Search Results (86)

Massive relief supplies inter-regional lateral transshipment (MRSIRLT) can significantly enhance the efficiency of disaster response, meet the needs of affected areas (AAs), and reduce deprivation costs. This paper develops an integrated allocation and intermodality optimization model (AIOM) to address the MRSIRLT challenge. A phased interactive framework incorporating adaptive differential evolution (JADE) and improved adaptive large neighborhood search (IALNS) is designed. Specifically, JADE is employed in the first stage to allocate the volume of massive relief supplies, aiming to minimize deprivation costs, while IALNS optimizes intermodal routing in the second stage to minimize the weighted sum of transportation time and cost. A case study based on a typhoon disaster in the Chinese region of Bohai Rim demonstrates and verifies the effectiveness and applicability of the proposed model and algorithm. The results and sensitivity analysis indicate that reducing loading and unloading times and improving transshipment efficiency can effectively decrease transfer time. Additionally, the weights assigned to total transfer time and costs can be balanced depending on demand satisfaction levels. Full article

Cross-line operation and express–local train coordination are both crucial for enhancing the efficiency of multi-level urban rail transit systems. Most studies address suburban railway operations in isolation, overlooking coordination and inducing supply–demand mismatches that weaken system efficiency. This study addresses the joint optimization of cross-line operation and express–local scheduling by proposing a novel train timetable model. The model determines train service plans and departure times to minimize total system cost, including train operating and passenger travel costs. A space–time network represents integrated train–passenger interactions, and an extended adaptive large neighborhood search (E-ALNS) algorithm is developed to solve the model efficiently. Numerical experiments verify the effectiveness of the proposed approach. The E-ALNS achieves near-optimal solutions with less than 4% deviation from Gurobi. Comparative analysis shows that the proposed hybrid operation mode reduces total passenger travel cost by 6% and improves the cost efficiency ratio by 13% compared to independent operations. Sensitivity analyses further confirm the model’s robustness to variations in transfer walking time, passenger penalties, and waiting thresholds. This study provides a practical and scalable framework for optimizing train timetables in complex cross-line transit systems, offering insights for enhancing system coordination and passenger service quality. Full article

Reference-based super-resolution (RefSR) enhances the detail restoration capability of low-resolution images (LR) by utilizing the details and texture information of external reference images (Ref). This study proposes a RefSR method based on hash adaptive matching and progressive multi-scale dynamic aggregation to improve the super-resolution reconstruction capability. Firstly, to address the issue of feature matching, this chapter proposes a hash adaptive matching module. On the basis of similarity calculation between traditional LR images and Ref images, self-similarity information of LR images is added to assist in super-resolution reconstruction. By dividing the feature space into multiple hash buckets through spherical hashing, the matching range is narrowed down from global search to local neighborhoods, enabling efficient matching in more informative regions. This not only retains global modeling capabilities, but also significantly reduces computational costs. In addition, a learnable similarity scoring function has been designed to adaptively optimize the similarity score between LR images and Ref images, improving matching accuracy. Secondly, in the process of feature transfer, this chapter proposes a progressive multi-scale dynamic aggregation module. This module utilizes dynamic decoupling filters to simultaneously perceive texture information in both spatial and channel domains, extracting key information more accurately and effectively suppressing irrelevant texture interference. In addition, this module enhances the robustness of the model to large-scale biases by gradually adjusting features at different scales, ensuring the accuracy of texture transfer. The experimental results show that this method achieves superior super-resolution reconstruction performance on multiple benchmark datasets. Full article

Adaptive Large Neighborhood Search (ALNS) represents a versatile and highly efficient optimization methodology that has demonstrated significant effectiveness in practical applications. This study introduces an enhanced ALNS approach integrated with Simulated Annealing (SA), termed IALNS-SA. The proposed algorithm incorporates supplementary destruction and repair operators within the ALNS framework to augment its robustness and generalization capacity. Additionally, it adopts the SA acceptance criterion to mitigate local optima entrapment. The research investigates the applicability of IALNS-SA to the Vehicle Routing Problem with Simultaneous Delivery and Pickup and Time Windows (VRPSDPTWs), a pivotal challenge in logistics optimization. Through comprehensive evaluation across 56 large-scale benchmark instances, the algorithm’s performance is systematically compared against four established methods: p-SA, DCS, VNS-BSTS, and DGWO. Empirical results indicate that IALNS-SA achieves superior performance relative to DGWO in 69.64% of cases, surpasses VNS-BSTS in 94.64% of instances, and consistently outperforms both p-SA and DCS. The obtained optimal solutions exhibit reduced total vehicle routing distances, thereby substantiating the operational feasibility and algorithmic efficacy of the proposed methodology. Full article

The spatial separation of port yards and railway hubs, which relies on external truck drayage as a necessary link, hampers the seamless transshipment of sea–rail intermodal containers between ports and railway hubs. This creates challenges in synchronizing yard cranes (YCs) at the port terminal, external trucks (ETs) on the road, and rail-mounted gantry cranes (RMGs) at the railway hub. However, most existing studies focus on equipment scheduling or container transshipment organization under the port–railway integration mode, often overlooking critical time window constraints, such as train schedules and export container delivery deadlines. Therefore, this study investigates the collaborative scheduling of YCs, ETs, and RMGs for synchronized loading and unloading under the port–railway separation mode. A mixed-integer programming (MIP) model is developed to minimize the maximum makespan of all tasks and the empty-load time of ETs, considering practical time window constraints. Given the NP-hard complexity of this problem, an improved genetic algorithm (GA) integrated with a “First Accessible Machinery” rule is designed. Extensive numerical experiments are conducted to validate the correctness of the proposed model and the performance of the solution algorithm. The improved GA demonstrates a 6.08% better solution quality and a 97.94% reduction in computation time compared to Gurobi for small-scale instances. For medium to large-scale instances, it outperforms the adaptive large neighborhood search (ALNS) algorithm by 1.51% in solution quality and reduces computation time by 45.71%. Furthermore, the impacts of objective weights, equipment configuration schemes, port–railway distance, and time window width are analyzed to provide valuable managerial insights for decision-making to improve the overall efficiency of sea–rail intermodal systems. Full article

With the rapid development of e-commerce, the importance of logistics distribution is becoming increasingly prominent. In particular, the last-mile delivery is particularly important because it serves customers directly. Improving customer satisfaction is one of the important factors to ensure the quality of service in delivery and also an important guarantee for improving the market competitiveness of logistics enterprises. In the process of last-mile delivery, flexible delivery locations and variable delivery times are effective means to improve customer satisfaction. Therefore, this paper introduces a Vehicle Routing Problem with flexible time windows and delivery locations, considering customer satisfaction (VRP-CS), which considers customer satisfaction by using prospect theory from two aspects: the flexibility of delivery time and delivery locations. This VRP-CS is formally modeled as a bi-objective optimization problem, which is an NP-hard problem. To solve this problem, a Multi-Strategy Adaptive Large Neighborhood Search (MSALNS) method is proposed. Operators guided by strategies such as backtracking and correlation are introduced to create different neighborhoods for ALNS, thereby enriching search diversity. In addition, an acceptance criterion inspired by simulated annealing is designed to balance exploration and exploitation, helping the algorithm avoid being trapped in local optima. Extensive numerical experiments on generated benchmark instances demonstrate the effectiveness of the VRP-CS model and the efficiency of the proposed MSALNS algorithm. The experiment results on the generated benchmark instances show that the total cost of the VRP-CS is reduced by an average of 14.22% when optional delivery locations are utilized compared to scenarios with single delivery locations. Full article

The use of electric vehicles in urban transportation is increasing daily due to their energy efficiency and environmental friendliness. In last-mile logistics, route optimization must consider charging station locations while balancing operational costs and customer satisfaction. In this context, solutions for cost-oriented route optimization have been presented in the literature. On the other hand, customer satisfaction is also important for third-party logistics companies. This study discusses the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW) encountered in last-mile logistics. This article defines the objective function of minimizing total tardiness and compares the routes between the service provider logistics company and the customer receiving the service. In this study, the CEVRPTW was solved for the minimum total tardiness objective function with the hybrid adaptive large neighborhood search (ALNS) algorithm. The success of ALNS was proven by comparing the differences between the optimal solutions obtained with the CPLEX Solver and the ALNS solutions. Tardiness objective function-specific operators for ALNS are proposed and supported by local search and VNS algorithms. The findings of this study contribute to the literature by analyzing the balance trade-offs between customer-oriented and cost-oriented and the effect of time windows on the number of vehicles. Full article

Effective cooperation between railways and seaports is crucial for enhancing the efficiency of seaport-hinterland corridors (SHC) . However, existing challenges stem from fragmented decision-making across seaports, rail operators, and inland cities, leading to asynchronous routing and scheduling, suboptimal service coverage, and delays. Addressing these issues requires a comprehensive approach to scheduled train service design from a network-based perspective. To tackle the challenges in SHCs, we propose a targeted networked solution that integrates multimodal coordination and resource optimization. The proposed framework is built upon a time-space-state network model, incorporating service selection, timing, and frequency decisions. Furthermore, an improved adaptive large neighborhood search (ALNS) algorithm is developed to enhance computational efficiency and solution quality. The proposed solution is applied to a representative land–sea transport corridor to assess its effectiveness. Compared to traditional operational strategies, our optimized approach yields a 7.6% reduction in transportation costs and a 56.6% decrease in average cargo collection time, highlighting the advantages of networked service coordination. The findings underscore the potential of network-based operational strategies in reducing costs and enhancing efficiency, particularly under unbalanced demand distributions. Additionally, effective demand management policies and targeted infrastructure capacity enhancements at bottleneck points may play a crucial role in practical implementations. Full article

The harm of marine debris (MD) to the environment and human beings has been paid more and more attention. At present, the most effective way to collect macro-MD floating on the sea is to send vessels. We employ vessels equipped with a hybrid energy system (HES) composed of photovoltaic (PV), battery and diesel to carry out MD cleanup. We propose a two-stage optimization approach for vessel routing and energy management strategy. In the first stage, the vessel routing problem with a drifting time window is modeled to minimize the vessel travel time considering continuous speed. The drifting time window means that multiple time windows are set on the MD trajectory, which is used to depict its dynamic nature. An adaptive large neighborhood search algorithm considering an elitist strategy coupled with speed optimization is designed to solve this problem. In the second stage, a mixed integer linear programming model for energy management strategy is established to minimize the total cost, including the power generation cost of diesel and PV, the battery charge, and discharge and carbon tax costs. The model takes the power load balance, the power limit of each part of the hybrid energy system and the battery charge and discharge state as constraints. The correctness of the proposed models and the effectiveness of the proposed algorithm are verified by a numerical example. The results not only show the advantages of hybrid energy vessels in energy saving and emission reduction but also show that the drifting time window can provide a rich and effective route selection solution. Some suggestions for rational utilization of hybrid energy vessels with long and short trips are put forward. Full article

With the growing demand for delivery services and the escalating labor costs, much effort has been made to achieve faster and cost-efficient delivery. A promising emerging strategy involves the integration of autonomous delivery vehicles or drones into the last-mile delivery. This study presents a fully automated last-mile delivery system that synergistically integrates autonomous vehicles and drones. We also introduce a novel variant of the vehicle routing problem with drones, referred to as the hybrid autonomous vehicle-drone routing problem (HAVDRP). In HAVDRP, we employ three delivery tools: autonomous vehicles, vehicle-carried drones, and independent drones. The aim is to fully leverage the advantages of autonomous vehicles and drones to provide customers with more efficient last-mile delivery services. An improved adaptive large neighborhood search algorithm is developed to address this problem. The algorithm incorporates a tabu list and an adaptive mechanism specifically designed for the HAVDRP, thereby augmenting the search efficiency. Computational experiments are conducted to evaluate the efficiency of the designed algorithm. Additionally, sensitivity analyses are conducted to explore the influences of some key parameters on the total time, which includes the cumulative working time of autonomous vehicles and drones. Based on the results of sensitivity analyses, we propose some management recommendations for the fully automated last-mile delivery system utilizing autonomous vehicles and drones. Full article

To address the challenges of distribution cost and efficiency in electric vehicle (EV) logistics, this study proposes a time-dependent, multi-center, semi-open heterogeneous fleet model. The model incorporates a nonlinear power consumption measurement framework that accounts for vehicle parameters and road impedance, alongside an objective function designed to minimize the total cost, which includes fixed vehicle costs, driving costs, power consumption costs, and time window penalty costs. The self-organizing mapping network method is employed to initialize the EV routing, and an improved adaptive large neighborhood search (IALNS) algorithm is developed to solve the optimization problem. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional methods in terms of solution quality and computational efficiency. Furthermore, through real-world case studies, the impacts of different distribution modes, fleet sizes, and charging strategies on key performance indicators are analyzed. These findings provide valuable insights for the optimization and management of EV distribution routes in logistics enterprises. Full article

We propose a hub ridesharing method that considers path similarity to swiftly evacuate high volumes of passengers arriving at a high-speed railway hub. The technique aims to minimize total mileage and the number of service vehicles, considering the characteristics of hub passengers, such as the constraints of large luggage, departure times, and arrival times. Meanwhile, to meet passengers’ expectations, a path morphology similarity indicator combining directional and locational features is developed and used as a crucial criterion for passenger matching. A two-stage algorithm is designed as a solution. Passenger requests are clustered based on path vector similarity in the first stage using a heuristic approach. In the second stage, we employ an adaptive large-scale neighborhood search to form passenger matches and shared routes. The experiments demonstrate that this method can reduce operational costs, enhance computational efficiency, and shorten passenger wait times. Taking path similarity into account significantly decreases passenger detour distances. It improves the Jaccard coefficient (JAC) of post-ridesharing paths, fulfilling the passenger’s psychological expectation that the shared route will closely resemble the original one. Full article

Robots have been widely adopted in transportation and delivery applications. Path planning plays a critical role in determining the performance of robotic systems in these tasks. While existing research has predominantly focused on path planning for single robots and the design of robot delivery systems based on hotel-specific demand characteristics, there is limited exploration of multi-robot collaborative routing in three-dimensional environments. This paper addresses this gap by investigating the multi-robot collaborative path planning problem in three-dimensional, multi-floor hotel environments. Elevator nodes are modeled as implicit waypoints, and the routing problem is formulated as a Multi-Trip Vehicle Routing Problem (MTVRP). To solve this NP-hard problem, an Adaptive Large Neighborhood Search (ALNS) algorithm is proposed. The effectiveness of the algorithm is validated through comparative experiments with Gurobi, demonstrating its ability to handle complex three-dimensional delivery scenarios. Numerical results reveal that the number of robots and elevator operation times significantly impact overall delivery efficiency. Additionally, the study identifies an imbalance in resource utilization, where certain robots are overused, potentially reducing their lifespan and affecting system stability. This research highlights the importance of efficient multi-robot routing in three-dimensional spaces and provides insights into optimizing delivery systems in complex environments. Full article

Aiming at the problems of the A* algorithm’s long running time, large number of search nodes, tortuous paths, and the planned paths being prone to colliding with the corner points of obstacles, adaptive weighting and reward value theory are proposed to improve it. Firstly, the diagonal-free five-way search based on the number of coordinate changes is used to make the algorithm purposeful. Meanwhile, in order to improve the path security, the diagonal search is filtered out when there are obstacles in the search neighborhood. Secondly, a radial basis function is used to act as the adaptive weighting coefficient of the heuristic function and adjust the proportion of heuristic functions in the algorithm accordingly to the search distance. Again, optimize the cost function using the reward value provided by the target point so that the current point is away from the local optimum. Finally, a secondary optimization of the path is performed to increase the distance between the path and the barriers, and the optimized path is smoothed using Bessel curves. Typical working conditions are selected, and the algorithm is verified through simulation tests. Simulation tests show that the algorithm not only shortens the planning time and improves the path security but also reduces the number of search nodes by about 76.4% on average and the turn angle by about 71.7% on average. Full article

Entity Alignment (EA) in Knowledge Graphs (KGs) is a crucial task for the integration of multiple KGs, facilitating the amalgamation of multi-source knowledge and enhancing support for downstream applications. In recent years, unsupervised EA methods have demonstrated remarkable efficacy in leveraging graph structures or utilizing auxiliary information. However, the increasing complexity of many modeling methods limits their applicability to large KGs in real-world scenarios. Given that most EA encoders primarily focus on modeling one-hop neighborhoods within the KG’s graph structure while neglecting similarities among multi-hop neighborhoods, we propose an efficient and effective unsupervised EA method, MPGT-Align, based on a multi-hop pruning graph transformer. The core innovation of MPGT-Align lies in mining multi-hop neighborhood features of entities through two components: Pruning-hop2Token and Attention-based Transformer encoder. The former aggregates only those multi-hop neighborhoods that contribute to alignment targets, inspired by search pruning algorithms. The latter empowers MPGT-Align to adaptively extract more effective alignment information from both entity itself and its multi-hop neighbors. Furthermore, Pruning-hop2Token serves as a non-parametric method that not only reduces model parameters, but also allows MPGT-Align to be trained with small batch sizes, thereby enabling efficient handling of large KGs. Extensive experiments conducted across various benchmark datasets demonstrate that our method consistently outperforms most existing supervised and unsupervised EA techniques. Full article