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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. [ABSTRACT FROM AUTHOR]

Food waste is a serious worldwide issue that has an impact on the environment, society, and economy. This comprehensive review provides a detailed description of methods and approaches for reducing food waste, emphasizing the necessity of comprehensive strategies to tackle its intricate relationship with environmental sustainability, social equity, and economic prosperity. By scrutinizing the extent and impact of food waste, from initial production stages to final disposal, this comprehensive review underlines the urgent need for integrated solutions that include technological advancements, behavioral interventions, regulatory frameworks, and collaborative endeavors. Environmental assessments highlight the significant contribution of food waste to greenhouse gas emissions, land degradation, water scarcity, and energy inefficiency, thereby emphasizing the importance of curtailing its environmental impact. Concurrently, the social and economic consequences of food waste, such as food insecurity, economic losses, and disparities in food access, underscore the imperative for coordinated action across multiple sectors. Food waste can also be effectively reduced by various innovative approaches, such as technological waste reduction solutions, supply chain optimization strategies, consumer behavior‐focused initiatives, and waste recovery and recycling techniques. Furthermore, in order to foster an environment that encourages the reduction of food waste and facilitates the transition to a circular economy, legislative changes and regulatory actions are essential. By embracing these multifaceted strategies and approaches, stakeholders can unite to confront the global food waste crisis, thereby fostering resilience, sustainability, and social equity within our food systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 CO 2 emissions. The case study shows there is a 6% cost premium to reduce CO 2 emissions by 2%. Furthermore, the CO 2 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. [ABSTRACT FROM AUTHOR]

Nowadays, Energy has become one of the most important issues. Negative environmental effects of fossil fuels lead countries to use renewable and sustainable energy sources day by day. In addition, energy supply and security have been an motivator factor in this field. This study focus on biomass that renewable and sustainable energy source. According to studies in literature, energy production from biomass resource less than other energy sources. The most important reason for this is the logistics costs. Therefore, biomass supply chain optimization is an important issue. In this study, mathematical models of biomass supply chain are reviewed. When the studies are evaluated, there are three approaches in modeling biomass supply chains. These approaches are as follows: 1) Collection and Distirubiton, 2) Selection, 3) Clustering. In additon, researchers generally focus on single-aim mixed integer mathematical models. However, recently, it is seen that there are multi-aim models in the literature for minimizing emissions as well as supply chain costs. In this study, general information about biomass and biomass supply chain, studies of in this area, mathematical models, details of published papers are given. Objective functions, cost/income items, methods of these models are discussed in detail. [ABSTRACT FROM AUTHOR]

Implementing reverse logistics in the construction industry is considered a crucial method to achieve a circular economy. Despite a wealth of research focusing on improving reverse logistics systems, businesses still encounter challenges during the implementation process. Therefore, this study conducted a systematic literature review utilizing bibliometric methods to analyze 623 articles on reverse logistics in the construction industry published on Web of Science from 1995 to 2023. Additionally, a comprehensive review of 56 high-quality literature on obstacles to implementing reverse logistics in the construction industry and optimizing reverse supply chains was conducted. This review uncovered the current status and challenges of implementing reverse logistics in the construction industry and proposed potential solutions to address these issues. The main findings of this study include: (1) increasing academic interest in construction waste reverse logistics, with Chinese scholars leading the way and publications predominantly in environmental and construction journals, with limited coverage in logistics journals; (2) the primary obstacles to implementing reverse logistics in the construction industry lie in supply chain management, such as lacking deconstruction designs, incomplete recycling markets, difficulties in evaluating the quality of secondary materials, and insufficient supply chain integration; (3) proposing a framework for a construction industry reverse logistics supply chain ecosystem, aiming to establish a platform to facilitate online collection of construction waste, online transactions of secondary materials, end-to-end monitoring, and data analytics for consultation. [ABSTRACT FROM AUTHOR]

Green supply chain management is critical for driving sustainable development and addressing escalating environmental challenges faced by companies. However, due to the multidimensionality of cost–benefit analysis and the intricacies of supply chain operations, strategic decision-making regarding green supply chains is inherently complex. This paper proposes a green supply chain optimization framework based on a two-stage heuristic algorithm. First, anchored in the interests of intermediary core enterprises, this work integrates upstream procurement and transportation of products with downstream logistics and distribution. In this aspect, a three-tier green complex supply chain model incorporating economic and environmental factors is developed to consider carbon emissions, product non-conformance rates, delay rates, and transportation costs. The overarching goal is to comprehensively optimize the trade-off between supply chain costs and carbon emissions. Subsequently, a two-stage heuristic algorithm is devised to solve the model by combining the cuckoo search algorithm with the brainstorming optimization algorithm. Specifically, an adaptive crossover–mutation operator is introduced to enhance the search performance of the brainstorming optimization algorithm, which caters to both global and local search perspectives. Experimental results and comparison studies demonstrate that the proposed method performs well within the modeling and optimization of the green supply chain. The proposed method facilitates the efficient determination of ordering strategies and transportation plans within tight deadlines, thereby offering valuable support to decision-makers in central enterprises for supply chain management, ultimately maximizing their benefits. [ABSTRACT FROM AUTHOR]

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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.

In order to establish a green, low-carbon circular development economic system, imperative goals include achieving carbon peaking and carbon neutrality. This research delves into the resource utilization of city-scale demolition waste (C&DW), aligning with environmental protection needs and sustainable development principles. The paper introduces a unique closed-loop supply chain (CLSC) model tailored for C&DW and employs a distinctive mixed integer nonlinear programming (MINLP) model for optimization. Guangzhou serves as a case study for thorough analysis, verification, and practical application of the proposed model, especially under diverse scenarios of carbon price (CP) and carbon trading (CT) policies. The key conclusions drawn from this study include the following: (1) The cost of carbon emissions is intricately influenced by both carbon emissions and carbon price, with the latter effectively regulating the carbon emissions during C&DW recycling. (2) The implementation of a CT policy, with a fixed carbon price, contributes to a further reduction in the cost of C&DW recycling treatment. (3) Under equivalent conditions, the CT policy demonstrates the potential to decrease costs and enhance the economic benefits within the building environmental protection product market. The research outcomes not only contribute to the advancement of management theory in the C&DW recycling supply chain (SC) but also provide a robust theoretical foundation for governmental initiatives aimed at introducing effective C&DW recycling management policies. [ABSTRACT FROM AUTHOR]

Cap-and-trade models have been largely studied in the literature when it comes to reducing emissions in a supply chain. In this paper, further pursuing the goal of analyzing the effectiveness of cap-and-trade strategies in reducing emissions in supply chains, we propose a mathematical model for sustainable supply chain management. This optimization program aims at reducing emissions and supply chain costs in an unregulated scenario w.r.t. the cap definition, i.e., trading CO2 is allowed but no formal limit on the CO2 emissions is imposed. Also, we considered an initial budget for technological investments by the facilities in the considered supply chain, allowing plants to reduce their unit production emissions at a different unit production cost. For this model, differently from what exists in the literature, we derive some theoretical conditions guaranteeing that, if obeyed, the emissions over time have a non-increasing trend meaning that decreasing caps over time can be attained with a self-regulated scenario. Computational results show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This work considers the incorporation of renewable ammonia manufacturing sites into existing ammonia supply chain networks while accounting for ammonia price uncertainty from existing producers. We propose a two-stage stochastic programming approach to determine the optimal investment decisions such that the ammonia demand is satisfied and the net present cost is minimized. We apply the proposed approach to a case study considering deploying in-state renewable ammonia manufacturing in Minnesota's supply chain network. We find that accounting for price uncertainty leads to supply chains with more ammonia demand met via renewable production and thus lower costs from importing ammonia from existing producers. These results show that the in-state renewable production of ammonia can act as a hedge against the volatility of the conventional ammonia market. [ABSTRACT FROM AUTHOR]

Green Supply Chain Management requires coordinated decisions between the strategic and operational organization layers to address strict green goals. Furthermore, linking CO2 emissions to supply chain operations is not always easy. This study proposes a new mathematical model to minimize CO2 emissions in a three-layered supply chain. The model foresees using a financial budget to mitigate emissions contributions and optimize supply chain operations planning. The three-stage supply chain analyzed has inbound logistics and handling operations at the intermediate level. We assume that these operations contribute to emissions quadratically. The resulting bilevel programming problem is solved by transforming it into a nonlinear mixed-integer program by applying the Karush-Kuhn-Tucker conditions. We show, on different sets of synthetic data and on a case study, how our proposal produces solutions with a different flow of goods than a modified linear model version. This results in lower CO2 emissions and more efficient budget expenditure.

Purpose: This article introduces an innovative method designed to optimize distribution strategies with respect to future uncertainty. It goes beyond the limitations of traditional scenario-based planning that often leads to suboptimal strategies due to the unpredictability of future developments and the challenge of accurately assigning probabilities to these scenarios. Consequently, the method allows selection of the most economically viable future strategy. Methodology: Our methodology diverges from conventional approaches by refraining from making rigid assumptions about the probabilities of future scenarios. Instead, it comprehensively explores the entire allowable probability space to identify an optimal strategy that works well in possible future developments. We employed this method in the case study of a real-world company based in Czechia, where we devised three viable distribution strategies and four model development scenarios. Results: The application of our method demonstrated its effectiveness in selecting the most advantageous strategy, as evidenced by the results of our case study. However, the applicability of the method is contingent upon the accurate definition of potential future scenarios and the evaluation of the performance of different strategies within these scenarios. Conclusion: Our findings suggest that this approach significantly enhances strategic planning under uncertainty. Future research will seek to refine this method further by integrating causal relationships to convey additional information across different model periods, thereby improving the robustness and applicability of the strategy selection process.

The Vehicle Routing Problem with Time Windows (VRPTW) is paramount in elevating operational efficiency, driving cost reductions, and enhancing customer satisfaction. It is a renowned challenge with diverse real-world applications, where the core objective is determining the most efficient routes for a fleet of vehicles. This research introduces a cutting-edge Hybrid Genetic Algorithm-Solomon Insertion Heuristic (HGA-SIH) solution, reinforced by the powerful Solomon Insertion constructive heuristic to solve the VRPTW as an NP-hard problem. The performance of the proposed HGA-SIH is validated against Solomon’s VRPTW benchmark instances. The results showcase the outstanding performance of HGA, achieving Best-Known Solutions (BKS) for 11 instances and enhancing BKS solutions in one instance. Experimental findings validate that HGA-SIH consistently delivers results on par with or surpasses those obtained by several cutting-edge algorithms when evaluated based on various solution quality metrics. HGA-SIH consistently excels in efficiently managing the number of vehicles while minimizing travel distances, resulting in slight deviations from BKS that remain within practical limits. The research highlights the adaptability and efficacy of HGA-SIH in addressing a wide range of VRPTW scenarios, thereby making substantial contributions to logistics and supply chain optimization.

Today, the world is transitioning from traditional energy to clean, renewable sources. The petroleum sector is to play a role in this transition by supporting material and energy needs related to developing new energy systems. It is, therefore, vital that in upcoming years, the petroleum sector runs in a smart and efficient way, which can be achieved by coordination and the meaningful integration of decision-making issues in petroleum supply chains (PSCs). The existing literature on PSC optimization reveals a research gap; specifically, there is an insufficient level of technological detail considered while planning capacities of new infrastructures and its impact on the efficiency of further operations, specifically in the push segment of the PSC. This paper proposes a mixed-integer nonlinear programming model for planning capacities and coordinating activities within the mentioned PSC segment. The infrastructure capacity planning model covers technological details such as hydraulics and pump systems' operational efficiency. The results reveal that the proposed model and its technological decision-making criterion of minimizing energy consumption drive infrastructural choices and operational modes to achieve machinery performance close to the best efficiency point. Also, the computational results demonstrate how traditional (minimum-cost) approaches lead to inefficient energy use while producing and transporting hydrocarbons. The proposed framework aims to facilitate the preliminary design stage of projects undertaken by engineering contractors in the energy sector. [ABSTRACT FROM AUTHOR]

Resource planning and management are essential strategic practices in the supply chain. Resource allocation problems are becoming more complex due to the dynamic nature of these logistical systems. Since the increasing popularity of Deep Reinforcement Learning (DRL) algorithms in Gaming and Robotics, scholars have started investigating their potential for addressing supply chain concerns. The utilization of DRL-based approaches for addressing supply chain optimization problems remains largely unexplored. Therefore, we present a systematic literature analysis to investigate the adoption of DRL solution techniques for solving supply chain optimization problems. Afterward, we propose a novel method to address allocation problems in the supply chain founded on DRL-based algorithms, namely Asynchronous Actor critic (A3C) and Proximal Policy Optimization (PPO). A simulation model is developed to train and evaluate the application of these algorithms. We transform numerical data from the simulation into Gantt charts and pass them as observations. This formulation of the observation space is motivated by the fact that Deep Neural Networks (DNNs) are well-suited for image-based analysis. The computational results demonstrate that both algorithms successfully learn allocation policies using image-based observation considering multiple objective values. The results of the conducted experiments indicate that A3C achieves a more stable allocation policy than PPO in minimizing all considered objective values. [ABSTRACT FROM AUTHOR]

Purpose: This article introduces an innovative method designed to optimize distribution strategies with respect to future uncertainty. It goes beyond the limitations of traditional scenario-based planning that often leads to suboptimal strategies due to the unpredictability of future developments and the challenge of accurately assigning probabilities to these scenarios. Consequently, the method allows selection of the most economically viable future strategy. Methodology: Our methodology diverges from conventional approaches by refraining from making rigid assumptions about the probabilities of future scenarios. Instead, it comprehensively explores the entire allowable probability space to identify an optimal strategy that works well in possible future developments. We employed this method in the case study of a real-world company based in Czechia, where we devised three viable distribution strategies and four model development scenarios. Results: The application of our method demonstrated its effectiveness in selecting the most advantageous strategy, as evidenced by the results of our case study. However, the applicability of the method is contingent upon the accurate definition of potential future scenarios and the evaluation of the performance of different strategies within these scenarios. Conclusion: Our findings suggest that this approach significantly enhances strategic planning under uncertainty. Future research will seek to refine this method further by integrating causal relationships to convey additional information across different model periods, thereby improving the robustness and applicability of the strategy selection process. [ABSTRACT FROM AUTHOR]

Walmart takes a holistic approach to its supply chain, which integrates strategic and operational decisions consisting of three components: (1) network planning and transformation, which recommends the long-term network with step-by-step recommendations on how to achieve the end state; (2) a routing and loading system (Load Planner), which determines how to efficiently move products across the network; and (3) a simulation platform, which combines strategic and executional decision engines to enable a holistic decision-making process. Walmart built a set of scalable and fast optimization decision engines and deployed them using underlying innovative algorithms and models. The company fully adopted this next-generation optimization capability throughout its entire grocery supply chain in the United States, and approved optimization model-based network design and transformation plans for long-term investments involving billions of dollars. As a result of this efficient routing and loading executional system, Walmart prevented 98.6 million pounds of CO2 emissions and saved $91.5 million by eliminating 108,000 truck routes covering 33 million miles in fiscal year 2023 (FY23). Moreover, this optimization-empowered decision framework is evolving and transforming Walmart's supply chain while keeping its Every-Day-Low-Price (EDLP) promise to its customers. [ABSTRACT FROM AUTHOR]

Renewable jet fuel (RJF) production has been recognized as a promising approach for reducing the aviation sector's carbon footprint. Over the last decade, the commercial production of RJF has piqued the interest of airlines and governments around the world. However, RJF production can be challenging due to its dispersed supply resources. Furthermore, the production of RJF is more costly compared to producing conventional jet fuel. In this study, using a mixed integer linear programming (MILP), we design a corn-stover-based RJF supply chain network in which we obtain an optimized configuration of the supply chain and determine operational decisions required to meet RJF demand at airports. To accelerate the commercialization of RJF production, we examined four incentive programs designed to cover the supply chain's costs, with agricultural statistics districts serving as the designated supply regions. This study is validated by employing the model to design the supply chain in the Midwestern United States. The results from this study are promising as they show the supply chain can achieve commercialization with partial financial coverage from the incentive programs. Based on the findings of this study, policymakers can devise policies to commercialize RJF production and accelerate its adoption by the industry. [ABSTRACT FROM AUTHOR]

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.

Implementing reverse logistics in the construction industry is considered a crucial method to achieve a circular economy. Despite a wealth of research focusing on improving reverse logistics systems, businesses still encounter challenges during the implementation process. Therefore, this study conducted a systematic literature review utilizing bibliometric methods to analyze 623 articles on reverse logistics in the construction industry published on Web of Science from 1995 to 2023. Additionally, a comprehensive review of 56 high-quality literature on obstacles to implementing reverse logistics in the construction industry and optimizing reverse supply chains was conducted. This review uncovered the current status and challenges of implementing reverse logistics in the construction industry and proposed potential solutions to address these issues. The main findings of this study include: (1) increasing academic interest in construction waste reverse logistics, with Chinese scholars leading the way and publications predominantly in environmental and construction journals, with limited coverage in logistics journals; (2) the primary obstacles to implementing reverse logistics in the construction industry lie in supply chain management, such as lacking deconstruction designs, incomplete recycling markets, difficulties in evaluating the quality of secondary materials, and insufficient supply chain integration; (3) proposing a framework for a construction industry reverse logistics supply chain ecosystem, aiming to establish a platform to facilitate online collection of construction waste, online transactions of secondary materials, end-to-end monitoring, and data analytics for consultation.

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.

Artificial intelligence is considered the fastest-growing technology globally in the second decade of the 21st century. Different information technology companies have introduced Numerous artificial intelligence applications globally, with their uses in different industry perspectives. The business sector is one of the leading sectors that is adopting the artificial intelligence tools for its decision-making processes. A qualitative-based conceptual analysis method was used to address the issue in question. The researcher has gathered vast literature based on artificial intelligence and developed some literature-based themes, which are the main business sector areas where they use artificial intelligence as a decisionmaking process. Based on the findings, the following themes: data-driven decision-making, machine learning, automation, Personalization and Customer Experience, and supply chain optimization were developed from the past literature. Based on these developed themes, this study recommends that future researchers test these themes quantitatively on artificial intelligence decision-making to enhance the generalizability of the findings further. [ABSTRACT FROM AUTHOR]

The transport and logistics industry plays a crucial role in supporting the economy, but it faces various challenges, including high costs and the need for operational efficiency. To address these challenges, the industry is embracing digital transformation, and 5G networks are expected to play a significant role in this process. This paper explores the benefits of 5G technologies in the transportation and logistics sector, focusing on device density, low latency, network slicing, supply chain visibility, port operations, and enhanced communication. Additionally, the paper emphasizes the importance of stakeholder engagement and sustainability considerations in the adoption of innovative technologies. The research methodology involves an online survey administered to stakeholders in the port logistics sector, aiming to assess their knowledge and implementation of innovative technologies. The paper also reviews the relevant literature and highlights the potential of digital technologies, such as IoT, blockchain, AI, and 5G, in optimizing supply chains and port operations. The findings provide insights into the current state of knowledge and implementation of innovative technologies in port operations and the potential for market adoption and contribute to understanding the benefits and challenges of 5G technology in the logistics industry. [ABSTRACT FROM AUTHOR]

The challenges faced by classical supply chain management affect efficiency with regard to business. Classical supply chain management is associated with high risks due to a lack of accountability and transparency. The use of optimization algorithms is considered decision-making support to improve the operations and processes in green manufacturing. This paper suggests a solution to the green lot size optimization problem using bio-inspired algorithms, specifically, the butterfly algorithm. For this, our methodology consisted of first collecting the real data, then the data were expressed with a simple function with several constraints to optimize the total costs while reducing the CO2 emission, serving as input for the butterfly algorithm BA model. The BA model was then used to find the optimal lot size that balances cost-effectiveness and sustainability. Through extensive experiments, we compared the results of BA with those of other bio-inspired algorithms, showing that BA consistently outperformed the alternatives. The contribution of this work is to provide an efficient solution to the sustainable lot-size optimization problem, thereby reducing the environmental impact and optimizing the supply chain well. Conclusions: BA has shown that it can achieve the best results compared to other existing optimization methods. It is also a valuable chainsaw tool. [ABSTRACT FROM AUTHOR]

Cap-and-trade models have been largely studied in the literature when it comes to reducing emissions in a supply chain. In this paper, further pursuing the goal of analyzing the effectiveness of cap-and-trade strategies in reducing emissions in supply chains, we propose a mathematical model for sustainable supply chain management. This optimization program aims at reducing emissions and supply chain costs in an unregulated scenario w.r.t. the cap definition, i.e., trading CO2 is allowed but no formal limit on the CO2 emissions is imposed. Also, we considered an initial budget for technological investments by the facilities in the considered supply chain, allowing plants to reduce their unit production emissions at a different unit production cost. For this model, differently from what exists in the literature, we derive some theoretical conditions guaranteeing that, if obeyed, the emissions over time have a non-increasing trend meaning that decreasing caps over time can be attained with a self-regulated scenario. Computational results show the effectiveness of our approach.

In this work, we present a generic multi‐period integrated infrastructure planning and operational scheduling approach for developing energy systems. The methodology is applicable to energy systems with multiple resources, locations, processing pathways, and planning periods in which infrastructure decisions can be carried out. The framework incorporates a graph based approach to mode based scheduling, in which the mode transitions are mapped to a directed graph. We illustrate the applicability and effectiveness of the overall framework through the use of a case study examining the long‐term development of a multi‐site energy‐intensive hydrogen based energy system in Texas. In the case study, we find that developing the energy system over the course of its operational life as opposed to the beginning of its operational life reduces its capital and operational cost by over 20%. [ABSTRACT FROM AUTHOR]