supply chain transparency as a key mechanism for tackling this issue.

In the current global context of environmental degradation and resource constraints, the pursuit of sustainable development has become an imperative. One avenue that holds promise for achieving this objective is the application of digital technologies, which have the potential to decouple economic growth from its carbon footprint. However, it is crucial to ensure that these technologies are designed and governed in a prudent manner, with a strong alignment to environmental priorities. This study focuses on exploring the potential roles of blockchain and artificial intelligence (AI) in supply chain coordination and impact mitigation. Furthermore, they have the capacity to incentivize recycling and circular business models, as well as facilitate carbon accounting and offsetting. To fully realize these benefits, it is essential to deploy these technologies within inclusive collaborative frameworks that take into consideration social and ecological considerations. The study also offers policy recommendations that highlight key leverage points for digital innovation, enabling countries to embark on smart and green industrial transformation pathways. By harnessing the potential of blockchain and AI in supply chains, governments can promote transparency, traceability, and accountability, thereby fostering sustainable practices and reducing environmental impacts. Incorporating blockchain and AI technologies into supply chain approaches leads to a substantial improvement in efficiency, as demonstrated by a numerical analysis. In conclusion, the integration of innovative digital technologies offers significant opportunities to optimize production systems and economic activity while prioritizing sustainability objectives for the betterment of society and the environment. These technologies have the potential to mitigate environmental externalities by addressing information imbalances within global supply chains. However, it is essential to prioritize inclusive governance that emphasizes democratic participation to mitigate any unintended negative consequences, especially for vulnerable communities. By ensuring inclusive decision-making processes, we can maximize the positive impact of these technologies while minimizing potential harm.

This research was conducted on industrial agriculture in Indonesia. Risk analysis was carried out based on previous research. One source of risk was obtained, namely raw materials that did not meet specifications, which was then proposed to be mitigated by evaluating supplier performance. This activity involves a lot of data, requiring efficient and effective data storage and access. The level in the simulation layout includes analysing system needs, using problem diagrams, compiling activity diagrams, deciding subprocesses, and filtering information. The analysis is carried out by comparing the use of supply chains with Blockchain and without Blockchain, which is then obtained to determine whether there is an increase. A sequentially stored data scenario describes a situation when the transaction process is in progress and is stored sequentially according to the process that occurs. Storing data in groups explains a problem when a transaction has been completed and stored in groups with similar data, making it easier to track specific data. In this regard, a simulation will be carried out using a website, namely a blockchain demo. The design stage starts with identifying system requirements, creating use case diagrams, compiling activity diagrams, determining subprocesses, and selecting information. The simulation results obtained will be analysed to determine the feasibility of Blockchain as a means of supporting risk mitigation related to data using aspects, including security, trust, traceability, sustainability, and costs.