摘要：
As the 3D woven composites have broad application prospects in aerospace and high-speed train body structures, exploring their strain rate effects is fundamental to enhancing their out-of-plane impact resistance during service. This paper conducts experiments on the impact behavior of 3D woven composites under different strain rates in three directions and validates the established mesoscale finite element model. By comparing experimental and simulation results, it is found that the peak stress and maximum strain during impact compression show significant strain rate effects in all three directions, with the degree of influence being negatively correlated with fiber areal density. When the areal densities ratio of warp, weft, and out-of-plane direction is 1:2:0.675, and when the strain rate in the warp and out-of-plane directions increases by 122.22% and 41.67% respectively, the ultimate stress increases by 20.78% and 18.96%, and when the strain rate in the weft direction increases by 68.22%, the ultimate stress only increases by 6.33%. Damage in the warp and weft directions is initiated and propagated by tension between yarns caused by the impact, while the Z-yarn plays a role in inhibiting this propagation process. Out-of-plane impact damage is induced by shear loading, exhibiting stronger impact compression resistance compared to in-plane directions.
摘要：
To achieve novel core with high energy absorption and strong water intrusion resistance, a composite structure was created by employing in-situ foaming of closed-cell polyurethane (PU) in a honeycomb structure. Experimental, theoretical, and finite element analyses revealed that the in-situ foaming technique increased the honeycomb's energy absorption by 312.84 % and improved impact efficiency by 61.45 % while maintaining specific energy absorption (SEA). Remarkably, this composite structure surpassed the energy absorption capacity of individual honeycomb and foam. The coupling gain ratio introduced by the foam increases within a certain range as the honeycomb cell size decreases and wall thickness increases, with little influence from the foam's mechanical properties. Water intrusion, leading to ice formation at low temperatures, drastically reduced honeycomb SEA by 94.61 % and impaired mechanical properties significantly. Closed-cell PU in-situ foaming provided exceptional water intrusion resistance, preserving the honeycomb's mechanical performance, including energy absorption and SEA.
To achieve novel core with high energy absorption and strong water intrusion resistance, a composite structure was created by employing in-situ foaming of closed-cell polyurethane (PU) in a honeycomb structure. Experimental, theoretical, and finite element analyses revealed that the in-situ foaming technique increased the honeycomb's energy absorption by 312.84 % and improved impact efficiency by 61.45 % while maintaining specific energy absorption (SEA). Remarkably, this composite structure surpassed the energy absorption capacity of individual honeycomb and foam. The coupling gain ratio introduced by the foam increases within a certain range as the honeycomb cell size decreases and wall thickness increases, with little influence from the foam's mechanical properties. Water intrusion, leading to ice formation at low temperatures, drastically reduced honeycomb SEA by 94.61 % and impaired mechanical properties significantly. Closed-cell PU in-situ foaming provided exceptional water intrusion resistance, preserving the honeycomb's mechanical performance, including energy absorption and SEA.
作者机构：
[Yan, Hongyu; Xie, SC; Xie, Suchao; Jing, Kunkun] Cent South Univ, Key Lab Traff Safety Track, Minist Educ, Changsha 410075, Peoples R China.;[Yan, Hongyu; Xie, SC; Xie, Suchao; Jing, Kunkun] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China.;[Zhang, Yifan] Tiangong Univ, Inst Composite Mat, Key Lab Adv Text Composite Mat, Minist Educ, Tianjin 300387, Peoples R China.;[Zhou, Hui] Cent South Univ Forestry & Technol, Sch Logist & Transportat, Changsha 410004, Peoples R China.
通讯机构：
[Xie, SC ] C;Cent South Univ, Key Lab Traff Safety Track, Minist Educ, Changsha 410075, Peoples R China.;Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China.
关键词：
3D woven fabrics;3D woven composites;Out-of-plane impact performance;Multi-scale analysis;Engineering applications
摘要：
Three-dimensional(3D) woven composites are widely used as structural materials in fields such as personal protection, aerospace, automotive, and rail vehicles due to their lightweight, high strength, superior out-of-plane impact performance, and high design flexibility. This systematic review focuses on the impact resistance of 3D woven fabrics and their composites, discussing synthetic fibers, natural fibers, and their hybrid configurations. Particular emphasis is placed on the mechanical response of 3D woven fabrics and composites under out-of-plane impact, with a detailed exploration and comparison of the factors influencing their impact performance. The current status of multiscale simulation analysis methods is also highlighted. Finally, the applications of impact-resistant 3D woven fabrics and composites are summarized and some existing problems in this field are elaborated. Overall, this paper summarizes the key progress and future prospects in the field of 3D woven composites and their behavior under impact loading, aiming to provide new insights into the design of new lightweight impact-resistant structural materials.
Three-dimensional(3D) woven composites are widely used as structural materials in fields such as personal protection, aerospace, automotive, and rail vehicles due to their lightweight, high strength, superior out-of-plane impact performance, and high design flexibility. This systematic review focuses on the impact resistance of 3D woven fabrics and their composites, discussing synthetic fibers, natural fibers, and their hybrid configurations. Particular emphasis is placed on the mechanical response of 3D woven fabrics and composites under out-of-plane impact, with a detailed exploration and comparison of the factors influencing their impact performance. The current status of multiscale simulation analysis methods is also highlighted. Finally, the applications of impact-resistant 3D woven fabrics and composites are summarized and some existing problems in this field are elaborated. Overall, this paper summarizes the key progress and future prospects in the field of 3D woven composites and their behavior under impact loading, aiming to provide new insights into the design of new lightweight impact-resistant structural materials.
期刊：
Expert Systems with Applications,2025年261:125532 ISSN：0957-4174
通讯作者：
Liu, RH
作者机构：
[Zhao, Chengwei] Cent South Univ Forestry & Technol, Sch Business, Changsha, Peoples R China.;[Liu, Ruihuan] Cent South Univ Forestry & Technol, Sch Logist, Changsha, Peoples R China.;[He, Jishan; Xu, Xuanhua] Cent South Univ, Sch Business, Changsha, Peoples R China.
通讯机构：
[Liu, RH ] C;Cent South Univ Forestry & Technol, Sch Logist, Changsha, Peoples R China.
关键词：
Multi-attribute group decision-making;Fuzzy heterogeneous preferences;Dual interaction of attributes and alternatives;Interaction non-additive fusion;Shapley-Choquet integral
摘要：
Heterogeneous preferences and the dual interaction of attributes and alternatives are two important features in real-world multi-attribute group decision-making (MAGDM). However, fewer MAGDM studies simultaneously consider heterogeneous preferences and dual interaction, reducing method efficacy and practicability. This study proposes a novel fuzzy heterogeneous MAGDM method considering the dual interaction of attributes and alternatives. First, a similarity-based optimization method is constructed to process fuzzy heterogeneous preferences with triangular fuzzy numbers (TFNs), intuitionistic fuzzy numbers (IFNs), and interval-valued intuitionistic fuzzy numbers (IVIFNs), decreasing the complexity and subjective fuzziness of processing. The dual interaction of attributes and alternatives is objectively measured by constructing the alternatives deviation optimization model and multi-attribute alternative interaction network, which is the inaugural achievement in MAGDM. Next, the novel Shapley-Choquet dual interaction superiority integral is defined to fuse non-additive preferences, overcoming the absolute superiority hypothesis of alternative ranking and improving ranking accuracy. Finally, an application study of hot dry rock siting, along with a detailed analysis and discussion, demonstrate the effectiveness, reliability and superiority of the proposed MAGDM method. The TDDV (total discrimination degree based on variance) respectively increased by 52.381%, 99.259%, and 99.647% in the three comparison cases, revealing the significant role of fuzzy heterogeneous preferences and dual interaction of attributes and alternatives in improving the accuracy and reliability of MAGDM and should be simultaneously considered in MAGDM modeling.
关键词：
Nonclassical diffusion equation;time-dependent global attractor;nonlinear delay;arbitrary polynomial growth
摘要：
In this paper, we mainly investigate the asymptotic behavior of solutions for the nonclassical diffusion problem with hereditary effects and time-dependent perturbed parameter. The main novelty is that the delay term may be driven by a function under very minimal assumptions, namely, measurability and the fact that the phase space is a time-dependent space of functions that are continuous in time. The existence and regularity of time-dependent global attractors are proved by using a new analytical technique. It is remarkable that the nonlinearity $ f $ has no restriction on the upper growth.
摘要：
Top logistics enterprise plays an essential supporting role in the city’s sustainable development. According to the 2019–2021 list of China’s A-level logistics enterprises, this paper aims to explore the spatial heterogeneity of the factors influencing A-level logistics enterprises over time by using a multi-scale geographically weighted regression model. In addition, the spatial evolution characteristics of A-level logistics enterprises were analyzed through spatial visualization and density estimation methods. The results show that: (1) The spatial evolutionary pattern of A-level logistics enterprises in Chinese cities demonstrates simultaneous agglomeration-diffusion dynamics. (2) The MGWR model provides an innovative scientific approach to the study of spatial heterogeneity in logistics. Its regression results are more reliable. (3) Different factors have different scales and intensities of action in different periods and regions over time. In terms of time-series characteristics, the intensity and range of the influence of the 2021 significance indicators all expanded compared to 2019, with a fluctuating upward trend in the intensity of impact. At the same time, the variability in the intensity of the impact is more pronounced across cities. This study stresses the factors influencing the spatial heterogeneity of A-level logistics enterprises have different scales and intensities of action with cities over time and in different years, providing new insights for sustainable development of logistics enterprises and logistics location theory in heterogeneity research. By identifying key influencing factors, this study supports the planning of sustainable logistics enterprises.
摘要：
The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development pattern.In today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.
The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development pattern.In today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.
摘要：
With the intensification of global resource competition, the issue of timber supply has escalated from an economic concern to a significant strategic challenge. This study focuses on the evolution of disruption resilience in the global trade network for wood forest products, aiming to reveal the patterns of resilience dynamics under disruption risks by simulating underload cascading failure phenomena. The study provides theoretical support for enhancing the security and stability of the global wood forest product supply chain. Utilizing global trade data from the UN Comtrade Database 2023, a directed weighted complex network model was constructed, spanning upstream, midstream, and downstream sectors, with trade intensity distances serving as edge weights. By developing an underload cascading failure model, the evolution of disruption resilience was simulated under various disruption scenarios from 2002 to 2023, and the long-term impacts of critical node failures on network performance were analyzed. The results demonstrate significant spatiotemporal heterogeneity in the disruption resilience of the global wood forest product trade network. The upstream network exhibits improved resilience in total node strength but reduced global efficiency. The midstream network shows marked volatility in resilience due to external shocks, such as the global financial crisis, while the downstream network remains relatively stable. Simulations reveal that failures in core nodes (e.g., China, the United States, and Germany) disproportionately degrade global efficiency and node strength, with node centrality metrics positively correlated with network performance loss. This study elucidates the evolutionary mechanisms of disruption resilience in the wood forest product trade network under risk propagation, offering actionable insights for optimizing network robustness and supply chain stability. It is recommended that policymakers promote green supply chain initiatives, accelerate afforestation projects, and enhance domestic timber self-sufficiency to reduce reliance on imported timber, thereby strengthening node resilience and fostering sustainable forest resource utilization for economic and environmental benefits.
期刊：
Journal of Cleaner Production,2025年518:145742 ISSN：0959-6526
通讯作者：
Huang, XY;Shao, LG
作者机构：
[Tian, Wujun] Cent South Univ Forestry & Technol, Coll Comp Sci & Math, Changsha 410004, Peoples R China.;[Li, Yihua; Huang, Xiangyu; Wang, Zhongwei] Cent South Univ Forestry & Technol, Coll logist, Changsha 410004, Peoples R China.;[Li, Yihua; Huang, Xiangyu; Wang, Zhongwei] Cent South Univ Forestry & Technol, Coll Econ & Management, Changsha 410004, Peoples R China.;[Shao, Liuguo] Cent South Univ, Business Sch, Changsha 410083, Peoples R China.;[Shao, Liuguo] Cent South Univ, Inst Met Resources Strategy, Changsha 410083, Peoples R China.
通讯机构：
[Shao, LG ; Huang, XY ] C;Cent South Univ Forestry & Technol, Coll logist, Changsha 410004, Peoples R China.;Cent South Univ Forestry & Technol, Coll Econ & Management, Changsha 410004, Peoples R China.;Cent South Univ, Business Sch, Changsha 410083, Peoples R China.;Cent South Univ, Inst Met Resources Strategy, Changsha 410083, Peoples R China.
摘要：
This study aims to assess the resilience of the global pulp trade network by applying complex network modeling methods. A directed weighted network model was constructed based on UN commodity trade data from 2002 to 2023, and a dynamic resilience evaluation framework based on an underload cascading failure model was proposed, systematically revealing both static and dynamic resilience characteristics during the network’s spatiotemporal evolution. Findings show that the global pulp trade network exhibits significant spatiotemporal evolution: while the number of nodes has decreased, the number of edges continues to increase, reinforcing the dominance of core countries such as China and Brazil. The multipolar trend has led to the formation of five major communities. Although total trade volume remains stable—reflecting the industrial necessity of pulp—different external shocks have distinct impacts: the financial crisis triggered delayed effects through market adjustments, whereas the outbreak of the pandemic initiated immediate institutional interventions. Static resilience analysis reveals significantly improved network transmission efficiency, with high-trade-volume countries becoming less clustered. Assortativity patterns indicate continued dependency of smaller countries on central nodes, while power-law fitting suggests weakened hierarchy and increased robustness against targeted attacks due to diversified structures. Notably, China’s role as a hub has grown rapidly, with its incoming strength increasing by 5.14 times and weighted betweenness centrality rising by 3.54 times. Cascading failure simulation further demonstrates that the failure of key nodes, such as Brazil, may trigger large-scale chain reactions, directly causing trade reductions in 32 countries. Under targeted attack scenarios, network performance loss far exceeds random disruptions, confirming the structural risks associated with supply concentration. Dynamic resilience optimization shows that reducing failure thresholds can enhance global efficiency redundancy by 5.57 times and increase the strength of the largest connected subgraph by 3.59 times, offering quantitative support for resilience-enhancing strategies. Moreover, external shocks exhibit two distinct modes of impact on network resilience: the financial crisis induced gradual adaptation via market mechanisms, while the pandemic triggered emergency response mechanisms, highlighting the critical role of institutional intervention in supply chain stability and revealing fundamental differences between market-driven and policy-driven resilience pathways. The innovations of this research lie in the first application of an underload cascading failure model to pulp trade network resilience assessment, breaking through the limitations of traditional static analyses. It also constructs a dynamic resilience evaluation framework integrated with this model, enabling end-to-end analysis from failure simulation to resilience quantification. By transforming time-series responses into quantifiable metrics through cumulative integration, this work addresses the lack of standardized resilience indicators in forest product trade networks. The findings provide scientific support for policies related to supply chain security, sustainable forest management, and cleaner production practices.
This study aims to assess the resilience of the global pulp trade network by applying complex network modeling methods. A directed weighted network model was constructed based on UN commodity trade data from 2002 to 2023, and a dynamic resilience evaluation framework based on an underload cascading failure model was proposed, systematically revealing both static and dynamic resilience characteristics during the network’s spatiotemporal evolution. Findings show that the global pulp trade network exhibits significant spatiotemporal evolution: while the number of nodes has decreased, the number of edges continues to increase, reinforcing the dominance of core countries such as China and Brazil. The multipolar trend has led to the formation of five major communities. Although total trade volume remains stable—reflecting the industrial necessity of pulp—different external shocks have distinct impacts: the financial crisis triggered delayed effects through market adjustments, whereas the outbreak of the pandemic initiated immediate institutional interventions. Static resilience analysis reveals significantly improved network transmission efficiency, with high-trade-volume countries becoming less clustered. Assortativity patterns indicate continued dependency of smaller countries on central nodes, while power-law fitting suggests weakened hierarchy and increased robustness against targeted attacks due to diversified structures. Notably, China’s role as a hub has grown rapidly, with its incoming strength increasing by 5.14 times and weighted betweenness centrality rising by 3.54 times. Cascading failure simulation further demonstrates that the failure of key nodes, such as Brazil, may trigger large-scale chain reactions, directly causing trade reductions in 32 countries. Under targeted attack scenarios, network performance loss far exceeds random disruptions, confirming the structural risks associated with supply concentration. Dynamic resilience optimization shows that reducing failure thresholds can enhance global efficiency redundancy by 5.57 times and increase the strength of the largest connected subgraph by 3.59 times, offering quantitative support for resilience-enhancing strategies. Moreover, external shocks exhibit two distinct modes of impact on network resilience: the financial crisis induced gradual adaptation via market mechanisms, while the pandemic triggered emergency response mechanisms, highlighting the critical role of institutional intervention in supply chain stability and revealing fundamental differences between market-driven and policy-driven resilience pathways. The innovations of this research lie in the first application of an underload cascading failure model to pulp trade network resilience assessment, breaking through the limitations of traditional static analyses. It also constructs a dynamic resilience evaluation framework integrated with this model, enabling end-to-end analysis from failure simulation to resilience quantification. By transforming time-series responses into quantifiable metrics through cumulative integration, this work addresses the lack of standardized resilience indicators in forest product trade networks. The findings provide scientific support for policies related to supply chain security, sustainable forest management, and cleaner production practices.
摘要：
Multimodal transportation, as an efficient and comprehensive transport mode in the modern logistics system, significantly improves logistics efficiency and reduces costs by integrating various transportation modes. However, due to uncertainties such as weather conditions, logistics companies cannot accurately grasp and predict freight transportation demand. Additionally, the time-sensitivity requirements of multimodal transportation frequently lead to delays in freight transportation. To characterize the randomness and uncertainty of transportation demand parameters, triangular fuzzy numbers are introduced as a descriptive tool. A multiobjective optimization model for low-carbon multimodal transport routing under uncertain demand is constructed, aiming to minimize total transportation cost and carbon emissions. Monte Carlo simulation combined with data-driven methods is employed to sample and analyze uncertain demand, effectively addressing demand uncertainty. To solve the developed model, a novel multi-form competitive swarm optimization algorithm is proposed, which enhances both the quality and efficiency of the solutions. The experimental results indicate that compared to peer algorithms, the proposed algorithm achieves a better tradeoff in diversity, convergence, and feasibility, ultimately obtaining superior performance.
Multimodal transportation, as an efficient and comprehensive transport mode in the modern logistics system, significantly improves logistics efficiency and reduces costs by integrating various transportation modes. However, due to uncertainties such as weather conditions, logistics companies cannot accurately grasp and predict freight transportation demand. Additionally, the time-sensitivity requirements of multimodal transportation frequently lead to delays in freight transportation. To characterize the randomness and uncertainty of transportation demand parameters, triangular fuzzy numbers are introduced as a descriptive tool. A multiobjective optimization model for low-carbon multimodal transport routing under uncertain demand is constructed, aiming to minimize total transportation cost and carbon emissions. Monte Carlo simulation combined with data-driven methods is employed to sample and analyze uncertain demand, effectively addressing demand uncertainty. To solve the developed model, a novel multi-form competitive swarm optimization algorithm is proposed, which enhances both the quality and efficiency of the solutions. The experimental results indicate that compared to peer algorithms, the proposed algorithm achieves a better tradeoff in diversity, convergence, and feasibility, ultimately obtaining superior performance.
作者机构：
[Huang, Qian; Pan, Shuangli; Liao, Huiyu; Jiang, Zehua] Cent South Univ Forestry & Technol, Sch Logist, Changsha, Hunan, Peoples R China.;[Zheng, Guijun] Cent South Univ Forestry & Technol, Business Sch, Changsha, Hunan, Peoples R China.;[Pan, Shuangli] Hunan Key Lab Intelligent Logist Technol, Changsha, Hunan, Peoples R China.
通讯机构：
[Pan, SL ] C;Cent South Univ Forestry & Technol, Sch Logist, Changsha, Hunan, Peoples R China.;Hunan Key Lab Intelligent Logist Technol, Changsha, Hunan, Peoples R China.
摘要：
The effective circulation of fresh agricultural products is conducive to increasing farmers' income and improving the living standards of urban residents. Cold chain storage facilities in agricultural producing areas play an important role in ensuring the quality of agricultural products, extending the freshness period of goods, and improving logistics efficiency. Different types of fresh produce have different requirements for refrigeration and often require transshipment due to quantity constraints. In addition, there are economies of scale in the construction and operation of cold chain storage facilities. Based on the above considerations, with the aim of minimizing the total daily cost, an optimization model for the layout of multi-level cold chain storage facilities is established to determine the number, location, type and capacity of cold chain storage facilities at the same time. Genetic algorithm is chosen to solve the model according to the characteristics of the model. Taking J County of China as an example, the model is proved to have strong operability and applicability. It is of guiding significance and reference value to optimize the layout of cold chain storage facilities in rural areas.
作者机构：
[Zhang, Yinggui; Mo, Weiwei; Xiao, Yang; Wu, Caiyi] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Peoples R China.;[Xiao, Yuxie] Changsha Planning & Design Inst Co Ltd, Engn Consulting Dept, Changsha 410011, Peoples R China.;[Wang, Juan] Cent South Univ Forestry & Technol, Sch Logist, Changsha 410004, Peoples R China.
通讯机构：
[Wang, J ] C;Cent South Univ Forestry & Technol, Sch Logist, Changsha 410004, Peoples R China.
关键词：
oversize and heavyweight cargo;carbon emission;multimodal transportation;genetic algorithm
摘要：
With the increasing global concern over climate change, reducing greenhouse gas emissions has become a universal goal for governments and enterprises. For oversize and heavyweight cargo (OHC) transportation, multimodal transportation has become widely adopted. However, this mode inevitably generates carbon emissions, making research into effective emission reduction strategies essential for achieving low-carbon economic development. This study investigates the optimization of multimodal transportation paths for OHC (OMTP-OHC), considering various direct carbon pricing policies and develops models for these paths under the ordinary scenario-defined as scenarios without any carbon pricing policies-and two carbon pricing policy scenarios, namely the emission trading scheme (ETS) policy and the carbon tax policy, to identify the most cost-effective solutions. An enhanced genetic algorithm incorporating elite strategy and catastrophe theory is employed to solve the models under the three scenarios. Subsequently, we examine the impact of ETS policy price fluctuations, carbon quota factors, and different carbon tax levels on decision-making through a case study, confirming the feasibility of the proposed model and algorithm. The findings indicate that the proposed algorithm effectively addresses this problem. Moreover, the algorithm demonstrates a small impact of ETS policy price fluctuations on outcomes and a slightly low sensitivity to carbon quota factors. This may be attributed to the relatively low ETS policy prices and the characteristics of OHC, where transportation and modification costs are significantly higher than carbon emission costs. Additionally, a comparative analysis of the two carbon pricing policies demonstrates the varying intensities of emission reductions in multimodal transportation, with the ranking of carbon emission reduction intensity as follows: upper-intermediate level of carbon tax > intermediate level of carbon tax > lower-intermediate level of carbon tax = ETS policy > the ordinary scenario. The emission reduction at the lower-intermediate carbon tax level (USD 8.40/t) matches that of the ETS policy at 30%, with a 49.59% greater reduction at the intermediate level (USD 50.48/t) compared to the ordinary scenario, and a 70.07% reduction at the upper-intermediate level (USD 91.14/t). The model and algorithm proposed in this study can provide scientific and technical support to realize the low-carbonization of the multimodal transportation for OHC. The findings of this study also provide scientific evidence for understanding the situation of multimodal transportation for OHC under China's ETS policy and its performance under different carbon tax levels in China and other regions. This also contributes to achieving the goal of low-carbon economic development.
摘要：
An Electric Vehicle (EV) is an appropriate substitution for traditional transportation means for diminishing greenhouse gas emissions. However, decision-makers are beset by the limited driving range caused by the low battery capacity and the long recharging time. To resolve the former issue, several transportation companies increases the travel distance of the EV by establishing recharging stations in various locations. The proposed Electric Vehicle-Routing Problem with Time Windows (E-VRPTW) and recharging stations are constructed in this context; it augments the VRPTW by reinforcing battery capacity constraints. Meanwhile, super-recharging stations are gradually emerging in the surroundings. They can decrease the recharging time for an EV but consume more energy than regular stations. In this paper, we first extend the E-VRPRTW by adding the elements of super-recharging stations. We then apply a two-stage heuristic algorithm driven by a dynamic programming process to solve the new proposed problem to minimize the travel and total recharging costs. Subsequently, we compare the experimental results of this approach with other algorithms on several sets of benchmark instances. Furthermore, we analyze the impact of super-recharging stations on the total cost of the logistic plan.
关键词：
Green computing;Cloud workflow;Large-scale scheduling;Evolutionary algorithm;Multi-objective optimization
摘要：
Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm , namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.
Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm , namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.
摘要：
The aim of this study was to develop a high-performance energy-absorbing device with both high energy absorption and smooth energy dissipation. Drawing upon the concept of exploiting temporal misalignment of impact force, a novel composite energy-absorbing device with staggered combination of cutting rings (CECR) was investigated. Dynamic impact tests were conducted using a drop hammer system, and a finite element model of CECR was built to study its application in railway vehicles. The study showed that the cutting rings fail into filamentous fine circles under impact loads, exhibiting high metal utilization efficiency. Under the influence of staggered combination of cutting rings, CECR demonstrated impact force misalignment compensation, with smooth impact forces. The average impact force reached 351.59 kN, with a maximum energy absorption of 195.37 kJ. The FE simulation model of CECR provided good simulation of failure modes, impact force, and energy absorption. Application of CECR to railway vehicles, with a collision simulation of the entire vehicle at 36 km/h, showed a 91.14% increase in steady-state impact force and a significant improvement in passive safety protection capability. CECR can provide design concepts and guidance for the development of energy-absorbing devices with smooth energy absorption characteristics.
关键词：
Cloud computing;Workflow scheduling;Evolutionary optimization;Constraint handling;Energy-efficient scheduling;Dynamic voltage and frequency scaling
摘要：
Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting the evolution of task runtime to leverage the dynamic voltage and frequency scaling (DVFS) technology for further energy saving. To compensate for that deficiency, this paper designs a tri-chromosome-based evolutionary algorithm, namely TCEA, to evolve three types of decision vectors (i.e., task order, task and resource mapping, and task runtime) simultaneously using three problem-specific mechanisms. Firstly, we construct a search space by using the tasks’ minimum and optimal runtime, and propose a solution representation mechanism to simplify the decision vector for task runtime between 0 and 1. Secondly, we design a deadline constraint handling mechanism to distribute those durations exceeding the deadline to each task based on their extension of the minimum runtime. Thirdly, we exploit the workflow structure to cluster decision variables without direct constraints into the same group. During each iteration, only the order of tasks within a group evolves to avoid precedence constraints, thus performing searches within the feasible space. At last, we conduct comparison experiments on five types of real-world workflows with 30 to 1000 tasks. The energy consumed by TCEA is much less than those consumed by the state-of-the-art workflow scheduling algorithms, demonstrating the superior performance of TCEA in energy saving.
Cloud computing is increasingly attracting workflow applications, where workflows need to satisfy execution deadlines and energy consumption is to be minimized. So far, numerous studies have adopted evolutionary algorithms to optimize the energy consumption of workflow execution. Dynamic voltage and frequency scaling (DVFS) has been widely employed to save energy on computing devices running workflow tasks. However, most existing evolutionary algorithms focus on evolving task execution order or mapping from tasks to resources, while neglecting the evolution of task runtime to leverage the dynamic voltage and frequency scaling (DVFS) technology for further energy saving. To compensate for that deficiency, this paper designs a tri-chromosome-based evolutionary algorithm, namely TCEA, to evolve three types of decision vectors (i.e., task order, task and resource mapping, and task runtime) simultaneously using three problem-specific mechanisms. Firstly, we construct a search space by using the tasks’ minimum and optimal runtime, and propose a solution representation mechanism to simplify the decision vector for task runtime between 0 and 1. Secondly, we design a deadline constraint handling mechanism to distribute those durations exceeding the deadline to each task based on their extension of the minimum runtime. Thirdly, we exploit the workflow structure to cluster decision variables without direct constraints into the same group. During each iteration, only the order of tasks within a group evolves to avoid precedence constraints, thus performing searches within the feasible space. At last, we conduct comparison experiments on five types of real-world workflows with 30 to 1000 tasks. The energy consumed by TCEA is much less than those consumed by the state-of-the-art workflow scheduling algorithms, demonstrating the superior performance of TCEA in energy saving.
