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Background: In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches. Methods: This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. Results: The application of these ML models resulted in significant improvements across key operational areas. We achieved a 15% increase in demand forecasting accuracy, a 10% reduction in overstock and stockouts, and a 95% accuracy in predicting order fulfillment timelines. Additionally, the approach identified at-risk shipments and enabled customer segmentation based on delivery preferences, leading to more personalized service offerings. Conclusions: Our evaluation demonstrates the transforma-tive potential of ML in making supply chain operations more responsive and data-driven. The study underscores the importance of adopting advanced technologies to enhance deci-sion-making, evidenced by a 12% improvement in lead time efficiency, a silhouette coefficient of 0.75 for clustering, and an 8% reduction in replenishment errors.

Optimizing transportation routes to improve delivery efficiency and resource utilization in dynamic supply chain scenarios is a challenging task. Traditional route optimization methods often struggle with complex supply chain network structures and dynamic changes, which require a more efficient and flexible solution. This study proposes a method that integrates Graph Neural Networks (GNNs), self-attention mechanisms, and meta-reinforcement learning (Meta-RL) in order to address route optimization in supply chains. The goal is to develop a path planning method that excels in both static and dynamic environments. First, GNNs model the supply chain network, converting node and edge features into high-dimensional graph representations in order to capture local and global network information. Next, a Transformer-based strategy network captures global dependencies, optimizing path planning. Finally, Meta-RL enables rapid strategy adaptation to dynamic changes (e.g., new demand points or route disruptions) with minimal sample support. Experiments on multiple supply chain datasets show that our method improves path planning quality by about 7%, compared to traditional methods, achieving a path coverage of 92.29%. Ablation studies reveal that the on-time delivery rate improves by nearly 30% over the baseline model. These results demonstrate that the proposed method not only optimizes routes but also significantly enhances the overall efficiency and robustness of supply chain networks. This research provides an efficient route optimization framework applicable to complex supply chain management and other scheduling fields, offering new insights and technical solutions for future research and applications.

Food computing refers to the integration of digital technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and data-driven approaches, to address various challenges in the food sector. It encompasses a wide range of technologies that improve the efficiency, safety, and sustainability of food systems, from production to consumption. It represents a transformative approach to addressing challenges in the food sector by integrating AI, the IoT, and data-driven methodologies. Unlike traditional food systems, which primarily focus on production and safety, food computing leverages AI for intelligent decision making and the IoT for real-time monitoring, enabling significant advancements in areas such as supply chain optimization, food safety, and personalized nutrition. This review highlights AI applications, including computer vision for food recognition and quality assessment, Natural Language Processing for recipe analysis, and predictive modeling for dietary recommendations. Simultaneously, the IoT enhances transparency and efficiency through real-time monitoring, data collection, and device connectivity. The convergence of these technologies relies on diverse data sources, such as images, nutritional databases, and user-generated logs, which are critical to enabling traceability and tailored solutions. Despite its potential, food computing faces challenges, including data heterogeneity, privacy concerns, scalability issues, and regulatory constraints. To address these, this paper explores solutions like federated learning for secure on-device data processing and blockchain for transparent traceability. Emerging trends, such as edge AI for real-time analytics and sustainable practices powered by AI–IoT integration, are also discussed. This review offers actionable insights to advance the food sector through innovative and ethical technological frameworks.

Energy storage is a foundational clean energy technology that can enable transformative technologies and lower carbon emissions, especially when paired with renewable energy. However, clean energy transition technologies need completely different supply chains than our current fuel-based supply chains. These technologies will instead require a material-based supply chain that extracts and processes massive amounts of minerals, especially critical minerals, which are classified by how essential they are for the modern economy. In order to develop, operate, and optimize the new material-based supply chain, new decision-making frameworks and tools are needed to design and navigate this new supply chain and ensure we have the materials we need to build the energy system of tomorrow. This work creates a flexible mathematical optimization framework for critical mineral supply chain analysis that, once provided with exogenously supplied projections for parameters such as demand, cost, and carbon intensity, can provide an efficient analysis of a mineral or critical mineral supply chain. To illustrate the capability of the framework, this work also conducts a case study investigating the global lithium supply chain needed for energy storage technologies like electric vehicles (EVs). The case study model explores the investment and operational decisions that a global central planner would consider in order to meet projected lithium demand in one scenario where the objective is to minimize cost and another scenario where the objective is to minimize CO2 emissions. The case study shows there is a 6% cost premium to reduce CO2 emissions by 2%. Furthermore, the CO2 Objective scenario invested in recycling capacity to reduce emissions, while the Cost Objective scenario did not. Lastly, this case study shows that even with a deterministic model and a global central planner, asset utilization is not perfect, and there is a substantial tradeoff between cost and emissions. Therefore, this framework—when expanded to less-idealized scenarios, like those focused on individual countries or regions or scenarios that optimize other important evaluation metrics—would yield even more impactful insights. However, even in its simplest form, as presented in this work, the framework illustrates its power to model, optimize, and illustrate the material-based supply chains needed for the clean energy technologies of tomorrow.

This work investigates a service-oriented manufacturing supply chain in which a manufacturer and an operator make decisions about equipment quality and maintenance service. Both the manufacturer and the operator have to make tradeoffs between equipment quality and maintenance service to maximize their own profit, which can lead to supply chain conflict. Decision models under decentralized decisions are formulated first for the manufacturer and the operator to make their respective independent optimal decisions, and a decision model under centralized decisions is formulated to obtain optimal decisions for the supply chain. The results show that channel coordination is not achievable and an agreement cannot be reached with decentralized decisions. To address this issue, two, i.e., a cost-sharing and a performance-based, strategies are introduced to coordinate the supply chain. The results reveal that the manufacturer and the operator are motivated to find the optimal decisions to maximize the profit of the supply chain when the subsidy rate or the penalty rate is equal to the profit margin of the operator. The models and the coordination strategies are extended to the situation considering the learning behavior of the manufacturer. The results show that the learning behavior impacts the profit of the supply chain with coordination and the preferences of the coordination strategy in the supply chain.

Industrial 4.0 (I4.0) is believed to revolutionize supply chain (SC) management and the articles in this domain have experienced remarkable increments in recent years. However, the existing insights are scattered over different sub-topics and most of the existing review papers have ignored the underground decision-making process using OR methods. This paper aims to depict the current state of the art of the articles on SC optimization in I4.0 and identify the frontiers and limitations as well as the promising research avenue in this arena. In this study, the systematic literature review methodology combined with the content analysis is adopted to survey the literature between 2013 and 2022. It contributes to the literature by identifying the four OR innovations to typify the recent advances in SC optimization: new modeling conditions, new inputs, new decisions, and new algorithms. Furthermore, we recommend four promising research avenues in this interplay: (1) incorporating new decisions relevant to data-enabled SC decisions, (2) developing data-enabled modeling approaches, (3) preprocessing parameters, and (4) developing data-enabled algorithms. Scholars can take this investigation as a means to ignite collaborative research that tackles the emerging problems in business, whereas practitioners can glean a better understanding of how to employ their OR experts to support digital SC decision-making.

This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amount of pollutants, and the number of job opportunities created in the proposed supply chain network under the uncertainty of cost, supply, and demand. In addition, to deal with uncertainty, fuzzy logic is used. Moreover, four multi-objective metaheuristic algorithms are employed to solve the model, which include a novel multi-objective version of the recently proposed metaheuristic algorithm known as Multi-Objective Reptile Search Optimizer (MORSO), Multi-Objective Simulated Annealing (MOSA), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Grey Wolf (MOGWO). All the algorithms are evaluated using LP-metric in small sizes and their results and performance are compared based on criteria such as Max Spread (MS), Spread of Non-Dominance Solution (SNS), the number of Pareto solutions (NPS), Mean Ideal Distance (MID), and CPU time. In addition, to achieve better results, the parameters of all algorithms are tuned by the Taguchi method. The programmed model is implemented using a real case study in Iran to confirm its accuracy and efficiency. To further evaluate the current model, some key parameters are subject to sensitivity analysis. Empirical results indicate that MORSO performed very well and by constructing solar panel sites and producing energy out of rice waste up to 19% of electricity can be saved.

Expansion of immunization coverage is dependent in part on delivering potent vaccines in an equitable and timely manner to immunization outreach session sites from Cold Chain Points (CCPs). When duration of travel between the last CCP and the session site (Time-to-Supply) is too long, three consequences may arise: decreased potency due to exposure to heat and freezing, beneficiary dropouts due to delayed session starts, and, increased operational costs for the Health Facility (HF) conducting the outreach sessions. Guided by the Government of India’s recommendation on cold chain point expansion to ensure that all session sites are within a maximum of 60 min from the last CCP, CHAI and the State Routine Immunization Cell in the state of Madhya Pradesh collaborated to pilot a novel approach to cold chain network optimization and expansion in eight districts of Madhya Pradesh. Opportunities for realignment of remote sub-health centers (SHCs) and corresponding session sites to alternative existing CCPs or to HFs which could be converted to new CCPs were identified, and proposed using a greedy adding algorithm-based optimization which relied on health facility level geo-location data. Health facility geo-coordinates were collected through tele-calling and site visits, and a Microsoft Excel based optimization tool was developed. This exercise led to an estimated reduction in the number of remote SHCs falling beyond the permissible travel time from CCPs by 56.89 percent (132 remote sites), from 232 to 100. The 132 resolved sites include 73 sites realigned to existing CCPs, and 59 sites to be attached to 22 newly proposed CCPs. Both the network optimization approach and the institutional capacity built during this project will continue to be useful to India’s immunization program. The approach is replicable and may be leveraged by developing countries facing similar challenges due to geographical, institutional, and financial constraints.

The topic of digital transformation in supply chain optimization has garnered considerable attention in recent years due to its importance. The purpose of the study was to offer empirical evidence and insights into the advantages and obstacles linked with digital transformation in supply chain management. To investigate the effects of digital transformation on supply chain optimization, the research employs a hybrid methodology and comprehensive approach that includes a thorough literature review, the creation of a theoretical framework, and the presentation of empirical finings through various case studies using the predefined selection criteria. The case analyses highlight crucial elements that support effective digital transformations, including real-time data analytics, teamwork, blockchain technology, digital twin augmented and virtual reality and collaborative robots. The practical implications from the findings of this study, proffers insights that can be extremely helpful for professionals in various industrial sectors and businesses planning similar digital transformation journeys. This empirical study with regards to the implication of Digital transformation 5.0 on supply chain management is novel to the body of literature. It is however necessary to conduct more study to confirm the results, apply them to a wider range of businesses, and investigate different aspects of digital transformation in supply chain optimization.