Search Results (154)

This paper proposes the Open Competency Optimization (OCO) approach, based on adaptive combinatorial operators, to solve the Dynamic Green Traveling Salesman Problem (DG-TSP), which extends the classical TSP by incorporating dynamic travel conditions, realistic road gradients, and energy consumption considerations. The objective is to minimize fuel consumption and emissions by reducing the total tour length under varying conditions. Unlike conventional metaheuristics based on real-coded representations, our method directly operates on combinatorial structures, ensuring efficient adaptation without costly transformations. Embedded within a dynamic metaheuristic framework, our operators continuously refine the routing decisions in response to environmental and demand changes. Experimental assessments conducted in practical contexts reveal that our algorithm attains a tour length of 21,059, which is indicative of a 36.16% reduction in fuel consumption relative to Ant Colony Optimization (ACO) (32,994), a 4.06% decrease when compared to Grey Wolf Optimizer (GWO) (21,949), a 2.95% reduction in relation to Particle Swarm Optimization (PSO) (21,701), and a 0.90% decline when juxtaposed with Genetic Algorithm (GA) (21,251). In terms of overall offline performance, our approach achieves the best score (21,290.9), significantly outperforming ACO (36,957.6), GWO (122,881.04), GA (59,296.5), and PSO (36,744.29), confirming both solution quality and stability over time. These findings underscore the resilience and scalability of the proposed approach for sustainable logistics, presenting a pragmatic resolution to enhance transportation operations within dynamic and ecologically sensitive environments. Full article

Background: Lifestyle modifications are pivotal to preventing the progression of prediabetes and associated cardiometabolic diseases. Recent evidence from cross-sectional analysis of community-dwelling Chinese adults suggests that regular consumption of tea, particularly dark tea, is associated with a reduced risk of both prediabetes and type 2 diabetes. However, the effects of tea consumption on prediabetes progression and regression remain uncertain. This study investigated the associations of tea consumption with prediabetes progression and regression in Chinese adults with prediabetes. Methods: A cohort of 2662 Chinese adults with prediabetes was followed over ~3 years. Baseline tea consumption, including the type (green, black, dark, or other) and frequency (daily, sometimes, or nil), was assessed using standardized questionnaires. Prediabetes was defined according to the American Diabetes Association criteria. Multinomial logistic and linear regression analyses with multivariable adjustment was performed to evaluate associations. Results: Compared to non-tea drinkers, dark tea consumers were less likely to progress to type 2 diabetes (odds ratio [OR]: 0.28, 95% confidence interval [CI]: 0.11, 0.72, p = 0.01), whereas green tea consumption was associated with a reduced probability of regressing to normoglycemia (OR: 0.73, 95 CI%: 0.59, 0.90, p = 0.01). Conclusions: These findings support further exploration of dark tea consumption as a strategy to reduce prediabetes progression, and suggest that effects of green tea consumption should also be examined more closely in this population. Full article

This study proposes a fuzzy logic-based approach to better manage supply chain uncertainty and improve decision-making flexibility. The developed framework categorizes supply chain activities into procurement, production, distribution and reverse logistics and integrates Lean, Agile, Resilient, and Green (LARG) KPIs within a hierarchical structure. The tool was implemented using Microsoft Excel^TM to enhance usability for practitioners. To test its applicability, the model was applied to a real case study. The results show that lean and resilient practices are consistently well-established across all supply chain phases, while agility and green practices vary significantly depending on the operational area—particularly between internal function (i.e., production and reverse logistics) and external ones (i.e., procurement and distribution). These findings help to better understand how the LARG capabilities are distributed across the different operational areas of the supply chain and offer practical guidance for managers seeking targeted performance improvement. Although the numerical results are context-specific, the framework’s adaptability makes it suitable for diverse supply chain environments. Full article

Over recent decades, widespread declines of kelp forests have been reported along the European coast, prompting the need for effective and scalable restoration strategies. The green gravel technique, in which kelp gametophytes are seeded onto small rocks and cultivated in the lab before being outplanted, has shown promising results. In this study, we tested the effects of four commonly available substrates—granite, limestone, quartz, and schist—on the early development of Laminaria ochroleuca recruits under optimal laboratory conditions. All substrates supported gametophyte adhesion and sporophyte development. By week 6, quartz promoted the greatest recruit length (1.25 ± 0.16 mm), with quartz and limestone (1.54 ± 0.17 and 1.58 ± 0.14 mm, respectively) showing the best overall performance by week 7. Final recruit densities were similar across substrates, indicating multiple materials can support early development. Quartz and limestone showed both biological effectiveness and practical advantages, with limestone emerging as the most cost-effective option. Substrate selection should consider not only biological performance but also economic and logistical factors. These findings contribute to refining green gravel protocols and improving the feasibility of large-scale kelp forest restoration, although field validation is necessary to assess long-term outcomes under natural conditions. Full article

The concepts of sustainable development, triple bottom line, and ESG have a strong influence on the process of formation and operation of supply chains today. This requires the implementation of various green solutions and practices to improve supply chain sustainability. An analysis of supply chain research did not reveal a universally accepted methodology to systematize green solutions and practices for their effective use in chain management. It was revealed that there are many views on the content of green solutions, in addition to insufficient specificity of their description, as well as fragmentation of the use of green solutions in relation to the elements and functions of supply chains (procurement, production, warehousing, transportation, and distribution). This reduces the effectiveness of the implementation of green solutions. In this study, based on the literature review, a systematization of currently existing green solutions and practices was carried out. The systematization was performed according to the affiliation of supply chain elements and the functions performed by the elements to promote and process the material flow from supplier to consumer. The proposed system of methods (GLMs) and instruments (GLIs) of green logistics covers all known functional areas of logistics and includes 27 methods and 105 instruments. We performed a ranking of methods and instruments using TOPSIS, MABAC, and MARCOS methods. The most and least significant GLM and GLI for each element of the supply chain, as well as for chains of complex structure in general, were determined. The results of GLM and GLI ranking can be used as a basis for the implementation of management decisions to improve the sustainability of supply chains. Full article

This study examines the effect of internal dynamic capabilities i.e., digital leadership, environmental awareness, and organizational learning, on sustainable supply chain performance as studied in the logistics sector. It builds on the Dynamic Capabilities Theory by combining notions of green innovation and sustainability and fills the growing gap in the existing literature. Despite the fact that these domains have been extensively studied independently, there has been limited research examining how internal capabilities contribute to green supply chain innovation (GSCI) that in turn results in sustainability outcomes, especially in the case of emerging markets. Seven hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis of data collected from 312 logistics and supply chain professionals in Jordan. This study shows that each of the three capabilities has a major effect on GSCI and therefore sustainable performance. Linking the most influential predictor of sustainability outcome to sustainable supply chain performance, as indicated by the strongest effect (β = 0.825, p < 0.001) between GSCI and sustainable supply chain performance, and followed by significant coefficients between the sustainable information processing (β = 0.261, p < 0.01), and information capabilities (β = 0.297, p < 0.001), indicates that the theory is more suited to GSCI. In particular, digital leadership had the largest impact on the green innovation (β = 0.481, p < 0.001), indicating that the role of digitally driven leadership is to facilitate eco-innovation. In addition, this intermediate factor, GSCI, serves as a variable that mediates relationships between the capabilities and the sustainability outcomes. As the results here suggest, leveraging internal capabilities is a very tangible channel for green innovation that has important ramifications for practitioners and policymakers facing resource constraints. 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

In the context of mounting concerns over carbon emissions and the need to accelerate the energy transition, green hydrogen has emerged as a strategic solution for decarbonizing hard-to-abate sectors. This paper introduces a methodological innovation by proposing the Green Hydrogen Efficiency Index (GHEI), a unified and quantitative framework that integrates multiple stages of the hydrogen value chain into a single comparative metric. The index encompasses six core criteria: electricity source, water treatment, electrolysis efficiency, compression, end-use conversion, and associated greenhouse gas emissions. Each are normalized and weighted to reflect different performance priorities. Two weighting profiles are adopted: a first profile, which assigns equal importance to all criteria, referred to as the balanced profile, and a second profile, derived using the analytic hierarchy process (AHP) based on structured expert judgment, named the AHP profile. The methodology was developed through a systematic literature review and was applied to four representative case studies sourced from the academic literature, covering diverse configurations and geographies. The results demonstrate the GHEI’s capacity to distinguish the energy performance of different green hydrogen routes and support strategic decisions related to technology selection, site planning, and logistics optimization. The results highlight the potential of the index to contribute to more sustainable hydrogen value chains and advance decarbonization goals by identifying pathways that minimize energy losses and maximize system efficiency. Full article

Most image encryption methods based on Rubik’s cube scrambling adopt the idea of cyclic shift or map the image pixels to the cube surface, not fully considering the cube’s three-dimensional (3D) properties. In response to this defect, we propose a mixed chaotic color image encryption method based on parallel rotation scrambling in 3D Rubik’s cube space. First, a seven-dimensional hyperchaotic system is introduced to generate chaotic pseudo-random integer sequences. Then, a proven lemma is applied to preprocess the red (R), green (G), and blue (B) channels of the plain image to realize the first diffusion. Next, the chaotic integer sequence is employed to control Arnold transformation, and the scrambled two-dimensional (2D) pixel matrix is converted into a 3D matrix. Then, the 3D cube is scrambled by dynamically selecting the rotating axis, layer number, and angle through the chaotic integer sequence. The scrambled 3D matrix is converted into a 2D matrix, realizing the second diffusion via exclusive OR with the chaotic matrix generated by logistic mapping. Finally, the matrices of the R, G, and B channels are combined into an encrypted image. By performing the encryption algorithm in reverse, the encrypted image can be decrypted into the plain image. A simulation analysis shows that the proposed method has a larger key space and exhibits stronger key sensitivity than some existing methods. Full article

Road transport is a major contributor to air pollution, necessitating sustainable solutions for urban logistics. This study presents a time-dependent vehicle routing problem (VRP) model aimed at minimizing fuel consumption and greenhouse gas emissions while addressing stochastic customer demands. By incorporating key environmental factors such as road gradients, vehicle load, temperature, wind direction, and asphalt type, the proposed model provides a comprehensive approach to reducing transportation-related pollutants. To solve the computationally complex problem, a hybrid algorithm combining the gray wolf optimizer (GWO) and the multilayer perceptron (MLP) neural network is introduced. The algorithm demonstrates superior performance, achieving an error rate of less than 2% for medium-scale problems and significantly reducing fuel and driver costs. Sensitivity analyses reveal the profound impact of environmental parameters, with wind speed and direction altering optimal routing in over 80% of cases for large-scale instances. This research advances green logistics by integrating dynamic environmental considerations into routing decisions, balancing economic objectives with sustainability. The proposed model and algorithm offer a scalable solution to real-world challenges, enabling policymakers and logistics planners to improve environmental outcomes while maintaining operational efficiency. Full article

Background: Prostate cancer is the most common malignancy among UK men, yet the lack of a national screening program creates disparities in early detection. PSA testing during primary care is inconsistent, limiting timely diagnosis. The Graham Fulford Charitable Trust (GFCT) and its associated support groups offer community-based PSA testing to help bridge this gap. Objectives: This study evaluates GFCT’s historical testing records and a participant survey (2021–2024) to assess their community-based PSA testing program, which is widely used and offers shared decision support. Additionally, we examine the GFCT’s alignment with emerging initiatives such as the UK TRANSFORM trial and other PSA screening developments across Europe and the USA. Methods: The GFCT systematically collects PSA testing data, conducting over 50,000 tests annually. The study assesses the performance of their traffic light scoring system and a previously defined combination algorithm, “Riskman”, which incorporates PSA, PSAft%, and age to classify individuals into risk categories. Multivariable logistic regression was used to evaluate and compare the predictive performance of each approach. Results: Based on the self-reported data in the survey, the GFCT’s traffic light system—using age-specific PSA thresholds to assign green, amber, or red risk—showed good predictive ability. Men in the red group had over 15-fold increased odds of clinically significant cancer (Grade Group ≥ 3) compared to those in the green group. While the Riskman score achieved a higher AUC (0.84 vs. 0.76), both tools were effective in identifying high-risk individuals. Conclusions: This study highlights the GFCT’s role in improving access to PSA screening and integrating practical risk stratification. Its alignment with evolving screening initiatives demonstrates the value of community-based, data-driven approaches for earlier detection of aggressive prostate cancer. Full article

The sea–rail intermodal container terminal serves as a key transportation hub for green logistics, where efficient resource coordination directly enhances multimodal connectivity and operational synergy. To address limited storage capacity and trans-shipment inefficiencies, this study innovatively proposes a resource-sharing strategy between the seaport and the railway container terminal, focusing on the joint allocation of yard space and internal trucks. For indirect trans-shipment operations between ships, the port, the railway container terminal, and trains, a mixed-integer programming model is formulated with the objective of minimizing the container trans-shipment cost and the weighted turnaround time of ships and trains. This model simultaneously determines yard allocation, container transfers, and truck allocation. A two-layer hybrid heuristic algorithm incorporating adaptive Particle Swarm Optimization and Greedy Rules is designed. Numerical experiments verify the model and algorithm performance, revealing that the proposed method achieves an optimality gap of only 1.82% compared to CPLEX in small-scale instances while outperforming benchmark algorithms in solution quality. And the shared yard strategy enhances ship and train turnaround efficiency by an average of 33.45% over traditional storage form. Sensitivity analysis considering multiple realistic factors further confirms the robustness and generalizability. This study provides a theoretical foundation for sustainable port–railway collaboration development. Full article

To advance energy conservation and emissions reduction in urban logistics systems, this study focuses on the green vehicle routing problems with time windows (GVRPTWs), which remains underexplored in balancing environmental and service quality objectives. We propose a comprehensive multi-objective optimization framework that addresses this gap by simultaneously minimizing total distribution costs and carbon emissions while maximizing customer satisfaction, quantified based on the vehicle’s arrival time at the customer location. The rationale for adopting this tri-objective formulation lies in its ability to reflect real-world trade-offs between economic efficiency, environmental performance, and service level, which are often considered in isolation in previous studies. To tackle this complex problem, we develop an improved Non-Dominated Sorting Genetic Algorithm III (NSGA-III) that incorporates three key enhancements: (1) an integer-encoded initialization method to enhance solution feasibility, (2) a refined selection strategy utilizing crowding distance to maintain population diversity, and (3) an embedded 2-opt local search operator to prevent premature convergence and avoid local optima. Comprehensive validation experiments using Solomon’s benchmark instances and a real-world case demonstrate that the presented algorithm consistently outperforms several state-of-the-art multi-objective optimization methods across key performance metrics. These results highlight the effectiveness and practical relevance of our approach in advancing energy-efficient, low-emission, and customer-centric urban logistics systems. Full article

The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives. Full article

As the core carrier of the low-carbon transportation transformation, the sustainable optimization of the supply chain of new energy vehicles is crucial to reduce carbon emissions throughout the life cycle and improve resource utilization efficiency. However, the current problems, such as resource waste, duplicate production, and low logistics efficiency caused by insufficient supply chain information coordination, have become bottlenecks restricting the green development of the industry. A large number of studies have shown that information collaboration plays a key role in reducing risks and costs, improving quality and innovation capabilities, adaptability, performance, and supply chain competitiveness in the new energy vehicle supply chain. Although the advantages of supply chain information collaboration are widely known, supply chain information collaboration has not been widely adopted in actual operation, and there are almost no studies on the lack of adoption or the restriction of the development of supply chain information collaboration. Based on the research methods of the modified Delphi technique and analytic hierarchy process (AHP), this paper finds that the lack of information quality, information security, and information collaboration motivation are important factors restricting the collaborative development of information in the new energy vehicle supply chain. Furthermore, an optimization model of the new energy vehicle supply chain information co-evolution game combined with traceability and blockchain technology is proposed, and it is found that the evolutionary game model that solves the stability of information quality and information security has a significant effect on the information collaborative optimization of the new energy vehicle supply chain. This study proposes an information co-evolution game model combined with blockchain traceability technology, which can improve the level of information collaboration in the supply chain of new energy vehicles, significantly reduce the “bullwhip effect” and redundant inventory in the supply chain, reduce energy waste and carbon emissions caused by information asymmetry, and improve the overall energy efficiency of the supply chain, so as to provide theoretical support for the sustainable and green supply chain transformation of the new energy vehicle industry. Full article