Search Results (1,083)

Field-service operations for utility companies require routing technicians across multiple depots while guaranteeing same-day response to critical infrastructure customers, a constraint that standard multi-depot routing methods cannot structurally enforce. We introduce the MDVRP with Priority Requests (MDVRP-PR), formalised as a lexicographic optimisation problem that guarantees service to priority customers before maximising coverage and minimising route duration. A three-stage pipeline is proposed: hybrid DBSCAN-Hierarchical clustering for topology-aware depot assignment, an Enhanced Max-Min Ant System (MMAS) with priority-driven construction, lexicographic solution selection, and repair, and a Boundary Relocate post-optimisation stage with global cross-depot recovery. The approach is evaluated on a real-world applied case study from Algérie Télécom (Guelma, Algeria), comprising a single four-depot field-service instance scaled to three sizes (55, 90, and 150 customers) and assessed over 2135 controlled runs. On this case study, the proposed clustering method outperforms the MDVRP-adapted Sweep baseline by $22.9$ percentage points on the largest instance (n = 150; Friedman p < 0.001). The priority mechanisms sustain $100\%$ feasibility across all configurations, compared to complete collapse without them ($0/10$ seeds at $\ge 40\%$ priority), at a route-time overhead below $5\%$. Relative to the company’s current manual practice, the framework improves customer coverage by $16.1$ percentage points within 28 s, confirming its practical utility for daily deployment in this capacity-constrained, priority-sensitive routing context. Full article

This systematic literature review (SLR) synthesizes current research on integrating machine learning (ML) into robust optimization (RO) frameworks for solving Green Vehicle Routing Problems (Green-VRP) under uncertainty. The key contributions include utilizing the EmbedSLR 2.0 framework for objective screening, establishing a functional ML role taxonomy, and mapping uncertainty sets to computational tractability. Following PRISMA guidelines, searches across Scopus, Sage, and Dimensions identified 82 eligible studies validated through a three-point quality assessment scale. Bibliometric analysis indicates that the VRP has evolved into an interdisciplinary field that combines the power of rigorous RO with the integration capabilities of ML to achieve sustainability and resilience goals. Based on the results of the literature review, it was found that ML plays four crucial functional roles: as an end-to-end problem solver, a tool for predicting input parameters, a guide for search subroutines, and a mechanism for constructing more precise uncertainty sets. Various frameworks such as Adjustable Robust Optimization (ARO), Distributionally Robust Optimization (DRO), and Data-Driven Robust Optimization (DDRO) have been reported in various studies to offer improved cost efficiency and robustness compared to conventional static RO models by utilizing data more dynamically to reduce the level of conservatism. The integration of these environmental factors is carried out through emission and energy consumption parameters, which systematically give rise to operational trade-offs. This SLR has several limitations, including database and language limitations, the absence of cross-reference validation in EmbedSLR 2.0, and limitations in quality assessment. This publication is funded by the Universitas Padjadjaran through the LPDP on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under the EQUITY Program (Contract No. 4303/B3/DT.03.08/2025 and 3927/UN6.RKT/HK.07.00/2025), as well as the Universitas Padjadjaran Research Grant under Research Grant for Graduate Students (Hibah Riset Melibatkan Mahasiswa Pascasarjana - RMMP) with contract number 5598/UN6.3.1/PT.00/2025. This systematic review was registered on the Open Science Framework (OSF) on 8 May 2026. Full article

Extensive research has been conducted to develop technologies that enable paratransit systems to operate autonomously, including advanced sensing technologies and associated software. However, there remains a gap in research addressing adaptive operational algorithms for such systems under stochastic and dynamically evolving demand. To address this gap, this study develops an imitation-learning-assisted deep reinforcement learning (DRL) approach for autonomous shuttle routing. The proposed framework integrates generative adversarial imitation learning with proximal policy optimization to enable sequential pickup and drop-off decision-making under stochastic passenger demand without centralized re-optimization. The DRL agent was trained over approximately 1.5 million training steps and evaluated across 1000 episodes with stochastic passenger generation. Its performance was benchmarked against a deterministic dial-a-ride problem (DARP) solver implemented using Google’s OR-Tools, as well as online heuristic baselines. Results indicate that while heuristic methods achieve lower average time-based performance metrics, the proposed approach is capable of learning adaptive routing policies and demonstrates consistent behavior across diverse demand realizations. These findings highlight the feasibility of learning-based routing in controlled environments and provide a foundation for extending such approaches to more complex and realistic autonomous mobility systems. Full article

Unmanned aerial vehicles (UAVs) are widely used for inspection and monitoring tasks, where mission efficiency is strongly influenced by environmental conditions such as wind. In this work, we study a joint route–speed optimization problem for UAV inspection missions under uncertain wind conditions. The objective is to determine both the visiting sequence of inspection targets and the flight speeds along route segments in order to minimize worst-case energy consumption while satisfying mission duration constraints. We formulate the problem using a robust optimization framework that accounts for uncertainty in both wind speed and wind direction. The resulting model involves coupled discrete routing decisions and continuous speed control variables, which makes the problem computationally challenging. To address this difficulty, we propose a robust route–speed decomposition (RRSD) framework that alternates between route improvement and nonlinear speed optimization. Computational experiments on randomly generated instances, evaluated over eight random seeds per setting and compared against five baselines, including a simulated-annealing metaheuristic, demonstrate that RRSD consistently reduces worst-case energy consumption. A sensitivity analysis over the wind-uncertainty half-widths further shows that this advantage widens as the uncertainty set grows, and comparisons with exact enumeration on small instances confirm near-optimal solution quality at reasonable computational cost. These results highlight the importance of jointly optimizing routing decisions and speed control for energy-efficient UAV mission planning under uncertain environmental conditions. Full article

The joint optimization of routing and three-dimensional loading is a highly complex NP-hard combinatorial problem, particularly when stringent center-of-gravity (CoG) stability constraints are required for unmanned aerial vehicle (UAV) operations. Existing algorithms typically adopt a route-first, load-second evaluation strategy for these interconnected components, often yielding distance-optimal yet physically infeasible solutions. To address this bottleneck, this paper formulates the Three-Dimensional Loading-Constrained UAV Routing Problem (3DLC-UAVRP), integrating unloading sequence consistency, spatial packing feasibility, and CoG deviation control into the routing decision process. A guided collaborative optimization framework, GLS-WSCPA, is proposed, coupling an Improved White Shark Optimization (IWSO) algorithm for global route exploration with a Human-like Divide-and-Conquer Packing Strategy (HLDCPS) for spatial arrangement. Unlike conventional decoupled approaches that treat loading feasibility as a post hoc filter, a Center-of-Gravity-Guided Path Adjustment (CGPA) and Local Loading Repair (LLR) mechanism is introduced to establish a dynamic feedback loop between routing search and loading evaluation, so that CoG violations are actively translated into guided routing perturbations rather than simply triggering solution rejection. Experimental results demonstrate that GLS-WSCPA generally achieves better solutions than the compared algorithms across the tested problem scales, with the performance gap tending to widen as the instance size increases within the tested range. Ablation studies verify the complementary roles of CGPA and LLR, and sensitivity analysis confirms that moderately relaxing payload and CoG constraints reduces routing distance within safety boundaries. Case analysis shows that the proposed method reduces fleet size by 20% and total delivery distance by 6.85% compared to traditional decoupled strategies. Full article

The Multi-Destination Vehicle Routing Problem (MD-VRP) with a heterogeneous electric fleet is a critical challenge in optimizing commuter services for large-scale institutions and logistics operations. To address the complexities of electric fleet composition uncertainty and multi-center routing in “micro-city” campus environments, this paper establishes a robust multi-objective programming model. The model aims to simultaneously minimize three conflicting objectives, the total number of vehicles, total driving distance, and total electric energy consumption (kWh), under constraints of flow conservation and vehicle availability. Considering the nondeterministic polynomial-time hard (NP-hard) nature of the problem, a novel two-stage hybrid heuristic algorithm is proposed. In the first stage, a Modified Kruskal’s algorithm is employed to aggregate scattered stops into optimized clusters to reduce dimensionality. In the second stage, a State-Compressed Dynamic Programming (SC-DP) algorithm is applied to determine the optimal routing and electric vehicle type selection for each cluster. The methodology is validated using a case study of a large-scale campus network with 100 nodes. The optimization results identify an optimal fleet configuration of 41 campus electric commuter vehicles across three specific types (capacities of 45, 55, and 60), resulting in an annual total energy consumption of 5893.98 kWh. Compared with a global Ant Colony Optimization (ACO) baseline in this case study, the proposed framework reduces the required fleet size by 22.6% and annual energy consumption by 9.2%; however, this comparison should be interpreted as a preliminary case-study benchmark because the proposed method adopts a decomposition-based “Cluster-First, Route-Second” strategy. The results indicate that the approach achieves higher solution efficiency, offering an economically and environmentally friendly scheme for electric vehicle fleet operations. Full article

The electrification of field service fleets introduces complex constraints: shift limits, overtime fairness, and battery–range feasibility. This paper proposes the Multi-Shift Single Electric Vehicle Routing Problem under Possibilistic Uncertainty (MS-SEVRP-PU), a formulation focused on a single-vehicle multi-shift planning unit and capturing imprecise travel/service times and state-of-charge dynamics. Travel durations and energy consumption are modelled as triangular fuzzy numbers to reflect expert knowledge when probabilistic data is limited. A closed-form credibility function evaluates overtime risk, while an Ordered Weighted Averaging (OWA) aggregation of per-shift risks ensures fairness by discouraging systematic overload on specific shifts. To solve this multi-objective problem, we develop a Pareto-Conditioned Transformer with risk-aware and battery-conscious large neighbourhood search (PCT-RABLNS), combining a preference-conditioned attention policy with targeted local search. Computational experiments on calibrated municipal maintenance case studies indicate that PCT-RABLNS improves hypervolume by 2–5% over strong baselines and reduces maximum shift overtime risk by 15–25%, with a marginal makespan overhead of only 1–3%. The results demonstrate that the proposed framework is a promising decision-support approach for energy-aware, risk-fair, and operationally compliant planning of single-vehicle, multi-shift electric service operations, jointly integrating multi-shift routing, fuzzy uncertainty, and preference-conditioned reinforcement learning. The paper also discusses how the framework can be extended to multi-vehicle settings. Full article

This study focuses on a large rubber-tired gantry crane (RTGC) operating in a nomadic prefabricated beam yard and develops a Digital Twin-driven dynamic feasible route planning method for the path planning problem under the combined effects of a discrete topological road network, dynamic road occupation, time-window constraints, and right-of-way priority rules. By integrating the existing digitalized yard management system with field-acquired data, an operational-stage updating mechanism for dynamic topology and time windows is established. On this basis, long-vehicle occupancy, time-window constraints, and right-of-way priority rules are embedded into the A* search and rolling replanning process, forming a Digital Twin-based dynamic A* (TD-A*) feasible route planning algorithm under evolving operational constraints. Results from a representative operational case show that, under long-path conditions with significant dynamic constraints, TD-A* reduces transportation distance by 16.8% and operation time by 18.9%. For the full-process results, the transportation distance is reduced by 16.4%, the operation time by 12.8%, and the number of turning maneuvers by 50.0%. The results demonstrate that the proposed method can improve the adaptability of feasible route planning to dynamic road occupation and traffic conflicts in a real nomadic beam yard with a single RTGC, and demonstrate the engineering feasibility of embedding Digital Twin-driven dynamic constraints into the route planning process. Full article

In many real-world logistics systems, two-echelon distribution structures and clustered customer demands often coexist. However, traditional Two-Echelon Vehicle Routing Problems (2E-VRPs) mainly focus on the coordination between depots, satellites, and customers, while usually ignoring clustered customer service requirements. To fill this research gap, this study investigates a novel variant of the 2E-VRP, called the 2E-VRP with Clustered Customers (2E-VRP-CC). In this problem, customers in the second echelon are partitioned into predefined clusters, and all customers within a cluster must be visited consecutively by the same vehicle. For the problem, a Mixed-Integer Linear Programming (MILP) model is first established, followed by the development of an Adaptive Large Neighborhood Search (ALNS) algorithm integrated with a local search method. To validate the effectiveness of the proposed algorithm, comparisons are conducted on instance sets adapted from the literature. For the traditional 2E-VRP, which is a special case of the 2E-VRP-CC, the proposed algorithm is compared with existing methods in the literature. For the proposed 2E-VRP-CC, it is compared with the CPLEX solver. Extensive computational experiments demonstrate that the proposed algorithm can achieve high-quality solutions within relatively short computing times, confirming its effectiveness and efficiency. In addition, sensitivity analysis shows that the number of customer clusters has a significant impact on transportation costs. The results indicate that moderately increasing the number of customer clusters can effectively reduce operational costs and provide practical decision support for customer clustering design and two-echelon logistics planning. Full article

The growing penetration of electric logistics vehicles (ELVs) poses a significant challenge to electric utility site selection. This paper addresses the problem of joint site selection for electric logistics vehicle charging and swapping stations (CSSs). First, a joint site selection model is introduced to characterize the problem, and an improved genetic algorithm (IGA) is designed to solve this model. Derived from the standard genetic algorithm (SGA), the IGA incorporates local search operations, evolutionary inversion operations, and an elitist preservation strategy to enhance performance. On this basis, small-scale numerical simulations are conducted to determine the optimal parameters, thereby guaranteeing optimal algorithmic efficiency. Subsequently, large-scale numerical simulations are performed, with key indicators recorded including the optimal routing length, battery replenishment frequency, number of stations, number of ELVs, and solution time. Finally, analysis across three efficiency levels demonstrates that joint siting improves distribution efficiency by 39.38%, increases grid electricity sales by 46.89%, and reduces total transportation costs by 26.28%, with the optimization scheme validated across six different numerical scenarios. Overall, the joint site selection proposed in this paper has enhanced the benefits of relevant stakeholders and provided a reference for building a low-carbon transportation chain. Full article

Post-disaster rescue scenarios often involve complex and variable terrains, imposing heterogeneous mobility requirements on different transport modes. Single-type vehicles face challenges in independently completing comprehensive rescue tasks. This study addresses the critical problem of coordinating heterogeneous aerial and ground vehicles to collaboratively plan relay rescue routes. To tackle the NP hard multi-terrain, multi-vehicle, and multi-route path planning problem, we propose a New Adaptive Multi-Strategy Particle Swarm Optimization algorithm (AMS-PSO-NEW). The algorithm features a synergistic integration of differential evolution’s multi-strategy mutation, SHADE-based adaptive parameter control, population diversity monitoring with restart mechanisms, and multi-level local search. A sequential hybrid mechanism is designed in which DE-generated trial vectors serve as reference positions for PSO velocity updates, enabling balanced global exploration and local exploitation. By leveraging adaptive parameter tuning, success history memory, and diverse population maintenance, AMS-PSO-NEW effectively overcomes premature convergence and low accuracy issues typical in discrete combinatorial optimization using traditional PSO, achieving a balanced global exploration and local exploitation. Performance validation is conducted over six rescue scenarios varying in scale and complexity, benchmarking AMS-PSO-NEW against nine algorithms: PSO, GA, NSGA-II, GWO, DE, ABC, CS, Q-learning, and MIP. Results demonstrate superior performance across four metrics (rescue success rate, average rescue time, total cost, and fairness), with significant improvements in high-complexity environments. Full article

This study addresses the electric refrigerated vehicle routing problem under a mixed public–private charging mode. An optimization model is developed with the objective of minimizing total cost. The model jointly considers vehicle load capacity, battery capacity, customer time windows, refrigeration energy consumption, cargo damage cost, and the heterogeneity of charging resources. To solve this NP-hard problem, an improved Grey Wolf Optimizer is proposed. The algorithm enhances solution quality and convergence performance through elite individual selection, a “destruction–repair” operator, and an adaptive position update strategy. Experimental results based on modified Solomon benchmark instances show that the proposed model can effectively capture the operational characteristics of electric refrigerated distribution under mixed charging scenarios. The proposed IGWO is compared with GA, GWO, and ALNS over multiple independent runs, and the results reported as means ± standard deviations demonstrate its competitive solution quality and robustness. These findings provide theoretical support for optimizing electric cold-chain distribution systems and coordinating charging resources. 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

Background: In the context of the digital transformation of transport systems and the increasing complexity of logistics flows, the role of intelligent route forming methods capable of accounting for the spatial structure of transport networks, time constraints and resource limitations is growing. This issue is particularly relevant for the Republic of Kazakhstan, which is characterized by a vast territory, a distributed network of transport nodes and significant transit potential. Methods: This article presents an integrated model for the intelligent optimization of freight transportation based on the combined use of the Google OR-Tools library and simulation modeling in the AnyLogic environment with the application of geographic information technologies. The main variants of vehicle routing problems are implemented, including VRPTW, CVRP, and MDVRP. Results: The developed model enables both identification of optimal routes and simulation of their execution in a dynamic environment, forming the basis for a digital twin of the transport system. Experimental studies demonstrate the impact of time constraints, capacity limitations, and spatial structure on routing solutions. Conclusions: The results confirm the effectiveness of the proposed approach for logistics flow distribution in a distributed transport system and its potential for decision support in the digital transport sector. Full article

Inspired by the practical need for reliable drone-assisted logistics under varying weather conditions, this study investigates the vehicle–drone collaborative routing problem with weather constraints and time windows. The objective is to minimize the total delivery cost, including vehicle fixed costs, vehicle travel costs, drone flight costs, and time window penalty costs. To capture the impact of weather conditions on drone operations, a wind-speed-dependent dynamic flight speed function is introduced. A mathematical model is formulated, and an adaptive large neighborhood search algorithm integrated with genetic operations is proposed to enhance both local search efficiency and global exploration capability. Computational experiments on benchmark instances demonstrate that the proposed algorithm obtains high-quality solutions across different problem scales. Compared with the adaptive large neighborhood search algorithm and the improved genetic algorithm, the proposed approach reduces the optimal total delivery cost by an average of 4% and 2%, respectively. Sensitivity analysis further shows that increasing wind speed levels and the proportion of no-fly periods reduces the number of drone service tasks and increases total system cost, highlighting the significant impact of weather conditions on vehicle–drone collaborative delivery systems. Full article