Vehicle Capacity Research Articles

Damage analysis of solid propellants with default defects based on macro-microscopic approach

Solid Rocket Motors (SRMs) rely on solid propellants, directly impacting the energy capacity of launch vehicles. High energy density material (HEDM) propellant exhibits superior energy density owing to the incorporation of a substantial quantity of energetic particles. However, the elevated particle fill ratio makes the formation of pores more prevalent during the manufacturing process of propellants. These pores, representing default defects, are indispensable factors in the investigation of mechanical properties of propellants. This study investigated default defects effects on HEDM propellants, analyzing both microscopic and macroscopic perspectives. Utilizing micro-CT imaging, the microstructure and default defects were characterized. Subsequently, using the cohesive zone model (CZM), microscopic damage evolution was depicted with the representative volume element (RVE) model, allowing in-depth analysis. Furthermore, a damage constitutive model, based on microscopic damage, was developed, with its softening function calibrated using microscopic damage evolution characteristics. Implemented into ABAQUS via user-defined material subroutine (UMAT), this model enabled the prediction of the mechanical response of HEDM propellants with different defects. The results demonstrate that the developed model accurately predicts the mechanical responses of HEDM propellants with different defects, showcasing the potential of macro-microscopic approaches in analyzing propellants with default defects.

Penerapan Algoritma Tabu Search pada Capacitated Vehicle Routing Problem Pengangkutan Sampah di Kota Padang Sidempuan

Waste transportation activities are an example of a form of Capacitated Vehicle Routing Problem (CVRP) related to finding the minimum route. The Tabu Search algorithm is one of the metaheuristic methods that can guide the heuristic local search procedure to explore the solution area outside the local optimal point. The Tabu Search algorithm can be used to find the optimal VRP solution, namely the route that has the minimum total mileage by considering vehicle capacity. The purpose of this research is to determine the optimal route for garbage transportation in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm. Based on the results of the study, it is concluded that the optimal route for transporting waste in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm obtained the shortest route in iteration 1 with the route (12-11-10-9-8-7-6-5-4-3-2-1-0) and route length 16.55 km.

Optimizing perishable food products across states: A multi objective evolutionary algorithm for surplus to deficit transportation

Ensuring the seamless flow of goods from farmer to stakeholder is paramount especially considering the facts such as demand fluctuations, quantity availability, vehicle capacity, inventory limits and truck preferences. In this paper, we proposed a mixed integer linear programming (MILP) model for distributing perishable food goods from surplus states to surplus and deficit state wholesalers in the Indian subcontinent, with the goal of minimizing total cost (fixed, variable, and operational) and transportation time. To ensure product delivery on time, we looked at various scenarios including a case with real-life constraints. Solving this computationally complex task with conflicting objectives in a reasonable time is difficult. To handle the aforementioned multi-objective problem, we proposed an evolutionary algorithm based modified non-dominated sorting genetic algorithm II (MNSGA-II). Furthermore, the suggested algorithm’s performance is tested against the most extensively used multi-objective algorithm, Non-dominated Sorting Genetic method-II (NSGA-II). According to the findings, the proposed MNSGA-II outperforms practically all instances.

Application of fuzzy logic control theory combined with target tracking algorithm in unmanned aerial vehicle target tracking

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

PODI: A Private Object Detection Inference framework for autonomous vehicles

Object detection is essential for autonomous vehicles to perceive and understand their environment. The restricted storage and processing capacities of vehicles necessitate the outsourcing of object detection services. However, this may raise concerns regarding the privacy of the uploaded images. Although there have been some studies on privacy-preserving object detection networks, they either lack location privacy protection or involve excessive computational and communication overheads. To address this issue, we propose a private object detection inference framework (PODI), which is based on a Faster R-CNN and aims to protect both classification and location privacy. PODI employs additive secret sharing protocols to support collaborative computation between two edge servers. By using efficient protocols such as secure Maxpool, secure array access, and secure exponent, PODI significantly reduces computational and communication overheads. A theoretical analysis has confirmed the security, correctness, and efficiency of PODI. Extensive experiments were used to demonstrate its security, inference accuracy comparable to plaintext approaches, and lower cost of secure inference.

Assessing isolation capacity of heavy vehicle’s control air suspensions with three different control cases

Assessing isolation capacity of heavy vehicle’s control air suspensions with three different control cases

Alleviating bus bunching via modular vehicles

The notorious phenomenon of bus bunching prevailing in uncontrolled bus systems produces irregular headways and downgrades the level of service by increasing passengers’ expected waiting time. Modular autonomous vehicles (MAVs), due to their ability to split and merge en route, have the potential to help both late and early buses recover from schedule deviation while providing continuous service. In this paper, we propose a novel bus bunching alleviation strategy for MAV-aided transit systems. We first consider a soft vehicle capacity constraint and establish a continuum approximation (CA) model (Model I) to capture the system dynamics intertwined with the MAV splitting and merging operations, and then establish an infinite-horizon stochastic optimization model to determine the optimal splitting and merging strategy. To capture the reality that passengers may fail to board an overcrowded bus, we propose a second model (Model II) by extending Model I to accommodate a hard vehicle capacity constraint. Based on the characteristics of the problem, we develop a customized deep Q-network (DQN) algorithm with multiple relay buffers and a penalized ruin state applicable for both models to optimize the strategy for each MAV. Numerical results show that the strategy obtained via the DQN algorithm is an effective bunch-proof strategy and has a better performance than the myopic strategy for MAV-aided systems and the two-way-looking strategy for conventional bus systems. Sensitivity analyses are also conducted to examine the effectiveness and benefits of the proposed strategy across different operation scenarios.

Sizing of Energy Storage Systems in Electric Vehicles based on Battery-Supercapacitor Technology

The purpose of this research is to determine the ideal hybrid energy storage system (HESS) size with the goal to improve the effectiveness and efficiency of combined battery and supercapacitor energy storage in electric vehicles. The research uses Mixed Integer Linear Programming (MILP) to determine the most suitable configurations using simulation data from a modeled electric vehicle. The results show that MILP works at identifying the specific capacity needs of the storage system, which change based on the vehicle's power and energy capacity. The innovation provides a paradigm for the development of sustainable and highly efficient electric vehicles in the future, while also enhancing the functionality of current electric vehicles.

Resilience-Based Approach to the Measurement of Headway Inconsistency

Maintaining a consistent headway is a primary goal in bus operation, but it poses a significant challenge because of fluctuating traffic conditions and uneven passenger demand. This leads to inconsistent headways and even bus bunching, resulting in negative consequences such as increased travel time and reduced vehicle capacity. Various control strategies have been developed to alleviate headway inconsistencies, but most of them have focused on whether an inconsistency occurs while overlooking its duration. This study proposes a resilience-based approach to measure headway inconsistencies considering both the depth and duration of the impacts with deviations from the optimal headway. The approach is devised to analyze the cumulative impacts of inconsistent headways comprehensively. It goes beyond addressing only extreme cases, such as bus bunching, and considers persistently uneven headways. We first identify vehicle trajectories from smart card data, then compute deviations from the center of the preceding and following vehicles as an index of impact. Using the concept of resilience, cumulative impacts are measured and their distribution is utilized to set the failure criterion. The approach is applied to 320 bus lines in Seoul to measure the deviations of headways and is used to analyze the characteristics of the bus lines and links where inconsistencies frequently occur. The proposed method enables transportation planners to identify factors that can lead to headway inconsistency, facilitating tailored and data-informed planning processes. This study provides a comprehensive framework that recognizes line-specific and link-specific factors, thereby contributing to enhancing the reliability and efficiency of public transportation systems.

Efficient Route Planning for Flour Distribution: A Heuristic Approach

This company operates in the Food and Beverage sector, selling Martabak, an Indonesian snack. The company needs to deliver flour to 25 branches spread across Bandung, Indonesia, every day, with varying demand capacities at each branch. Each day, the total amount of flour to be delivered is 377 boxes. Currently, the company has 12 distribution routes with a total distance of 271,16 km, with each route covering only two stores. However, these routes are too many and vehicle capacities need to be optimized, resulting in a high total travelled distance. The aim of this research is to optimize the delivery routes to reduce the total travelled distance and number of routes. To address this issue, known as the Vehicle Routing Problem (VRP), heuristic methods were employed, specifically the nearest neighbour method for initial route construction and Intra-Route (1-0) Insertion for improvement. Implementing these heuristic methods resulted in a final total of 6 distribution routes with a total distance of 160,90 km. This method successfully reduced the total travelled distance by 110,26 km and reduced the number of routes by 6. In conclusion, the application of heuristic methods in solving the VRP for this company proved to be effective in reducing both the total travelled distance and the number of routes, thereby enhancing the operational efficiency of the delivery process.

Study of the payload extraction trajectory heavy class carrier rocket

Relevance. As the weight and complexity of the payload that needs to be launched into orbit increases, the relevance of rational trajectory selection to ensure maximum efficiency and minimum costs for delivering the payload to a given orbit increases. Rational choice of the trajectory of a heavy-class launch vehicle has a number of important practical applications. Firstly, it allows you to increase the payload capacity of the launch vehicle and reduce the cost of delivering payload to the target orbit. This is especially important in the context of the development of the space industry, when more and more companies and organizations are showing interest in launching their own satellites and other spacecraft in conditions of fierce economic competition. Choosing a rational trajectory for launching a payload into orbit will significantly reduce the cost of launches and make them available to a wider range of potential customers. Secondly, the choice of launch vehicle trajectory parameters is important for ensuring safety and minimizing risks during spacecraft launches. Thanks to the rational choice of trajectory, it is possible to reduce adverse impacts on the environment and eliminate the possibility of emergency situations associated with loss of control over the flight of the launch vehicle. Rational selection of launch vehicle trajectory parameters is a complex task that requires comprehensive research and consideration of various factors, such as aerodynamic parameters of the atmosphere, mass and characteristics of the payload (spacecraft), engine operating parameters, characteristics of the target orbit, features of the launch of the launch vehicle and many other factors. A more thorough and systematic study of the influence of these parameters will significantly improve the efficiency and reliability of launching spacecraft into orbit. Thus, the choice of rational parameters for the launch vehicle trajectory is a relevant and important topic for scientific research. Increasing the rocket's payload capacity, reducing the cost of delivering a spacecraft to a given orbit, and ensuring launch safety are tasks that depend on the chosen shape and parameters of the rocket's trajectory. Such research has important practical significance and can become the basis for the development of new technologies and methods in the space industry. The purpose of the study is to study and select rational parameters for the trajectory of a heavy-class launch vehicle when launching a payload. The main task is to determine the flight path parameters that will allow achieving maximum efficiency and accuracy in delivering the payload to a given orbit. To achieve the goal of the study, an analysis of various factors influencing the launch parameters of the spacecraft is required, such as the structural and aerodynamic characteristics of the rocket, the influence of aerodynamic factors and the Earth’s gravitational field on the flight path. Taking these factors into account, numerical calculations were carried out on the basis of a system of differential equations of motion using a computer program created in the MAPLE software package. Based on calculations, modeling of the shape and parameters of the launch vehicle flight path was carried out. Research results. During the study, the rational parameters of the trajectory of a heavy-class launch vehicle were selected. Calculations were carried out using numerical modeling of the parameters of payload launch trajectories, and an analysis of the resulting trajectories was carried out. Minimizing the rocket's flight time was identified as the main criterion for the rational choice of trajectory, which allows increasing launch efficiency and saving energy resources. An increase in payload mass and minimization of fuel consumption were adopted as additional criteria. Conclusion. The procedure for choosing rational parameters for the trajectory of a heavy-class launch vehicle proposed in this work will improve the delivery accuracy and reliability of spacecraft launches at the stage of ballistic analysis when designing rockets. The results of the study have practical significance for the development of future heavy-duty launch vehicle missions and improving the efficiency of space launches.

A practical vehicle routing problem in small and medium cities for fuel consumption minimization

Today, reducing fossil fuel consumption in the transport sector is one of the important requirements to preserve the ecological environment. In this paper, we investigated a logistics problem in small and medium cities. We proposes the time-dependent vehicle routing problem model in small and medium cities considering heterogeneous fleets, time windows, multi-trip, time dependency, and road network. We conducted a numerical analysis on fuel consumption minimizing path selection between two customer nodes and found that the path selection is independent of the payload in most cases. The model is formulated as a mathematical integer linear programming model, and small problems are solved using a commercial optimization solver. For large-scale instances, we proposed a heuristic algorithm based on the variable neighborhood search and evaluated its performance. The proposed algorithm can decrease fuel consumption by 25 % compared to manual routing and scheduling. Furthermore, for provide useful managerial insights, sensitivity analyses were performed considering different vehicle capacities.

Enhancing payload capacity to low earth orbit via a relay solution trajectory with laser-powered vehicles

ABSTRACT Low Earth Orbit (LEO) stands as the primary access point for deep space exploration endeavors, encompassing a variety of space missions such as communication satellites, Earth observation platforms, and scientific research missions. In light of the expanding scope of human activities in space, the cost associated with accessing LEO has emerged as a critical focal point for spacecraft designers. Within the spectrum of proposed solutions, laser-powered vehicle has emerged as a viable avenue toward achieving cost-effective launch options. In order to significantly augment the payload capacity of laser-powered launch vehicles destined for LEO, this study introduced a novel Relay Solution Trajectory Flight Scheme. Central to this scheme was the implementation of a relay mirror system affixed to a satellite, which served to stabilize the incident angle of the laser beam directed toward the laser-powered vehicle, thereby ensuring continuous alignment between the laser beam and the vehicle’s axis throughout the duration of the flight. Following the development of comprehensive structural, propulsive, aerodynamic, and trajectory models tailored for the laser-powered vehicle, an optimization of its flight trajectory was conducted, comparing this innovative strategy with two conventional flight strategies. The outcomes of this analysis showcased a notable enhancement of 269.29% in payload capacity compared to conventional strategies that lacked a relay satellite system, within a designated 400 km LEO flight scenario. This endeavor underscored the transformative potential of advanced relay systems in reshaping payload delivery mechanisms and elevating mission feasibility levels, notwithstanding the intricate coordination challenges posed by intricate satellite systems.

Hardness of Pricing Routes for Two-Stage Stochastic Vehicle Routing Problems with Scenarios

On the Difficulty of Pricing Routes for Stochastic Vehicle Routing Problems Many approaches exist for dealing with the uncertainty in the vehicle routing problem with stochastic demands (VRPSD), but the most popular approach models the VRPSD as a two-stage stochastic program where a recourse policy prescribes actions that handle when the realized demands exceed the vehicle capacity. Similarly to other VRP variants, some state-of-the-art algorithms for the VRPSD use set-partitioning formulations that generate variables (routes) via a pricing problem. All of these algorithms, however, have strong assumptions on the probability distribution of customer demands, a simplification that might not be realistic in some applications. In “Hardness of Pricing Routes for Two-Stage Stochastic Vehicle Routing Problems with Scenarios,” Ota and Fukasawa examine the challenges associated with solving the pricing problem of the VRPSD when the customer demands are given by scenarios. They demonstrate that the VRPSD pricing problem is strongly NP-hard for a wide variety of recourse policies and route relaxations. This highlights the difficulty of developing efficient pricing algorithms for the VRPSD with scenario-based demand models.

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Optimization Model for Relief Distribution After Flood Disaster

Logistics planning is critical and a key component in meeting initial emergency needs in the aftermath of a disaster. The rapid and efficient distribution of logistical aid becomes critically important. In such situations, the construction of temporary depots in strategic locations and the determination of optimal distribution routes play an important role in ensuring that logistics aid can be distributed to the affected areas evenly. In this study, the Multi Depot Vehicle Routing Problem (MDVRP) is used which aims to minimize the total cost of distributing logistics aid which includes shipping costs, vehicle usage costs, temporary depot construction costs, and vehicle travel costs from distribution centers to temporary depots, while still meeting constraints such as logistics aid demand, vehicle capacity, area visits, maximum mileage, and depot construction. This model uses two types of vehicles where vehicle is tasked with carrying logistics aid from the distribution center to the temporary depot and vehicle is tasked with delivering logistics aid directly to the point of demand.

Mechanical properties and failure behavior of additively manufactured Al2O3 lattice structures infiltrated with phenol-formaldehyde resin

The lightweight design and load-bearing capacity of underwater vehicles remain perennial focal points. Ceramic lattice structures (CLSs) offer significant weight reduction while maximizing structural strength; however, their inherent brittleness poses a limitation. To optimize the performance of CLSs for underwater vehicle applications, a biomimetic Al2O3/phenol-formaldehyde (PF) resin composite structure (APCS) was proposed and fabricated by infiltrating additive-manufactured Al2O3 lattice structures (ALSs) with PF. Comprehensive assessments of the quasi-static mechanical properties were conducted using both experimental and simulation methods. The specific compressive strength and specific energy absorption of the APCSs under quasi-static compressive loading exhibited remarkable improvements, with the maximum values achieved from the body-centered cubic (BCC)/PF structure increasing by ∼15.23 and ∼307.93 times, respectively. In contrast to ALSs, the failure process of APCSs was gradual, with the confining pressure introduced by the PF promoting transverse crack propagation and layer-by-layer failure, thereby strengthening the ceramic lattice. Toughing mechanisms (i.e., crack arrest, crack deflection, and branching) were also observed in the APCSs. Furthermore, the simulation results aligned well with the experimental results, providing an in-depth analysis of internal damage and crack propagation. The improvements introduced by the composite structure in this study provide a reliable approach for obtaining lightweight and strong materials, thereby accelerating the application of ceramic-based materials in underwater vehicles.

Peculiarities of determining the capacity of land urban passenger transportation vehicles

Introduction. In the conditions of motorization growth the necessity to improve the quality of passenger service by public transport increases significantly. The determining factor for finding the design capacity of vehicles (TC) is the comfort provided to passengers. On the one hand, in order for public transport to be popular in comparison with private motor transport, it must provide comfortable travel conditions, which are ensured by the provision of free places for sitting and (or) the availability of sufficient space for standing. On the other hand, guaranteed provision of all passengers with seats with a comfortable interval between rows of seats, requires an increase in the number of vehicles to master the same volume of transportation and can lead to a sharp increase in the cost of transportation and fare, at which the economic advantages of public transport over private transport will be less obvious. The aim of the study was to find a compromise calculated occupancy rate of vehicles, at which public transport provides the necessary attractiveness and comfort for passengers and the required carrying capacity.Materials and methods. The methodological basis of the work was general scientific principles and research methods: empirical (expert evaluation) and theoretical (analysis, synthesis, systematization) methods. In order to achieve the set goal, the following tasks were solved: 1. The methods of determining the quality of occupancy quality of the vehicle interior were analyzed. 2. The maximum acceptable capacity of the considered public transportation vehicles was determined. 3. The proposals on the use of the calculated indicator of the density of standing passengers placement in determining the carrying capacity of public transportation are given.Discussion and conclusion. The conducted analysis of manuals on operation, repair, acting in corresponding periods of operation of the chosen vehicles shows that the capacity of vehicles according to norms of manufacturers of vehicles and social standard of the Ministry of Transport does not allow to reach the necessary quality of transportation. The performed research has allowed to establish a comfortable level of occupancy of vehicles for a passenger and a calculated indicator of the density of standing passengers.Practical significance. The established comfortable for passengers occupancy of vehicles can be used in the development and updating of social standards of the Ministry of Transport of the Russian Federation.Originality. There is an assessment of the travel conditions depending on passenger occupancy in different types of public transport vehicles operated in the Russian Federation and the Republic of Belarus.

Technology of regular urban passenger transportation operation improvement

Introduction. The degree of passenger vehicle capacity utilisation affects the economic component of regular urban passenger transport operation, as it depends on the number of passengers transported, which affects the revenue from transportation, as well as on the passenger vehicle capacity, which affects the cost of transportation. One of the tasks formulated in the programme-target documents of the Republic of Belarus is to achieve full selfsufficiency of urban regular passenger transport. One of the ways to ensure this can be maximising the degree of capacity utilisation set by the manufacturer of the passenger vehicle.The aim of the paper is to estimate the degree of passenger vehicle capacity utilisation and dynamics of its change, as well as to develop measures to improve the efficiency of urban regular passenger transport.Materials and methods. When writing the article, the data obtained by a full-scale survey of passenger flows in one of the cities of the Republic of Belarus, conducted in 2019 and in 2024, were used. When writing the article, statistical methods (descriptive statistics, fitting, visualisation, assessment of significance of differences using statistical criteria), analysis of scientific literature and work technology, synthesis of proposals were used.Conclusions. The low degree of utilisation of passenger vehicle capacity is shown. The new technology of the organisation of work of the city passenger transport of regular communication, allowing to increase use of capacity of passenger vehicles and, at the expense of this efficiency of such work is offered.The possibility of further use of the results of scientific work. The received results substantiate necessity of development of scientific toolkit for optimisation of assignment of passenger vehicles to flights taking into account passenger load on them and other restrictions.Practical significance. This article shows the need to develop measures to improve the efficiency of passenger vehicle capacity utilisation for the purposes of increasing the efficiency of urban regular passenger transport.Originality. This paper shows the change in the degree of use of passenger vehicle capacity, shows the factors affecting it, and proposes a new technology for organising the work of urban regular passenger transport. The article will be useful for officials of the management bodies responsible for the organisation of the work of urban passenger transport of regular communication, as well as employees of the enterprises of urban passenger transport, transport scientists.

A Two-Echelon Multi-Trip Capacitated Vehicle Routing Problem with Time Windows for Fresh E-Commerce Logistics under Front Warehouse Mode

Given the swift expansion of fresh e-commerce, the front warehouse mode can respond quickly and ensure the quality of fresh products. However, the complexity of the supply chain structure under front warehouse mode poses a challenge in reducing logistics costs and improving distribution efficiency while meeting consumers’ immediate delivery demands. Therefore, this paper studies the vehicle routing problem of two-echelon fresh e-commerce under front warehouse mode. Considering trans-shipment time constraints between the two echelons and the characteristics of terminal distribution, this paper initially models the vehicle routing problem for front warehouses as a two-echelon multi-trip capacitated vehicle routing problem with time windows. A mixed-integer linear programming model is subsequently established. To solve the model, a hybrid genetic algorithm integrated with neighborhood search is developed. Matrix coding is employed to merge vehicle selection and route assignment decisions. Simultaneously, neighborhood search is applied to enhance the search capability of algorithms, thereby improving the quality of solutions. Furthermore, the effectiveness and efficiency of the model and algorithm are verified through experiments of varying scales. Finally, comparative strategies and sensitivity analysis highlight the advantages of multi-trip strategies and provide insights into the optimal vehicle capacity limit.