Search Results (1,138)

This study investigates the dynamic-demand green vehicle routing problem with soft time windows (DDGVRPSTW). A two-stage optimization model is developed to minimize total distribution cost, including vehicle operating cost, fixed dispatch cost, fuel consumption cost, carbon emission cost, and time window penalty cost. To solve the model, a hybrid artificial bee colony state-transition algorithm (HABC-STA) is proposed. In the pre-optimization stage, multiple initial routes are generated and refined to obtain an initial distribution plan. In the dynamic optimization stage, customer information is updated at a specified event time, and four state-transition operators are used to search the neighborhood of the current solution and generate a revised routing plan with lower cost. Computational results on Solomon benchmark instances and a real-world case study show that the proposed method effectively reduces both total cost and environmental cost. The results also indicate that selecting an appropriate distribution scheme can significantly reduce fuel consumption and carbon emissions while improving overall routing efficiency. Full article

Electric logistics vehicles have been increasingly applied in various delivery scenarios due to their environmental benefits. However, limitations in battery capacity and the availability of charging infrastructure remain major obstacles to their wider adoption. This study develops a route optimization model for electric cold-chain logistics vehicles with the objective of minimizing total costs. The model incorporates multiple constraints, including charging mode selection, maximum vehicle capacity, and customer time windows. Computational results show that different charging modes have distinct effects on the total delivery cost of electric cold-chain logistics vehicles. Compared with the public charging mode, the public–private charging mode can achieve lower delivery costs. Moreover, as the difference in unit charging costs between public and private charging stations increases, electric cold-chain logistics vehicles are more likely to utilize charging stations that are farther away but offer lower charging costs. Full article

Quadrotors performing sensing missions near structures such as turbines, towers, and buildings must hold a stable attitude while traversing the structured wind wakes generated by these structures. To maintain trajectory tracking in wind, the vehicle must tilt continuously, and the inner-loop controller must work harder to hold that tilt against the fluctuating flow, raising mean tilt, angular jerk, and command-rate activity. These attitude-domain costs can degrade onboard imagery and gimbal-stabilized sensor data, consuming the actuator authority required to reject further disturbances. Existing work typically treats the two halves of this problem separately: wind is either estimated locally and compensated reactively, or routed around in fields assumed known a priori, and is rarely validated against attitude-domain metrics on hardware. These approaches are most effective when coupled through a single shared representation. A nonlinear disturbance observer estimates wind from the vehicle’s translational dynamics and accumulates it into a spatial map, which simultaneously provides per-stage feedforward compensation to a contouring controller and weights a wind-aware A* planner. On hardware, the estimator matches anemometer ground truth to within $1\mathrm{m}/\mathrm{s}$, and a $2\times 2$ ablation study across three wind configurations shows a reduction of up to $38\%$ in tilt RMS and $28\%$ in its 95th percentile relative to a wind-naive baseline, at the cost of longer paths. Full article

In the capacitated arc routing problem (CARP), a fleet of capacitated vehicles based at a depot must cover the streets of a network where the demand is located at the lowest possible total cost. Waste collection, street sweeping, winter gritting, and mail delivery are among its best-known applications. This work introduces the CARP with vehicle dependence (CARP-VD), an extension in which the cost of servicing an edge, and that of traversing it without service, are specific to each vehicle type and formulates it as a mixed-integer linear program. A two-stage matheuristic is proposed: the first stage distributes the required edges among the vehicles without exceeding their capacities, and the second builds the route of each vehicle. A bound is derived that limits the optimality loss of this decomposition by its own deadheading cost. Both approaches are evaluated on 47 benchmark instances adapted from the literature under a common one-hour budget, and their robustness is assessed over six scenarios that vary the parameters of the adaptation. The matheuristic returns good-quality solutions in a fraction of the time on the smaller instances, and on those in which almost every edge requires service it improves the best solutions found by a commercial solver applied to the complete model by up to 44%. Full article

Multi-agent reinforcement learning has emerged as a promising paradigm for cooperative unmanned aerial vehicle (UAV) swarm coordination. However, existing communication-aware MARL methods primarily focus on communication topology, message routing, and message aggregation, while the information content of the exchanged messages is often only implicitly controlled. In realistic UAV networks, inter-agent communication is constrained by limited bandwidth, communication range, energy consumption, and packet loss. It is therefore desirable for each UAV to transmit compact and task-relevant information rather than dense and redundant latent features. In this paper, we propose IB-CEMARL, an information-bottleneck-guided, communication-efficient multi-agent reinforcement learning framework for UAV swarms. We formulate inter-UAV communication as a minimal sufficient message-learning problem in which each UAV encodes its local observation into a stochastic bottleneck message before exchanging information with its neighbors. Cauchy–Schwarz divergence-based quadratic mutual information is adopted as a unified dependence measure to jointly regularize message compression, preserve decision-relevant information, and reduce statistical redundancy among neighboring UAV messages. Extensive experiments demonstrate that IB-CEMARL achieves superior cooperative performance, reduced message redundancy, and stronger robustness compared with representative communication-aware MARL baselines. In particular, IB-CEMARL improves the average return by $4.9\%$ and reduces inter-message dependence by $29.0\%$ compared with the KL-IB-MARL baseline while maintaining efficient communication under constrained bandwidth settings. Full article

The rapid development of shared delivery, parcel lockers, and community pickup services has enabled customers to receive orders at multiple alternative service locations. In such scenarios, the fixed-location assumption adopted in traditional electric vehicle routing problems is no longer appropriate. This paper investigates the Electric Vehicle Routing Problem with Time Windows and Flexible Service Locations (EVRPTW-FSL), in which customers can be served at one selected location from a candidate set, while each service location may accommodate multiple customers subject to capacity limits. A mixed-integer optimization model is developed to jointly determine service location assignments, vehicle routing, and charging decisions under vehicle capacity, battery range, partial recharging, and customer time-window constraints. To balance operational efficiency and customer convenience, the objective minimizes the total travel cost and the customer deviation cost incurred when a customer is assigned to an alternative service location rather than the original service location. To solve this NP-hard problem, a Modified Adaptive Large Neighborhood Search with Fix-and-Optimize mechanism (MALNS-FO) is proposed, incorporating specialized operators such as location association destroy, location similarity destroy, and route reconstruction repair, as well as a fix-and-optimize mechanism. Computational experiments demonstrate that the proposed method consistently outperforms benchmark approaches in solution quality and computational efficiency. Results further show that introducing flexible service locations can significantly reduce fleet usage and routing cost by consolidating spatially dispersed demand. Moreover, moderate customer flexibility provides substantial operational benefits while maintaining acceptable service deviation levels. Full article

This study investigates the collaborative green vehicle routing problem with time-dependent travel speeds (CGVRP-TD), which integrates horizontal collaboration among multiple depots with time-dependent traffic conditions. The problem jointly optimizes customer allocation, vehicle routing, and departure-time decisions to minimize transportation-related carbon emissions subject to vehicle capacity and customer time-window constraints. We formulate the CGVRP-TD as a mixed-integer programming model and develop a two-phase adaptive large neighborhood search algorithm with embedded departure-time optimization. The first phase explores routing and customer-assignment decisions using problem-specific operators, including two speed-related removal operators, while the second phase applies exact departure-time optimization to fixed routes. Computational experiments show that the proposed algorithm obtains high-quality solutions efficiently and that both departure-time optimization and speed-related operators contribute to emission reduction. The results further demonstrate that combining horizontal collaboration with time-dependent travel-speed information can substantially reduce transportation emissions while preserving on-time service. We also discuss emission-savings allocation mechanisms for sustaining collaboration among participating depots. Full article

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two classical combinatorial optimization problems. In recent years, their drone-assisted variants, the Traveling Salesman Problem with Drones (TSP-D) and the Vehicle Routing Problem with Drones (VRP-D) have attracted growing attention. Generally, these problems are solved using exact algorithms or metaheuristic algorithms. However, as the problem complexity increases and the scale of instances grows, these approaches often become less efficient. In this paper, we propose a reinforcement learning method with a shared attention encoder and a hierarchical dual-decoder architecture, where truck–drone coordination is achieved by first decoding the truck’s next node and then conditionally decoding the drone action. To further explore the solution space of large-scale instances, the proposed method adopts a multi-rollout learning strategy. We conducted experiments on large-scale TSP-D and VRP-D instances, and the results show that this model outperforms traditional metaheuristic algorithms in terms of both solution quality and computational efficiency. Full article

Ship path planning is a central challenge in autonomous navigation for unmanned surface vehicles and maritime autonomous surface ships. It is not simply a shortest-path problem, but a constrained sequential decision process that must reconcile collision risk, route efficiency, COLREGs compliance, vessel dynamics, and environmental uncertainty. Here we review the field through a unified framework based on planning scope, decision basis, and deployment requirements. We examine search- and sampling-based, geometric and rule-based, optimization-based, learning-driven, and hybrid methods, with particular emphasis on deep reinforcement learning for discrete decisions, continuous maneuvering, multi-vessel interaction, and safety-oriented control. Representative studies are compared across objective and reward design, state representation, exploration and policy optimization, rule integration, disturbance modeling, simulation platforms, and operational validation. The synthesis identifies persistent barriers, including ambiguous rule formalization, partial observability, strategic coupling among vessels, inconsistent benchmarks, limited cross-scenario generalization, and insufficient full-scale validation. We further discuss priority directions in explicit safety constraints, digital twins, transfer and meta-learning, world models, scalable multi-agent coordination, and large-model-assisted mission reasoning. We argue that progress will depend less on further algorithmic proliferation than on integrated, verifiable architectures that combine data-driven adaptation with model-based structure, standardized evaluation, and staged real-world assurance. Full article

With the electrification of urban bus fleets, the sustainability of public transport operation increasingly depends on resource-efficient energy management at bus depots. Under time-of-use electricity pricing and limited depot resources, shifting many charging tasks to low-price periods may increase depot-level peak load and charger competition, thereby affecting vehicle time windows and departure state-of-charge (SOC) requirements. This study formulates a multi-objective nighttime centralized charging scheduling model that minimizes an economic objective while controlling depot-level peak load. The model combines staged charging characteristics with an interval-overlap-based load calculation method to link SOC evolution, time-window occupancy, and depot load formation. For the resulting constrained discrete scheduling task, a problem-specific SPEA2-based solution framework, named DCS-SPEA2, is developed with vehicle-block encoding, repair-coupled decoding, and archive-based Pareto search. A case study using Shanghai bus route 71 shows that the compromise solution achieves a 100% minimum departure SOC compliance rate, reduces the depot-level peak load to 562.74 kW, and decreases the economic objective by 17.97% and 16.30% compared with first-come-first-served charging and valley-price-priority charging, respectively. Compared with standard SPEA2, DCS-SPEA2 increases the average hypervolume from 0.5783 to 0.6620 and reduces the average inverted generational distance from 0.2505 to 0.1993. These results indicate that coordinated charging scheduling can improve resource use efficiency and operational reliability at electric bus depots, thereby supporting sustainable public transport operation under limited depot resources. Full article

Truck–UAV collaborative delivery can improve last-mile logistics efficiency, but fixed-node rendezvous often causes waiting loss and service delay. To address this problem, this paper proposes a route optimization method integrating en route synchronization, pseudo-node insertion, and GAT-PPO. Pseudo-nodes are generated along truck travel arcs to provide flexible UAV recovery points, and a time-recursive simulation model is developed to evaluate makespan and total tardiness under soft time windows. In the proposed framework, GAT is used to capture spatial–temporal relationships among nodes, while PPO supports sequential routing decisions and UAV dispatch coordination. Experiments on Solomon VRPTW instances with clustered, random, and mixed customer distributions show that GAT-PPO achieves the shortest total travel distance, the lowest total tardiness, and the shortest completion time among Random, NN, NN+2-opt, MLP-PPO, ALNS, GA, and VNS. Ablation results further confirm the contributions of GAT, PPO, pseudo-node insertion, en route synchronization, and UAV collaboration. The results indicate that the proposed framework can effectively reduce synchronization waiting loss and improve the temporal efficiency of truck–UAV collaborative delivery. Full article

This paper studies the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Time Windows (VRPSPDTW). The problem arises from reverse logistics, last-mile distribution, and circular logistics, where vehicles must serve delivery and pickup demands while respecting vehicle capacity and customer time-window constraints. This work develops DBLS-SP. The method combines an improved insertion-based initialization procedure, a repair-oriented dynamic scoring strategy, ejection-based reinsertion, multi-armed-bandit control of the route-inheritance ratio, vehicle-layer archive-guided route descent, and quality-first nearest-neighbor pool replacement. Computational experiments on the Wang–Chen (WC) and JD Logistics (JD) benchmark instances show that DBLS-SP obtains the best algorithmic solution on 67 of 68 WC instances and the best or tied-best objective value on 16 of 20 JD instances. The ablation study confirms the contribution of the main components, and the proposed reduction strategy removes more infeasible paths than the original reduction rule while preserving feasibility. Full article

Construction sites are beginning to deploy material transport robots to relieve labor constraints; however, planning and validating robot operations within complex, evolving building layouts remain difficult. This study proposes a BIM-enabled simulation framework that connects building information to enable an objective pre-deployment evaluation. The method extracts navigable spaces, delivery points and material quantities from BIM, formulates dispatching as a capacity-constrained vehicle routing problem, and couples it with an A* algorithm for pathfinding. A greedy next-stop decision rule with a tunable correction factor governs whether the robot continues visiting additional stops or returns to the staging area. The experimental results show a more than 20% improvement in the travel efficiency of the robot compared with conventional algorithms. These results conclude that the framework offers a practical tool for what-if analysis and data-driven planning of robotized construction logistics and is readily extensible to diverse materials and project types. Full article

Trajectory planning, which determines a route from a starting position to a target position within a given airspace, is critical to unmanned aerial vehicle (UAV) mission execution. Many existing meta-heuristic approaches to three-dimensional (3D) trajectory planning aggregate competing requirements into a weighted cost and may suffer from limited adaptability when the environment changes. This paper formulates 3D UAV trajectory planning in dynamic multi-threat environments as a dynamic bi-objective optimization problem and proposes a multi-swarm dynamic multi-objective crow search algorithm (MDMCSA). The proposed method organizes objective-oriented swarms within a cooperative search framework and facilitates information exchange through archive sharing, thereby coordinating the search process among different objectives. The memory-time and diverse behavior strategies adjust search behaviors and solution perturbation to balance convergence and diversity. A hybrid change response strategy combines historical information reuse with diversity restoration after dynamic changes. Comparative experiments on dynamic benchmark problems and UAV trajectory planning scenarios demonstrate competitive convergence and adaptation performance, together with a favorable trade-off between solution quality and computational cost. Incremental ablation and parameter-sensitivity analyses further indicate the cumulative benefit of the integrated design and the stable performance of the selected parameter configuration across the tested settings. Full article

The transition from internal combustion engine vehicles to battery electric vehicles has changed the acoustic design problem in automotive engineering. The absence of combustion-related masking increases the perceptibility of tonal and narrowband sources, including gear whine, electric motor orders, inverter-related components, tire cavity resonances, auxiliary system noise, and lightweight-panel radiation. At the same time, mass-based acoustic treatments conflict with electric vehicle lightweighting, range, cost, and sustainability targets. Vibroacoustic metamaterials offer an alternative route by manipulating elastic and acoustic wave propagation through architected geometries, local resonances, periodicity, membranes, lattice architectures, and adaptive or topological wave-control mechanisms. This review examines vibroacoustic metamaterials for low-frequency electric vehicle noise, vibration, and harshness (EV NVH) from an engineering perspective. It covers mechanisms, EV-specific NVH problems, component applications, materials, manufacturing, modeling, validation, AI-assisted design, sustainability, and technology readiness. Particular emphasis is placed on order-targeted, path-oriented, manufacturable, and experimentally validated solutions for electric-drive (e-drive) housings, wheel arches, battery enclosures, body panels, covers, and auxiliary systems. The review concludes that vibroacoustic metamaterials are most promising when integrated into conventional NVH workflows through order analysis, transfer path ranking, robust resonator tuning, durability validation, and multi-objective design optimization. Full article