Search Results (133)

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

In precision manufacturing, selecting the most economically viable process is essential for low-volume, high-complexity applications. This study compares the machining process (MP), conventional investment casting (CIC), and rapid prototyping (RP) through a mathematical cost model based on the activity-based costing (ABC) approach. The model captures detailed cost drivers across design, logistics, production, and environmental dimensions. Results show that MP incurs the highest production cost (94.45%) but minimal logistics (3.43%). CIC bears the highest total cost and significant production overhead (93.2%), while RIC achieves the lowest total cost, driven by major savings in production (84.6%) and labor. Although RIC has slightly higher logistics than MP, it demonstrates superior economic efficiency for small-batch, high-accuracy production. This study provides a unified quantitative framework for cost comparison and offers valuable guidance for manufacturers aiming to enhance efficiency, sustainability, and profitability across diverse fabrication strategies. Full article

Background: Gold mining is a critical part of the industry of Central Asia, contributing significantly to regional economic growth. However, gold production management faces numerous challenges, including adopting innovative technologies such as AI, using improved logistical equipment, resolving supply chain inefficiencies and disruptions, and incorporating modernized waste management and advancements in gold bar processing technologies. This study explores how advanced technologies and improved logistical processes can enhance efficiency and sustainability. Method: This paper examines gold production processes in Kyrgyzstan, a gold-producing country in Central Asia. The case study approach combines qualitative interviews with industry stakeholders and a system dynamics (SD) simulation model to compare current operations with a technology-based scenario. Results: The simulation model shows improved outcomes when innovative technologies are applied to ore processing, waste refinement, and gold bar production. The results also indicate an approximate twenty-five percent reduction in transport time, a thirty percent decrease in equipment downtime, a thirty percent reduction in emissions, and a fifteen percent increase in gold extraction when using artificial intelligence, smart logistics, and regional smelting. Conclusions: The study concludes with recommendations to modernize equipment, localize processing, and invest in digital logistics to support sustainable mining and improve operational performance in Kyrgyzstan’s gold sector. Full article

The generation of fine coal particles during mining and processing presents significant environmental and logistical challenges, particularly in coal-dependent, developing countries like South Africa (SA). This review critically evaluates the technical viability of fine coal briquetting as a sustainable waste-to-energy solution within a SA context, while drawing from global best practices and comparative benchmarks. It examines abundant feedstocks that can be used for valorization strategies, including fine coal and agricultural biomass residues. Furthermore, binder types, manufacturing parameters, and quality optimization strategies that influence briquette performance are assessed. The co-densification of fine coal with biomass offers a means to enhance combustion efficiency, reduce dust emissions, and convert low-value waste into a high-calorific, manageable fuel. Attention is also given to briquette testing standards (i.e., South African Bureau of Standards, ASTM International, and International Organization of Standardization) and end-use applications across domestic, industrial, and off-grid settings. Moreover, the review explores socio-economic implications, including rural job creation, energy poverty alleviation, and the potential role of briquetting in SA’s ‘Just Energy Transition’ (JET). This paper uniquely integrates technical analysis with policy relevance, rural energy needs, and practical challenges specific to South Africa, while offering a structured framework for bio-coal briquetting adoption in developing countries. While technical and economic barriers remain, such as binder costs and feedstock variability, the integration of briquetting into circular economy frameworks represents a promising path toward cleaner, decentralized energy and coal waste valorization. Full article

This paper investigates sustainable decision systems within green E-business models by analyzing how different O2O (online-to-offline) fulfillment structures affect emission-reduction efforts and pricing strategies in a two-tier supply chain consisting of a manufacturer and a new retailer. Three practical sales formats—package self-pickup, nearby delivery, and hybrid—are modeled using Stackelberg game frameworks that incorporate key factors such as inconvenience cost, logistics cost, processing fees, and emission-reduction coefficients. Results show that the manufacturer’s emission-reduction decisions and both parties’ pricing strategies are highly sensitive to cost conditions and consumer preferences. Specifically, higher inconvenience and abatement costs consistently reduce profitability and emission efforts; the hybrid model exhibits threshold-dependent advantages over single-mode strategies in terms of carbon efficiency and economic returns; and consumer green preference and distance sensitivity jointly shape optimal channel configurations. Robustness analysis confirms the model’s stability under varying parameter conditions. These insights provide theoretical and practical guidance for firms seeking to develop adaptive, low-carbon fulfillment strategies that align with sustainability goals and market demands. Full article

This article examines the modeling of sustainable development in transport logistics, focusing on the impact of climate factors, changing weather conditions, and digitalization processes. The study analyzes the complex influence of adverse weather phenomena, such as fog, rain, snow, extreme temperatures, and strong winds, whose frequency and intensity are increasing due to climate change, on the efficiency, safety, and reliability of transport systems across all modes except pipelines. Special attention is paid to the integration of weather-resilient sensor technologies, including LiDAR, thermal imaging, and advanced monitoring systems, to strengthen infrastructure resilience and ensure uninterrupted transport operations under environmental stress. The methodological framework combines comparative analytical methods with economic–mathematical modeling, particularly Leontief’s input–output model, to evaluate the mutual influence between the transport sector and sustainable economic growth within an interconnected ecosystem of economic and technological factors. The findings confirm that data-driven management strategies, the digital transformation of logistics, and the strengthening of centralized hubs contribute significantly to increasing the resilience and flexibility of transport systems, mitigating the negative economic impacts of climate risks, and promoting long-term sustainable development. Practical recommendations are proposed to optimize freight flows, adapt infrastructure to changing weather risks, and support the integration of innovative digital technologies as part of an evolving ecosystem. Full article

Heilongjiang Province, as China’s principal gateway for Russian timber imports, faces structural inefficiencies in the localization of wood-processing enterprises—characterized by ecological sensitivity, resource–industry mismatches, and uneven spatial distribution. To address these challenges, this study proposes a two-stage site selection framework that integrates Geographic Information Systems (GIS) with an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II). The model aims to reconcile ecological protection with industrial efficiency by identifying optimal facility locations that minimize environmental impact, reduce construction and logistics costs, and enhance service coverage. Using spatially resolved multi-source datasets—including forest resource distribution, transportation networks, ecological redlines, and socioeconomic indicators—the GIS-based suitability analysis (Stage I) identified 16 candidate zones. Subsequently, a multi-objective optimization model (Stage II) was applied to minimize carbon intensity and cost while maximizing service accessibility. The improved NSGA-II algorithm achieved convergence within 700 iterations, generating 124 Pareto-optimal solutions and enabling a 23.7% reduction in transport-related CO₂ emissions. Beyond carbon mitigation, the model spatializes policy constraints and economic trade-offs into actionable infrastructure plans, contributing to regional sustainability goals and transboundary industrial coordination with Russia. It further demonstrates methodological generalizability for siting logistics-intensive and policy-sensitive facilities in other forestry-based economies. While the model does not yet account for temporal dynamics or agent behaviors, it provides a robust foundation for informed planning under China’s dual-carbon strategy and offers replicable insights for the global forest products supply chain. Full article

The reduction of environmental pollutant emissions—including greenhouse gases, particulate matter, and other harmful substances—represents one of the foremost challenges in climate policy, economics, and industrial management today. Excessive emissions of CO₂, NO_X, and suspended particulates exert significant impacts on climate change as well as human health and welfare. Consequently, numerous studies and regulatory and technological initiatives are underway to mitigate these emissions. One critical area is intra-plant transport within manufacturing facilities, which, despite its localized scope, can substantially contribute to a company’s total emissions. This paper aims to assess the potential of computer simulation using FlexSim software as a decision-support tool for planning inter-operational transport, with a particular focus on environmental aspects. The study analyzes real operational data from a selected production plant (case study), concentrating on the optimization of the number of transport units, their routing, and the layout of workstations. It is hypothesized that reducing the number of trips, shortening transport routes, and efficiently utilizing transport resources can lead to lower emissions of carbon dioxide (CO₂) and nitrogen oxides (NO_X). The findings provide a basis for a broader adoption of digital tools in sustainable production planning, emphasizing the integration of environmental criteria into decision-making processes. Furthermore, the results offer a foundation for future analyses that consider the development of green transport technologies—such as electric and hydrogen-powered vehicles—in the context of their implementation in the internal logistics of manufacturing enterprises. Full article

The Fourth Industrial Revolution and artificial intelligence (AI) technology are driving the transformation of the meat industry from mechanization and automation to intelligence and digitization. This paper provides a systematic review of key technological innovations in this field, including physical technologies (such as smart cutting precision improved to the millimeter level, pulse electric field sterilization efficiency exceeding 90%, ultrasonic-assisted marinating time reduced by 12 h, and ultra-high-pressure processing extending shelf life) and digital technologies (IoT real-time monitoring, blockchain-enhanced traceability transparency, and AI-optimized production decision-making). Additionally, it explores the potential of alternative meat production technologies (cell-cultured meat and 3D bioprinting) to disrupt traditional models. In application scenarios such as central kitchen efficiency improvements (e.g., food companies leveraging the “S2B2C” model to apply AI agents, supply chain management, and intelligent control systems, resulting in a 26.98% increase in overall profits), end-to-end temperature control in cold chain logistics (e.g., using multi-array sensors for real-time monitoring of meat spoilage), intelligent freshness recognition of products (based on deep learning or sensors), and personalized customization (e.g., 3D-printed customized nutritional meat products), these technologies have significantly improved production efficiency, product quality, and safety. However, large-scale application still faces key challenges, including high costs (such as the high investment in cell-cultured meat bioreactors), lack of standardization (such as the absence of unified standards for non-thermal technology parameters), and consumer acceptance (surveys indicate that approximately 41% of consumers are concerned about contracting illnesses from consuming cultured meat, and only 25% are willing to try it). These challenges constrain the economic viability and market promotion of the aforementioned technologies. Future efforts should focus on collaborative innovation to establish a truly intelligent and sustainable meat production system. Full article

Indonesia’s high logistics costs—approximately 14.6% of its GDP—pose a significant challenge to national economic competitiveness. Key contributing factors include complex geography, fragmented multimodal transport systems and inefficient container terminal operations, particularly concerning the handling of empty containers. This study investigates operational optimization in a container terminal using Agile and Lean principles, without additional investment or infrastructure expansion. It compares throughput before and after optimization, focusing on equipment productivity and reduction in idle time, especially related to equipment and human resources. Field implementation began in 2015, followed by simulation-based validation using system dynamics modeling. The terminal demonstrated a sustained increase in capacity beginning in 2016, eventually exceeding its original design capacity while maintaining acceptable berth and Yard Occupancy Ratios (BOR and YOR). Agile practices improved empty container handling, while Lean methods enhanced berthing process efficiency. The findings confirm that significant reductions in port operational costs, shipping operational costs, voyage turnover time, and logistics costs can be achieved through strategic operational reforms and better resource utilization, rather than through capital-intensive expansion. The study provides a replicable model for improving terminal efficiency in ports facing similar constraints. Full article

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. Results indicate that Artificial Neural Networks (ANN) and GB consistently outperform traditional models, particularly in capturing non-linear relationships and complex interactions among predictive factors. This study advances financial risk management by demonstrating the efficacy of ML-driven credit rating systems, offering a more accurate, efficient, and scalable solution. Additionally, it provides practical insights for financial institutions aiming to enhance their risk assessment frameworks. Future research should explore alternative data sources, real-time analytics, and model explainability to facilitate regulatory adoption. Full article

This systematic literature review explores how conflict management strategies (CMS) impact supply chain performance (SCP), focusing on the mediating roles of supply chain operational processes (SCOP) and customer-centric green supply chain management (CCGSCM) within Jordan’s food manufacturing sector. Framed within smart city initiatives and sustainable development goals (SDGs 9, 11, and 12), this study addresses critical gaps identified in the literature, particularly the lack of integrated examination of CMS impacts in emerging markets like Jordan. Utilizing thematic analysis, this review consolidates key findings across relevant studies from 2010 to 2025 sourced from top-tier databases. The results reveal that collaboration emerges as the most effective CMS strategy, enhancing stakeholder interactions, operational coordination, and resilience. SCOP significantly mediate CMS–SCP relationships, with logistics and inventory management notably vital in mitigating disruptions. Additionally, CCGSCM is highlighted as pivotal for sustainability and operational efficiency in post-COVID market conditions. The findings offer valuable insights for practitioners and policymakers, providing strategic recommendations for integrating technology-driven and relationship-focused CMS tailored to Jordan’s unique socio-economic context, thereby aligning operational practices with global sustainability goals (SDGs 9, 11, and 12). Full article

Modern logistics requires effective solutions for the optimization of intermodal transportation, providing cost reduction and improvement of transport flows. This paper proposes a multi-objective optimization method for intermodal freight transportation planning within the framework of sustainable service network design. The approach aims to balance economic efficiency and environmental sustainability by minimizing both transportation costs and delivery time. A bi-criteria mathematical model is developed and solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), which is well-suited for handling complex, large-scale optimization problems under multiple constraints. The aim of the study is to develop and implement this technology that balances economic efficiency, environmental sustainability and manageability of operational processes. The research includes the development of a two-criteria model that takes into account both temporal and economic parameters of the routes. The optimization method employs the NSGA-III, a well-known metaheuristic that generates a diverse set of near-optimal Pareto-efficient solutions. This enables the selection of trade-off alternatives depending on the decision-maker’s preferences and specific operational constraints. Simulation results show that the implementation of the proposed technology can reduce the costs of intermodal operators by an average of 8% and the duration of transportation by up to 50% compared to traditional planning methods. In addition, the automation of the process contributes to a more rational use of resources, reducing carbon emissions and increasing the sustainability of transportation networks. This approach is in line with the principles of sustainable economic development, as it improves the efficiency of logistics operations, reduces pressure on infrastructure and minimizes the environmental impact of the transport sector. Route optimization and digitalization of transport processes can increase resource efficiency, improve freight flow management and contribute to the long-term stability of transport systems. The developed technology of automated planning of intermodal transportation is oriented to application in large-scale production systems, providing effective management of cargo flows within complex logistics chains. The proposed method supports the principles of sustainable development by providing a formal decision-making framework that balances transportation cost, delivery time and environmental objectives. Instead of optimizing for a single goal, the model enables the identification of efficient trade-offs between economic performance and ecological impact. Moreover, by generating multiple routing scenarios under varying operational constraints, the approach enhances the adaptability and robustness of freight transportation systems in dynamic and uncertain environments. Full article

Retreading is a cornerstone in the remanufacturing process of tires, facilitating the extraction of maximum kilometers (Km) from a tire carcass. Tire remanufacturing plays a crucial role in conserving raw materials, reducing environmental impacts, and lowering the overall operating costs. This study employs predictive modeling techniques to forecast tire performance and optimize resource allocation, departing from traditional approaches, for a bus transport system in India. Machine learning models, including linear regression, ensemble boosted trees, and neural network models, were used. Two scenarios were devised: Scenario I addressed premature failures and optimizing performance to reduce tire procurement and Scenario II used targeted interventions, such as eliminating new tire condemnations and optimizing retread (RT) strategies, and could potentially salvage 169 tires from premature retirement. The results achieved R² values of 0.44, 0.51, and 0.45 and improved values for the test datasets of 0.46, 0.52 and 0.44. By leveraging these models, decision-makers can substantially improve tire mileage, reduce premature condemnations, increase tire production, and drive cost savings in fleet operations. Notably, this approach contributes to enhanced operational efficiency and promotes sustainability by cutting costs by 15–25%, improving tire mileage by 20–30%, and reducing environmental impacts by up to 25%. These results demonstrate the broader implications of predictive modelling as a decision-support tool, highlighting its capacity to drive economic and environmental benefits across industrial logistics and sustainable development. Full article

This paper examines the costs associated with installing PIP (Pile-in-Pile) slip joints compared to traditional tubular joints, focusing on investment, installation processes, and long-term benefits. Previous studies have indicated that the structural performance of PIP slip joints is superior to that of traditional joints. By utilizing the frictional interfaces between conventional structural steel components and the simplest installation methods, PIP slip joints maximize structural integrity and ease of maintenance. As a result, they can lead to lower lifecycle costs, provided they are installed correctly. Quantitatively, the PIP slip joint achieved the highest internal rate of return (IRR) at 43.42%, the lowest Levelized Cost of Energy (LCOE) at 0.013589 EUR/kWh, and the shortest payback period at 2.92 years—outperforming grouted and bolted flange joints across all key financial metrics. The analysis also addresses logistical challenges and workforce requirements, highlighting that significant economic benefits can be realized when implemented appropriately. Furthermore, the PIP slip joint promotes sustainability goals by minimizing material usage, which ultimately leads to reduced carbon emissions through more efficient fabrication and installation, as well as enabling faster deployment. A comprehensive financial assessment of these joint systems in offshore wind monopiles reveals that PIP slip joints are the most cost-effective and financially advantageous option, outperforming key metrics like IRR, LCOE, and payback period due to lower initial investments and operational costs. As PIP slip joints yield a higher net present value (NPV), a shorter payback period, and a lower LCOE, they can enhance profitability and reduce financial risk, and are suitable for streamlined implementation. While grouted and bolted flange joints exhibit similar financial performance, PIP slip joints’ minimal expenditure and consistent superiority make them the optimal choice for sustainable and economically viable offshore wind projects. Full article