Low-level Model Research Articles

SwCUDA: Auto parallel code translation framework from CUDA to ATHREAD for new generation sunway supercomputer

Since specific hardware characteristics and low-level programming model are adapted to both NVIDIA GPU and new generation Sunway architecture, automatically translating mature CUDA kernels to Sunway ATHREAD kernels are realistic but challenging work. To address this issue, swCUDA, an auto parallel code translation framework is proposed. To that end, we create scale affine translation to transform CUDA thread hierarchy to Sunway index, directive based memory hierarchy and data redirection optimization to assign optimal memory usage and data stride strategy, directive based grouping-calculation-asynchronous-reduction (GCAR) algorithm to provide general solution for random access issue. swCUDA utilizes code generator ANTLR as compiler frontend to parse CUDA kernel and integrate novel algorithms in the node of abstracted syntax tree (AST) depending on directives. Automatically translation is performed on the entire Polybench suite and NBody simulation benchmark. We get an average 40x speedup compared with baseline on the Sunway architecture, average speedup of 15x compared to x86 CPU and average 27 percentage higher than NVIDIA GPU. Further, swCUDA is implemented to translate major kernels of the real world application Gromacs. The translated version achieves up to 17x speedup.

Research on Power Investment and Expansion Planning Considering Coal-fired Power Capacity and Electricity Price in Market-Driven Environment

The market-oriented environment and the shift away from the coal-fired power are inevitable trends in the future development of electric power energy. Based on this, this article establishes a dual-level model for power investment expansion planning with wind power system in an oligopoly competitive electricity market. In the upper-level model, power generation companies make investment decisions and formulate their bidding strategies in market transactions, while the lower-level model simulates the day-ahead market clearing process under the centralized bidding and trading mode. To encourage the transition from coal-fired power plants, a model for expected annual operating revenue of coal-fired power companies under a dual pricing system is proposed, and the conditional value-at-risk (CVaR) is used to measure the investment risk brought by the uncertainty of competitors in the investment planning game for power generation companies. The numerical results verify the effectiveness of the proposed model and method.

Deep Analysis of Visual Product Reviews

With the recent shift toward the digital world, online customers are increasing rapidly. To fulfill the market demand, competitive market strategies are introduced in the e-commerce industry. Analyzing customer feedback is becoming one of the integral parts of this market strategy, and indispensable to a service provider. In recent days, it can be noticed that customers upload purchased product images with their review scores. In this article, we undertake the task of analyzing such visual reviews, which is very new of its kind. In the past, the researchers worked on analyzing language feedback. However, here, we do not take any assistance from linguistic reviews that may be absent, since a recent trend can be observed where customers prefer to quickly upload visual feedback through a smartphone instead of typing language feedback. We propose a hierarchical architecture, where the higher-level model engages in product categorization, and the lower-level model pays attention to predicting the review score from a customer-provided product image. We generated a database by procuring real visual product reviews, which was quite challenging. Our architecture obtained some promising results by performing extensive experiments on the employed database. The proposed hierarchical architecture attained a 26.26% more accuracy than the single-level best comparable architecture.

Application of low-altitude wind shear recognition algorithm and laser wind radar in aviation meteorological services

Abstract The rapid development of the aviation industry has attracted great attention to aviation safety, and the identification and early warning of low-level wind shear have become crucial. Therefore, it is also necessary to improve the performance of laser wind radar technology and achieve accurate recognition of low-level wind shear. In this regard, this study is based on laser wind radar technology and uses the K-neighborhood frequency method to denoise radar data. And based on the least squares fitting method, two-dimensional synthetic wind shear is obtained, and a low altitude wind shear recognition model based on laser wind radar is constructed. These test results confirm that the correlation coefficient values of the wind speed fitting curve measured by the laser detection radar on sunny, cloudy, and fog days are 0.978, 0.954, and 0.961, respectively. And the correlation coefficient values of wind direction fitting curve are 0.984, 0.982, and 0.952, respectively. On sunny, cloudy, and fog days, the recognition accuracy values of the low-level wind shear recognition model are 85.92, 82.17, and 72.43%, respectively. And the recognition accuracy values of wind analyzer radar are 70.18, 62.57, and 55.89%, respectively. In practical applications, the low-level wind shear recognition model accurately recognizes the occurrence of low-level wind shear. This study has achieved accurate recognition of low-level wind shear and has better performance compared to other existing radars. It has played an important reference role in aviation safety.

Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets

With the prevalence of cloud computing applications, cloud data centers (CDCs) are proliferating around the world. However, in the context of hybrid energy‑carbon markets environment, the high energy costs and environmental expenses caused by CDC operation are pressing CDC owners to restructure the future development of CDC in a more economical and low-carbon manner. The potential spatio-temporal transferability and reducibility of workloads provides CDCs with significant flexibility in their operations and thus may interact with the power grid as active demand users. Nonetheless, other than private data centers, public CDCs have no direct control over the workloads submitted by terminal cloud users. As such, this paper presents a bi-level model for CDC allocation planning, so as to incorporate cloud service-demand response (CS-DR) from the perspective of a hybrid electricity-carbon market. The upper level pertains to multi-domain resource collaborative planning model, which determines the optimum siting and sizing of CDCs as well as the incentive design for CS-DR program, with the objective of maximizing the total expected benefits of CDC. The lower level models correspond to the market clearing (electricity-carbon tariff model) of Independent system operator (ISO) and the decision-making of cloud users regarding CS-DR participation. The proposed model belongs to a bi-level mixed integer nonlinear programming problem with two non-convex lower levels, which can be intractable in mathematics. To solve such difficult problem, a hybrid solution method combining multiple linearization techniques and reformulation and decomposition (R&D) strategy based on column-and-constraint generation (C&CG) algorithm is developed. The proposed model is demonstrated on a modified IEEE 30-bus test case, and the simulation results verified the effectiveness of the proposed approach.

Infrastructure-Assisted cooperative driving and intersection management in mixed traffic conditions

Infrastructure-assisted cooperative driving, which consists of connected and automated vehicle (CAV) trajectory planning and traffic signal control, can greatly improve the efficiency of signalized intersection operations. In existing studies, the CAV trajectory planning problem is simplified to only modeling longitudinal vehicle behaviors or assuming the lane changing can be executed instantaneously. The generated CAV trajectories are not realistic and can’t be implemented at the vehicle level. Instantaneous lane changing process may also lead to safety issues or uncomfortable driving behavior of following vehicles in a mixed traffic environment. To fill this research gap, we propose an optimization framework that integrates a full CAV trajectory planning model with traffic signal control in a cooperative driving environment with both CAVs and human-driven vehicles (HDVs). A two-level optimization model is formulated based on discrete time. The high-level model optimizes traffic signal parameters, CAV arrival time, and lane assignment at the stop bar, to minimize total vehicle delay. Given arrival time and lane assignment, the low-level model generates trajectories with integrated car-following and lane-changing maneuvers to mimic a human driving policy based on imitation learning. Numerical experiments from a real-world intersection show that the proposed model outperforms adaptive and fixed time signal control by as much as 63.06% in terms of reduction of average vehicle delay, due to better utilization of lane capacity and reduced lost time. In addition to mobility benefits, fuel consumption is also significantly reduced under the proposed infrastructure-assisted cooperative driving framework.

Optimal configuration of dynamic wireless charging facilities considering electric vehicle battery capacity

Dynamic wireless charging facilities deployed on the road network offer an effective charging method to alleviate the electric vehicle users’ range anxiety and thus facilitate the promotion of electric vehicles. The main benefit of wireless charging is that it extends EV’s driving range by enabling EV en-route recharging, which consequently reduces the required battery size for EVs. Basically, given densely distributed wireless charging lanes on the road network, smaller and less expensive batteries would be able to meet the travel demand. From the societal point of view, the reduction of battery size/capacity is capable of justifying more construction of wireless charging lanes. While previous research works on optimal deployment of wireless charging facilities ignored this benefit, this study aims to explicitly consider the tradeoff between the costs of building recharging infrastructure and manufacturing batteries from the societal point of view in the optimal configuration of dynamic wireless charging facilities. The problem is formulated into a bi-level programming model, wherein the upper-level model seeks to minimize the total social cost, and the lower-level model captures EV drivers’ choices in terms of battery size, travel route, and driving and charging behavior. To handle the problem, we first relax the path cost constraint and apply the linearization techniques to reformulate the problem into a mixed-integer linear programming, from which a lower bound of the problem can be obtained. An efficient surrogate optimization algorithm is then developed to solve the problem.

False Data Injection Attack Against the Optimal Energy Management of Active Distribution Networks: A Bilevel Programming Model

With the wide integration of advanced measurement equipment and communication networks into active distribution networks (ADNs), the ADN cyber-physical system (CPS) is facing the risk of false data injection attacks (FDIAs). This paper proposes a bilevel attack model to obtain optimal false data, which maximizes power losses, photovoltaic (PV) curtailment, and load shedding while ensuring the concealment of the attack. Different from the FDIA for the transmission networks, the FDIA targeting ADNs not only injects false data into load demand but also further injects false data into PV output, achieving an attack through the cooperation of the two types of false data injection. Moreover, differing from a single-level attack model, the proposed bilevel model takes the optimal energy management model executed by the ADN system operators as the lower-level model, using a master-slave game to obtain the optimal false data that considers operator responses, making it more in line with practical engineering. Using the Karush-Kuhn-Tucker (KKT) conditions, the bilevel model is transformed into a single-level model for ease of solution. Simulation results based on the IEEE 33-bus test case confirm the effectiveness of the proposed model and reveal the risk of the ADN under FDIA.

Coordinated planning for flexible interconnection and energy storage system in low-voltage distribution networks to improve the accommodation capacity of photovoltaic

The increasing proportion of distributed photovoltaics (DPVs) and electric vehicle charging stations in low-voltage distribution networks (LVDNs) has resulted in challenges such as distribution transformer overloads and voltage violations. To address these problems, we propose a coordinated planning method for flexible interconnections and energy storage systems (ESSs) to improve the accommodation capacity of DPVs. First, the power-transfer characteristics of flexible interconnection and ESSs are analyzed. The equipment costs of the voltage source converters (VSCs) and ESSs are also analyzed comprehensively, considering the differences in installation and maintenance costs for different installation locations. Second, a bilevel programming model is established to minimize the annual comprehensive cost and yearly total PV curtailment capacity. Within this framework, the upper-level model optimizes the installation locations and capacities of the VSCs and ESSs, whereas the lower-level model optimizes the operating power of the VSCs and ESSs. The proposed model is solved using a non-dominated sorting genetic algorithm with an elite strategy (NSGA-II). The effectiveness of the proposed planning method is validated through an actual LVDN scenario, which demonstrates its advantages in enhancing PV accommodation capacity. In addition, the economic benefits of various planning schemes with different flexible interconnection topologies and different PV grid-connected forms are quantitatively analyzed, demonstrating the adaptability of the proposed coordinated planning method.

Generation side strategy and user side cost based on equilibrium analysis of the power market under the reliability option

The reliability option (RO) ensures generation capacity adequacy, drawing inspiration from financial product options. However, most recent RO studies overlook the gaming strategies employed by power producers. This paper proposes a bi-level model based on an equilibrium analysis of the power market under RO. The upper-level model aims to maximize the power producers’ profit, whereas the lower-level model focuses on market clearing. Profit calculation considers factors like electricity sales income, option fees income, fuel cost, expansion investment cost, refund cost, and penalties. The bi-level model is reformulated into a mathematical problem with equilibrium constraints (MPEC) for each producer using Karush–Kuhn–Tucker conditions of the lower-level model. We then apply a series of linearization methods. The market equilibrium is obtained through the dynamic game framework for all MPECs. We also provide a comprehensive purchase cost calculation and demonstrate its equivalence to user side surplus. We examine cases from various spot market construction maturities in a provincial area of China. Our results indicate that implementing the RO mechanism and aligning parameters with the proposed optimal RO parameter curve, one can reduce the comprehensive purchase cost.

A Charging Planning Method for Shared Electric Vehicles with the Collaboration of Mobile and Fixed Facilities

Faced with the charging difficulties of free-floating shared electric vehicles and the high cost of single-demand mobile charging, this paper proposes a cooperative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the system charging total cost minimization model, which searches for the optimal charging scheme and number of mobile charging vehicles. The lower level model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for the mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% when compared to the fixed-only charging scheme, and by 14.9% when compared to the mobile-only charging scheme.

Match of the cross-period capacity on the third-party shared manufacturing platform with demand delay

In this paper, we aim at the cross-period matching problem on a third-party shared manufacturing platform (TSMP) that provides matching services for capacity demanders (CDs) and capacity suppliers (CSs). Considering the impact of delay in meeting demand, we formulate a bi-level multi-objective optimization model (BMOM) with the objective functions of maximizing the manufacturers’ matching rate and the TSMP’s commission revenue, in which the lower-level model optimizes the matching strategy for the current period, and the upper-level model matches the cross-period demand (i.e., the demand with delay). Then, we propose a method that combines the ε-constraint and a three-stage solution algorithm to solve the BMOM, and the performance of the proposed method is verified by comparing it with the optimization solver CPLEX in numerical experiments. The results of numerical experiments show that the method proposed in the paper has advantages in computational effectiveness and efficiency and can solve the current-period and cross-period joint matching problem of the TSMP, which provides a reference method for TSMPs to optimize the matching strategies.

An Evaluation of Directive-Based Parallelization on the GPU Using a Parboil Benchmark

Heterogeneous architectures consisting of both central processing units and graphics processing units are common in contemporary computer systems. For that reason, several programming models have been developed to exploit available parallelism, such as low-level CUDA and OpenCL, and directive-based OpenMP and OpenACC. In this paper we explore and evaluate the applicability of OpenACC, which is a directive-based programming model for GPUs. We focus both on the performance and programming effort needed to parallelize the existing sequential algorithms for GPU execution. The evaluation is based on the benchmark suite Parboil, which consists of 11 different mini-applications from different scientific domains, both compute- and memory-bound. The results show that mini-apps parallelized with OpenACC can achieve significant speedups over sequential implementations and in some cases, even outperform CUDA implementations. Furthermore, there is less of a programming effort compared to low-level models, such as CUDA and OpenCL, because a majority of the work is left to the compiler and overall, the code needs less restructuring.

COSMOGLOBE DR1 results

We present COSMOGLOBEData Release 1, which implements the first joint analysis of WMAP andPlanckLFI time-ordered data, processed within a single Bayesian end-to-end framework. This framework directly builds on a similar analysis of the LFI measurements by the BEYONDPLANCKcollaboration, and approaches the cosmic microwave background (CMB) analysis challenge through Gibbs sampling of a global posterior distribution, simultaneously accounting for calibration, mapmaking, and component separation. The computational cost of producing one complete WMAP+LFI Gibbs sample is 812 CPU-h, of which 603 CPU-h are spent on WMAP low-level processing; this demonstrates that end-to-end Bayesian analysis of the WMAP data is computationally feasible. We find that our WMAP posterior mean temperature sky maps and CMB temperature power spectrum are largely consistent with the official WMAP9 results. Perhaps the most notable difference is that our CMB dipole amplitude is 3366.2 ± 1.4 μK, which is 11 μK higher than the WMAP9 estimate and 2.5σhigher than BEYONDPLANCK; however, it is in perfect agreement with the HFI-dominatedPlanckPR4 result. In contrast, our WMAP polarization maps differ more notably from the WMAP9 results, and in general exhibit significantly lower large-scale residuals. We attribute this to a better constrained gain and transmission imbalance model. It is particularly noteworthy that theW-band polarization sky map, which was excluded from the official WMAP cosmological analysis, for the first time appears visually consistent with theV-band sky map. Similarly, the long standing discrepancy between the WMAPK-band and LFI 30 GHz maps is finally resolved, and the difference between the two maps appears consistent with instrumental noise at high Galactic latitudes. Relatedly, these updated maps allowed us for the first time to combine WMAP and LFI polarization data into a single coherent model of large-scale polarized synchrotron emission. Still, we identified a few issues that require additional work, including (1) low-level noise modeling; (2) large-scale temperature residuals at the 1–2 μK level; and (3) a strong degeneracy between the absoluteK-band calibration and the dipole of the anomalous microwave emission component. We conclude that leveraging the complementary strengths of WMAP and LFI has allowed the mitigation of both experiments’ weaknesses, and resulted in new state-of-the-art WMAP sky maps. All maps and the associated code are made publicly available through the COSMOGLOBEweb page.

A suggestion-based hierarchical energy efficient control framework for connected and automated vehicles in mixed stream urban road networks

Connected and automated vehicles (CAVs) and their advanced control systems have the ability to improve energy efficiency and mobility of the transportation system, but hundred percent market penetration of CAVs will not happen in the near future. CAVs are thus expected to share the road with human-driven vehicles (HDVs), which can act in suboptimal manners. Since vehicles on road constitute an interactive system, where actions of one vehicle influence the actions of its surrounding vehicles, suboptimal behavior of HDVs affect all their surrounding vehicles including the CAVs. This paper thus focuses on the development of a suggestion-based control framework for CAVs in interactive mixed traffic environments. Our mixed traffic environment includes both CAVs and connected HDVs that have vehicle-to-vehicle (V2V) communication capabilities. In this suggestion-based control framework, a CAV is considered to provide velocity and lane-change suggestions to multiple surrounding HDVs to follow. These suggestions are the velocities and lane-change actions the CAV wants the HDVs to follow to improve its own and the group’s energy efficiency and mobility. To take into account the interactive nature of the environment, the CAV is considered to jointly plan its own trajectory, evaluate the suggestions to the surrounding HDVs, and predict the actions of its surrounding vehicles. The joint planning requires solving the problem in joint state- and action-space, and in this paper, we develop a Monte Carlo Tree Search (MCTS)-based trajectory planning approach for the CAV. Since the joint action- and state-space grows exponentially with the number of agents and can be computationally expensive, we propose an adaptive action-space by pruning the actions resulting in unsafe trajectories for each agent. The trajectory planning approach is followed by a low-level model predictive control (MPC)-based motion controller, which aims at tracking the reference trajectory in an optimal fashion. Simulation studies show the proposed control strategy’s efficacy compared with existing baseline methods. Simulation studies show improvements in fuel efficiency ranging from 4% to 50% for HDVs and from 1% to 30% for the CAV compared to the baseline.

Usage of model combination in computational toxicology

New Approach Methodologies (NAMs) have ushered in a new era in the field of toxicology, aiming to replace animal testing. However, despite these advancements, they are not exempt from the inherent complexities associated with the study's endpoint. In this review, we have identified three major groups of complexities: mechanistic, chemical space, and methodological. The mechanistic complexity arises from interconnected biological processes within a network that are challenging to model in a single step. In the second group, chemical space complexity exhibits significant dissimilarity between compounds in the training and test series. The third group encompasses algorithmic and molecular descriptor limitations and typical class imbalance problems. To address these complexities, this work provides a guide to the usage of a combination of predictive Quantitative Structure-Activity Relationship (QSAR) models, known as metamodels. This combination of low-level models (LLMs) enables a more precise approach to the problem by focusing on different sub-mechanisms or sub-processes. For mechanistic complexity, multiple Molecular Initiating Events (MIEs) or levels of information are combined to form a mechanistic-based metamodel. Regarding the complexity arising from chemical space, two types of approaches were reviewed to construct a fragment-based chemical space metamodel: those with and without structure sharing. Metamodels with structure sharing utilize unsupervised strategies to identify data patterns and build low-level models for each cluster, which are then combined. For situations without structure sharing due to pharmaceutical industry intellectual property, the use of prediction sharing, and federated learning approaches have been reviewed. Lastly, to tackle methodological complexity, various algorithms are combined to overcome their limitations, diverse descriptors are employed to enhance problem definition and balanced dataset combinations are used to address class imbalance issues (methodological-based metamodels). Remarkably, metamodels consistently outperformed classical QSAR models across all cases, highlighting the importance of alternatives to classical QSAR models when faced with such complexities.

An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game

With the growing popularity of charging pile infrastructure and the development of smart electronic devices and 5G communication technologies, the electric vehicle aggregator (EVA) as a bidding entity can aggregate numerous electric vehicle (EV) resources to participate in the electricity market. Moreover, as the number of grid-connected EVs increases, EVA will have an impact on the nodal marginal prices of electricity market clearing. Aiming at the bidding and offering problem of EVA participation in the day-ahead and intra-day electricity markets, based on the Stackelberg game theory, this paper establishes a bilevel optimization model for EVA participation in the two-stage electricity market as a price-maker. In the proposed bilevel model, the upper-level and lower-level models are constructed as an operational problem for EVA and a market-clearing problem for independent system operator (ISO), respectively. In the day-ahead stage, EVA is optimized to maximize its own expected benefits, and ISO aims to improve the social benefits. In the intra-day stage, EVA is optimized to maximize its self-interest, and the ISO aims to make it possible to minimize the cost of expenditures to maintain the system’s supply–demand balance. Karush–Kuhn–Tucker (KKT) conditions and dual theory are used to transform the nonlinear bilevel programming model into a mixed-integer single-level linear programming model. In order to verify the validity of the proposed bilevel model as well as to comparatively analyze the impact of EVA’s participation in the electricity market on the market clearing results. Two scenarios are set up where EVA is seen as the price-taker in Scenario 1 and EVA is seen as the price-maker in Scenario 2. ISO’s revenue under Scenario 2 increased by USD 2262.66 compared to Scenario 1. In addition, the EVA acts as an energy consumer in Scenario 1 with a charging cost of USD 26,432.95, whereas in Scenario 2, the EVA can profit by participating in the electricity market with a revenue of USD 26,432.95, at which point the EVA acts like a virtual power plant. The simulation examples verify that the proposed bilevel optimization model can improve the benefits of ISO and EVA at the same time, achieving mutual benefits for both parties. In addition, the simulation analyzes the impact of abandonment penalty price on ISO and EVA intra-day revenues. Comparing the scenarios where the abandonment penalty price is 0 with USD 10/MW, the ISO’s revenue in the intra-day market decreases by USD 197.5. Correspondingly, EVA’s reserve capacity is dispatched to consume wind power in the intra-day market, and its revenue increases by USD 197.5. The proposed two-stage bilevel optimization model can provide a reference for EVA to develop scheduling strategies in the day-ahead and intra-day electricity markets.

Capacity allocation method of hydrogen-blending natural gas pipeline network based on bilevel optimization

Blending hydrogen into the natural gas pipeline network is an efficient approach for storing and delivering renewable energy. However, the variation in gas quality and the diversity of delivery paths can lead to difficulties in pipeline operation management. In this paper, we propose a bilevel optimization model to understand the influence of hydrogen blending on pipeline capacity allocation and gas transmission tariffs. The lower-level model optimizes the best capacity booking of each shipper, and the upper-level model optimizes the best capacity allocation of the transmission system operator. Detailed transmission paths and gas calorific values at each delivery point are taken into account to calculate the actual gas transmission tariffs. The proposed model is solved by the alternating direction multiplier method and validated by a real-world pipeline network. Results show that: (1) the proposed method can facilitate shippers to meet the energy demand under different hydrogen blending cases, (2) a fair-cost sharing is achieved among all parties, (3) when the hydrogen blending ratio reaches 5 %, the pipeline capacity can be optimally utilized to achieve the maximum pipeline capacity allocation. This approach can guide efficient gas pipeline capacity allocation and facilitate the establishment of trading platforms.

Recovery Strategies for Urban Rail Transit Network Based on Comprehensive Resilience

To enhance the resilience of urban rail transit networks in dealing with interference events and facilitating rapid network recovery, this paper focuses on studying damaged urban rail transit networks and proposes comprehensive resilience evaluation indexes for urban rail transit networks that take into account two dimensions: network topology and passenger travel path selection. A bi-level programming model is constructed to maximize the comprehensive toughness, where the upper-level model is an integer planning model for determining the optimal recovery sequence of the affected stations under interference events that result in station closure or inoperability. The lower-level model is a passenger flow allocation model aiming to minimize travelers’ impedance. A genetic algorithm and Dijkstra’s labeling algorithm are used to solve the upper model as well as the shortest path of the lower model, respectively. Using a real-world urban rail transit network as an example, this research applies different recovery strategies, random recovery, node importance-based recovery, and comprehensive toughness-based recovery, across five common interference scenarios to analyze the recovery sequence of stations in each scenario. The modeling results show that the comprehensive toughness-based restoration strategy yields the most favorable results for the rail transportation network, followed by the node importance-based restoration strategy. In addition, the network’s toughness varies more significantly when employing different restoration strategies during target interference, as compared to the random and range interference scenarios.

Liner shipping network design model with carbon tax, seasonal freight rate fluctuations and empty container relocation

In response to the challenges posed by the shipping carbon tax resulting from the low-carbon environmental policy, the seasonal fluctuation of freight rates in the container liner shipping market, and the reduced flexibility in fleet adjustment, we develop a bi-level programming model to maximize the average revenue per container ship and the total revenue of the fleet.The upper-level model selects the optimal shipping networks for both the off-season and peak season, while also designing the liner fleet for these two networks. The lower-level model optimizes the slot allocation scheme and the empty container storage and transportation scheme to evaluate the revenue of the network calculated by the upper-level model. Three meta-heuristic algorithms are proposed. We take the China-West Europe liner shipping route as the research object, and conduct numerical experiments on different optimization objectives and liner types. Results demonstrate that maximizing the average revenue per container ship involves reducing the carbon tax cost, simplifying the trunk route structure, and increasing the number of feeder routes. These changes lead to reduced satisfaction rates for transportation demands in both China and Europe. If the company seeks to maximize total revenue, they may achieve the opposite result. Therefore, liner companies should reasonably set optimization goals, adjust the network structure and liner operation status in a timely manner, and scientifically allocate container ship and empty containers to achieve the operational goals of reducing carbon emissions and maximizing revenue to cope with the volatile market and the low-carbon regulations.