Search Results (24)

Efficient and resilient humanitarian logistics is critical for smart cities facing large-scale disasters, where infrastructure disruptions, uncertain demand, and time-critical deliveries complicate operational planning. This study proposes a multi-echelon vehicle-routing framework that integrates trucks, electric unmanned aerial vehicles (UAVs), and micromobility systems under uncertainty. Demand and travel-time variability are modeled through scenario-based representations, while delivery flexibility is captured through triangular fuzzy time windows defined by earliest-acceptable, preferred, and latest-tolerable delivery times. The problem is formulated as a four-objective optimization model that minimizes total cost, response time, CO₂ emissions, and fuzzy lateness. To solve the resulting highly constrained multi-objective problem, NSGA-II and NSGA-III are adapted with problem-specific repair operators, including capacity-splitting, range-feasibility correction, and fuzzy time-shift adjustment mechanisms, and embedded in a simulation-based evaluation framework. The proposed approach is validated using a geo-referenced earthquake scenario in Istanbul, constructed from open-source GIS, traffic, and demographic data. The computational results show that evolutionary methods generate feasible solutions within minutes, whereas exact optimization approaches fail to converge for realistic instances. Compared with NSGA-II, NSGA-III achieves superior performance, including a 9% higher hypervolume and improved robustness under stress-test scenarios. Furthermore, the hybrid truck–UAV–micromobility strategy reduces average cost by up to 30%, delivery time by 43%, and CO₂ emissions by 65% relative to a truck-only baseline, while eliminating fuzzy lateness. These findings demonstrate that evolutionary multi-objective optimization provides an effective and scalable decision-support framework for uncertainty-aware and sustainable humanitarian logistics in smart cities. Full article

Collaborative task allocation for multiple unmanned ground vehicles (UGVs) is a constrained combinatorial optimization problem in which symmetric vehicle resources must be coordinated with asymmetric task requirements. In delivery and inspection scenarios, homogeneous vehicles operate under identical rules, whereas task points differ in spatial distribution, demand, service time, and time window requirements. These asymmetries make compact, temporally feasible, and workload-balanced routing difficult. SACWDO-PSO is developed as a discrete particle swarm optimization framework that integrates Clarke–Wright savings initialization, adaptive parameter control, and simulated annealing local search. The savings strategy improves initial swarm quality, adaptive control adjusts exploration and exploitation during the search, and simulated annealing refines local route structures. The method is evaluated on Solomon VRPTW benchmark data under a soft time window penalty objective and insimulation scenarios developed using Unreal Engine 4.27 integrated with Microsoft AirSim 1.8.1. SACWDO-PSO obtains lower objective values and fewer time window violations than the compared swarm-intelligence baselines on most benchmark instances, while Wilcoxon signed-rank tests indicate statistically significant improvements over PSO, DPSO, and GA. Full article

Large-scale UAV data collection creates a tension among wide-area coverage, operational efficiency, and delivery continuity. Data must be continuously delivered to a base-station coordinator, but real-time replanning becomes increasingly difficult as the number of sensors and UAVs grows. Standard vehicle-routing methods slow down once routes have to be regenerated often, while reinforcement learning struggles with fixed-wing UAVs that cannot hover or turn sharply. We address this with a two-layer framework. In the lower layer, multirotor UAVs visit sensor nodes and buffer the collected payload until it is retrieved by a fixed-wing UAV. Their routes come from clustering the nodes and solving a capacitated vehicle routing problem within each cluster, with the cost biased toward older data and a short cooldown against immediate revisits. In the upper layer, fixed-wing UAVs deliver the buffered payload to the base-station coordinator, guided by a Multi-Agent Proximal Policy Optimization (MAPPO) policy that receives a local buffer-summary map and selected high-priority cells from a compact global summary. A spacing reward encourages separation before agents enter close-proximity states, instead of only penalizing collisions afterward. Component-level experiments show that the lower-layer planner handles up to 600 active routing targets within 1.3 s on average and that the age/cooldown objective improves freshness and revisit behavior. In integrated simulations with 1000 nodes, 32 multirotor UAVs, and 2 fixed-wing UAVs, the learned fixed-wing policy maintains collection performance comparable to a strong exclusive greedy baseline while recording no collision or persistent-proximity termination events over the reported data-generation-rate sweep. These results support the proposed framework as a scalable coordination-layer design for dynamic sensor workloads, where adaptive multirotor routing and motion-constrained fixed-wing retrieval are evaluated together under a shared data-generation workload. Full article

Electric-vehicle evacuation planning requires evacuation routes that are both road-capacity feasible and driving-range feasible. Existing evacuation routing methods typically account for road capacity but do not explicitly enforce electric-vehicle range constraints, whereas electric-vehicle routing models usually focus on individual vehicle routing and do not address large-scale evacuation scheduling under shared road-capacity limits. This paper studies the Resource Constraint Evacuation Route Planning (RC-ERP) problem, in which evacuees must be routed from source locations to safe destinations while satisfying road-capacity constraints and a maximum travel-distance constraint between consecutive charging-station visits. We propose the Time-Expanded Charge-Encoded Routing Algorithm (TE-CERA), a scalable constructive heuristic that combines charge-encoded route generation with time-expanded capacity-aware scheduling. The proposed Node-Encoded Shortest Path (NESP) procedure computes charging-feasible spatial routes by tracking the accumulated travel distance since the most recent charging-station visit, while the scheduling stage assigns feasible departure times using a time-indexed edge-occupancy table. Under the stated modeling assumptions, the framework guarantees charging-feasible and capacity-feasible evacuation schedules. Experiments on real-world transportation networks show that TE-CERA eliminates charging-constraint violations while maintaining comparable evacuation times to a capacity-constrained evacuation routing baseline. The results demonstrate the feasibility and scalability of integrating electric-vehicle range constraints into evacuation routing, while also highlighting future extensions involving charging duration, charger capacity, and station-level queueing. Full article

Post-disaster Search and Rescue (SAR) missions demand rapid coordination of Heterogeneous Unmanned Aerial Vehicle (UAV) fleets under stringent payload and flight range limitations. Traditional heuristic solvers struggle to solve the Large-Scale Heterogeneous Team Orienteering Problem (LSH-TOP) within operational time limits due to the coupled complexity of task allocation and route planning. A Hierarchical Deep Reinforcement Learning framework decomposes this high-dimensional combinatorial problem into tractable sub-problems. An upper-level policy, guided by Monte Carlo Tree Search (MCTS), partitions the global target set to balance fleet workload distribution, whereas a lower-level Transformer-based model constructs near-optimal trajectories for individual agents. A Curriculum-Integrated Alternating Cooperative Training (C-ACT) protocol resolves the convergence difficulties associated with sparse feasible solutions in constrained environments. This protocol incorporates a dynamic constraint annealing strategy and a virtual agent buffer to progressively shape the solution space from relaxed to strictly constrained formulations. Experiments conducted on real-world geographic data demonstrate the proposed approach consistently outperforms all baselines across scales of 80 to 300 targets, improving over the strongest competitor by 0.63–8.51% and over conventional heuristics by up to 53.27% in objective value. Results indicate a task completion rate of 27.5% at the 300-target scale (versus 25.1% for the strongest baseline MCTS + OR) and balanced workload distribution, validating framework adaptability to complex emergency response scenarios. Full article

Large language models (LLMs) have shown promise in intelligent transportation systems, but their direct use in constrained route planning remains unreliable because such tasks require exact numerical consistency and strict compliance with operational constraints. This challenge is particularly important in urban freight and logistics, where routing errors can reduce efficiency and undermine sustainability. To address this issue, this study proposes a supervised fine-tuning (SFT) framework that specializes a general-purpose LLM as an orchestration agent for route planning. Instead of generating routes directly, the model translates natural-language requests into structured function calls that invoke deterministic optimization solvers for the Traveling Salesperson Problem (TSP), Capacitated Vehicle Routing Problem (CVRP), and Vehicle Routing Problem with Time Windows (VRPTW). Experiments on a controlled synthetic benchmark with thousands of routing instances show that direct generation is ineffective for constrained routing, while tool augmentation substantially improves reliability. More importantly, SFT further strengthens function-calling performance, especially on the most challenging VRPTW task, where the overall success rate of the 8B model increases from 0.408 in the zero-shot setting to 0.792 after fine-tuning. The fine-tuned 8B model also outperforms a much larger zero-shot 235B model while requiring far fewer computational resources. These findings indicate that reliable LLM-based transportation decision support is better achieved by combining compact language models with deterministic optimization tools rather than relying on larger models for direct route generation, offering a lightweight and more sustainable path for real-world logistics deployment. Full article

With the rapid development of e-commerce and the increasing complexity of urban logistics, traditional delivery methods face significant challenges due to regional traffic restrictions and congestion. This paper presents a two-stage optimization approach for urban delivery routing, integrating the Split Delivery Vehicle Routing Problem (SDVRP) and truck-drone collaboration to address these challenges. In the first stage, a transportation route optimization model based on SDVRP is proposed, which accounts for regional constraints and vehicle capacity limitations. The model allows for demand splitting, reducing the number of vehicles required and minimizing transportation costs. In the second stage, a truck-drone collaborative delivery model is introduced to handle the “last mile” distribution, where drones complement trucks by delivering to areas with restricted vehicle access. The optimization model aims to minimize overall delivery costs while ensuring timely service. An enhanced genetic algorithm is further developed to solve this complex, multi-constrained model. Experimental results show that the proposed collaborative strategy reduces delivery costs by over 10% compared to truck-only delivery, and the improved algorithm achieves a 4.77% average cost reduction over traditional approaches. This study provides valuable insights for optimizing urban logistics systems under regional constraints, offering both theoretical and practical contributions to smart logistics development. Full article

This article addresses the problem of finding the shortest route for Automated Guided Vehicles (AGVs) in a production environment with constrained battery state-of-charge (SoC) and time-dependent operating conditions. The route map is divided into a uniform grid containing stationary obstacles and two types of dynamic obstacles: human, for which AGV transportation is prohibited, and inanimate (moving objects), which impose a penalty function. A key contribution of the proposed methodology is the introduction of a battery residual charge matrix, which embeds cell-level energy feasibility directly into the grid-based environment representation by determining minimum admissible SoC constraints and accounting for transition-dependent energy costs. This matrix restricts the set of traversable cells under low-energy conditions, enabling energy-aware route feasibility evaluation during both initial planning and adaptive replanning. The proposed approach is based on the A* and D* Lite algorithms, providing shortest-path construction that explicitly integrates battery SoC into the spatio-temporal cost function. To avoid collisions in a multi-agent environment during routing, a simplified hybrid scheme with M* elements performs local coordination and adaptive trajectory replanning. The effectiveness of the proposed methodology was assessed using travel time, temporal complexity, and spatial complexity metrics. Simulation results on a $10\times 10$ grid showed that agents with sufficient battery completed routes of 8 and 11 cells with travel times of 7.2 to 10.7 conventional units. A critically low-energy agent was initially unable to move, but after adjusting the minimum SoC constraint, all agents completed their routes with travel times up to 11.4 conventional units, demonstrating the direct impact of energy constraints on system performance. Additional experiments with varying agent counts and SoC thresholds confirmed reliable balancing of route feasibility and energy constraints across configurations. Full article

Uncertainty in travel and service times poses significant challenges for vehicle routing in logistics systems. This paper proposes a stochastic model predictive control (SMPC) strategy to manage a Vehicle Routing Problem with time windows (VRPTW) under stochastic service times with correlation across customers. The approach combines a dynamic optimization model with single and joint chance constraints and a forecasting tool for updating travel plans as new information becomes available. A deterministic reformulation of the stochastic constraints is developed so that the problem can be solved via mixed-integer programming. The aim of this paper is to demonstrate that the SMPC strategy can maintain a high level of time-window reliability (meeting customer time windows with high probability) at a reasonable cost by re-optimizing routes over a moving horizon. In numerical case studies, the SMPC approach achieves the desired reliability levels while incurring only modest increases in total cost, and it flexibly adjusts the cost–risk tradeoff by switching between single and joint chance constraints. These results illustrate the potential of the proposed method for real-time distribution routing under uncertainty and highlight the novel contribution of integrating chance-constrained optimization with Model Predictive Control in a VRPTW context. Full article

Deep reinforcement learning (DRL) with self-play has emerged as a promising paradigm for solving combinatorial optimization (CO) problems. The recently proposed Gumbel AlphaZero Plan-to-Play (GAZ PTP) framework adopts a competitive training setup between a learning agent and an opponent to tackle classical CO tasks such as the Traveling Salesman Problem (TSP). However, in complex and multi-constrained environments like the Electric Vehicle Routing Problem (EVRP), standard self-play often suffers from opponent mismatch: when the opponent is either too weak or too strong, the resulting learning signal becomes ineffective. To address this challenge, we introduce Two-Stage Self-Play GAZ PTP (TSS GAZ PTP), a novel DRL method designed to maintain adaptive and effective learning pressure throughout the training process. In the first stage, the learning agent, guided by Gumbel Monte Carlo Tree Search (MCTS), competes against a greedy opponent that follows the best historical policy. As training progresses, the framework transitions to a second stage in which both agents employ Gumbel MCTS, thereby establishing a dynamically balanced competitive environment that encourages continuous strategy refinement. The primary objective of this work is to develop a robust self-play mechanism capable of handling the high-dimensional constraints inherent in real-world routing problems. We first validate our approach on the TSP, a benchmark used in the original GAZ PTP study, and then extend it to the multi-constrained EVRP, which incorporates practical limitations including battery capacity, time windows, vehicle load limits, and charging infrastructure availability. The experimental results show that TSS GAZ PTP consistently outperforms existing DRL methods, with particularly notable improvements on large-scale instances. Full article

The electric vehicle routing problem (EVRP) is constrained by the limited driving range and time-consuming energy replenishment. These characteristics shift the focus of the EVRP from simple path optimization to an integrated optimization of routing and energy replenishment. Consequently, the energy replenishment strategy becomes a critical determinant of the feasibility and economic viability of EVRP solutions. This paper presents a systematic literature review structured around a core classification of replenishment strategies. The strategies are categorized into two primary modes: charging and battery swapping. This framework addresses common gaps in existing research, such as imprecise strategy definitions and fragmented analyses. For the charging strategy, we establish a three-dimensional classification framework, which comprises the charging function, charging policy, and charging station type. Within this context, wireless charging is considered as a special method of energy replenishment. The battery swapping strategy relies on battery swapping stations (BSSs): the EVRP with BSSs (EVRP–BSSs) and the BSS location-routing problem with electric vehicle (BSS–EV–LRP). Our review identifies several limitations in the current body of research. These include an imbalance between modeling accuracy and computational efficiency, insufficient coverage of diverse operational scenarios, and a superficial integration of emerging technologies. Furthermore, many studies lack a multi-stakeholder perspective that considers collaborative solutions. Future research should prioritize addressing these gaps. Key directions include developing effective methods for solving nonlinear charging functions and expanding research into more specialized scenarios. Additionally, there is a need to improve collaborative algorithms for battery swapping and to develop shared BSS models that serve multiple enterprises. The strategy-driven framework proposed here offers a clear reference for modeling and scenario adaptation in future EVRP studies. Full article

Vehicle-mounted multi-UAV (VM-UAV) systems offer enhanced flexibility and rapid deployment for large-scale remote sensing tasks such as disaster response and land surveys. However, maximizing their operational efficiency remains challenging, as it requires the simultaneous resolution of task scheduling and coverage path planning—an NP-hard problem. This study presents a novel multi-objective genetic algorithm (GA) framework that jointly optimizes routing and scheduling for cost-constrained, load-balanced multi-UAV remote sensing missions. To improve convergence speed and solution quality, we introduce two innovative operators: a Multi-Region Edge Recombination Crossover (MRECX) to preserve superior path segments from parents and an Adaptive Hybrid Mutation (AHM) mechanism that dynamically adjusts mutation strategies to balance exploration and exploitation. The algorithm minimizes total flight distance while equalizing workload distribution among UAVs. Extensive simulations and experiments demonstrate that the proposed GA significantly outperforms conventional GA, particle swarm optimization (PSO), ant colony optimization (ACO), and clustering-based planning methods in both solution quality and robustness. The practical applicability of our framework is further validated through two real-world case studies. The results confirm that the proposed approach delivers an effective and scalable solution for vehicle-mounted multi-UAV scheduling and path planning, enhancing operational efficiency in time-critical remote sensing applications. Full article

To overcome the limitations of traditional methods in emergency response scenarios—such as limited adaptability during the search process and a tendency to fall into local optima, which reduce the overall efficiency of emergency supply distribution—this study develops a Vehicle Routing Problem (VRP) model that incorporates multiple constraints, including service time windows, demand satisfaction, and fleet size. A multi-objective optimization function is formulated to minimize the total travel time, reduce distribution imbalances, and maximize demand satisfaction. To solve this problem, a hybrid deep reinforcement learning framework is proposed that integrates an Adaptive Large Neighborhood Search (ALNS) with Proximal Policy Optimization (PPO). In this framework, ALNS provides the baseline search, whereas the PPO policy network dynamically adjusts the operator weights, acceptance criteria, and perturbation intensities to achieve adaptive search optimization, thereby improving global solution quality. Experimental validation of benchmark instances of different scales shows that, compared with two baseline methods—the traditional Adaptive Large Neighborhood Search (ALNS) and the Improved Ant Colony Algorithm (IACA)—the proposed algorithm reduces the average objective function value by approximately 23.6% and 25.9%, shortens the average route length by 7.8% and 11.2%, and achieves notable improvements across multiple performance indicators. Full article

Vehicle Routing Problems are central to logistics and operational research, arising in diverse contexts such as transportation planning, manufacturing systems, and military operations. While Deep Reinforcement Learning has been successfully applied to both deterministic and stochastic variants of Vehicle Routing Problems, existing approaches often neglect critical time-sensitive conditions. This work addresses the Stochastic Capacitated Vehicle Routing Problem with Service Times and Deadlines, a challenging formulation that is suited to model time routing conditions. The proposal, POMO-DC, integrates a novel dynamic context mechanism. At each decision step, this mechanism incorporates the vehicle’s cumulative travel time and delays—features absent in prior models—enabling the policy to adapt to changing conditions and avoid time violations. The model is evaluated on stochastic instances with 20, 30, and 50 customers and benchmarked against Google OR-Tools using multiple metaheuristics. Results show that POMO-DC reduces average delays by up to 88% (from 169.63 to 20.35 min for instances of 30 customers) and 75% (from 4352.43 to 1098.97 min for instances of 50 customers), while maintaining competitive travel times. These outcomes highlight the potential of Deep Reinforcement Learning-based frameworks to learn patterns from stochastic data and effectively manage time uncertainty in Vehicle Routing Problems. Full article

This study addresses a real-world logistics problem in forestry operations: the distribution of plants from cultivation centers to planting sites under strict delivery time windows and limited depot resources. We introduce the Coordinated Truck Loading and Routing Problem (CTLRP), an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) that integrates routing decisions with truck loading schedules at a single depot with constrained capacity. To solve this NP-hard problem, we develop a metaheuristic algorithm based on Ant Colony Optimization (ACO), enhanced with a global memory system and a novel stochastic return rule that allows trucks to return to the depot when additional deliveries are suboptimal. Parameter calibration experiments are conducted to determine optimal values for the return probability and ant population size. The algorithm is tested on a real forestry dispatch scenario over six working days. The results show that an Ant Colony System (ACS–CTLRP) algorithm reduces total distance traveled by 23%, travel time by 22%, and the number of trucks used by 13 units, while increasing fleet utilization from 54% to 83%. These findings demonstrate that the proposed method significantly outperforms current company planning and offers a transferable framework for depot-constrained routing problems in time-sensitive distribution environments. Full article