Vehicle Scheduling Research Articles

Bi-Level Optimization for De-Icing Position Allocation and Unmanned De-Icing Vehicle Fleet Routing Problem.

Aircraft icing due to severe cold and local factors increases the risk of flight delays and safety issues. Therefore, this study focuses on optimizing de-icing allocation and adapting to dynamic flight schedules at medium to large airports. Moreover, it aims to establish a centralized de-icing methodology employing unmanned de-icing vehicles to achieve the dual objectives of minimizing flight delay times and enhancing airport de-icing efficiency. To achieve these goals, a mixed-integer bi-level programming model is formulated, where the upper-level planning guides the allocation of de-icing positions and the lower-level planning addresses the collaborative scheduling of the multiple unmanned de-icing vehicles. In addition, a two-stage algorithm is introduced, encompassing a Mixed Variable Neighborhood Search Genetic Algorithm (MVNS-GA) as well as a Multi-Strategy Enhanced Heuristic Greedy Algorithm (MSEH-GA). Both algorithms are rigorously assessed through horizontal comparisons. This demonstrates the effectiveness and competitiveness of these algorithms. Finally, a model simulation is conducted at a major northwestern hub airport in China, providing empirical evidence of the proposed approach's efficiency. The results show that research offers a practical solution for optimizing the use of multiple unmanned de-icing vehicles in aircraft de-icing tasks at medium to large airports. Therefore, delays are mitigated, and de-icing operations are improved.

Integrated Bus Timetabling, Vehicle Scheduling, and Crew Scheduling with a mutation-based evolutionary scheme

Public transport planning is a complex process that covers multiple interdependent planning tasks. Traditionally, these tasks are executed sequentially. However, an integrated approach to planning multiple tasks can improve the overall solution. This study focuses on the timetabling, vehicle scheduling, and crew scheduling problems and evaluates the benefits of integrated solution approaches. A mutation-based evolutionary scheme is proposed and applied to different real-world inspired instances to solve a complex integration of all three planning tasks. Integrated solving of all three problems significantly improves the solution quality compared to a sequential approach. In reasonable computational time, the full integration led to reduced vehicle and duty schedule costs and, as a result, reduced overall costs while keeping the service quality equally high.

A new mathematical formulation for the simultaneous optimization of charging infrastructure and vehicle schedules for electric bus systems

This paper presents a new mixed integer problem (MIP) formulation for the Charging Location and Electric Vehicle Scheduling Problem (CLEVSP). The CLEVSP aims at minimizing the investment costs for charging infrastructure and electric buses as well as the operational costs resulting from the vehicle schedule of an electric bus system applying opportunity charging. Leveraging the assumption of a homogeneous vehicle fleet, the presented MIP is based on two-index decision variables for vehicle scheduling. The formulation allows finding near-optimal solutions for real-world instances of small and medium-sized cities and estimating their solution quality. The computational experiments show that the model, when solved with the Gurobi optimization software, is competitive with state-of-the-art solution methods for the CLEVSP even for problem instances with several thousand service trips. The model's convergence behavior strongly depends on the difficulty of the problem, which is not only influenced by the number of service trips but also by the available battery capacity of the electric buses.

Optimized path planning and scheduling strategies for connected and automated vehicles at single-lane roundabouts.

This paper focuses on the cooperative driving challenges of connected and automated vehicles (CAVs) at single-lane roundabouts. First, a geometric path planning method is proposed for CAVs navigating a single-lane roundabout. Based on this method, a vehicle roundabout model is established. Four potential traffic scenarios for CAVs are established, and the optimal arrival times at conflict points are analyzed. By correlating the optimal arrival times at conflict points with the optimal entry times into the roundabout, the multi-vehicle coordination problem in complex intersections is simplified to a speed control issue during entry. Utilizing the principles of optimal control and Pontryagin minimization, two speed optimization strategies are proposed. Finally, MATLAB is employed for simulation analysis. The results indicate that the control strategy proposed in this paper enables the system to clearly identify potential conflicts between vehicles and implement an optimal control strategy, ensuring that vehicles can navigate the roundabout efficiently in terms of time and fuel without collisions. Additionally, the minimum time interval is established at 0.2 seconds to completely prevent vehicle collisions. In this study, the fusion problem involving two vehicles at a single conflict point is further expanded to encompass multiple vehicles at multiple conflict points. Thus, the efficient scheduling of multiple vehicles in single-lane roundabouts is realized.

Bi-directional Optimal Scheduling of Electric Vehicles Based on V2G Technology

Bi-directional Optimal Scheduling of Electric Vehicles Based on V2G Technology

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles

A Model for Scheduling of Electric Vehicles Charging in a Distribution Network using Multi-agent Model

A Model for Scheduling of Electric Vehicles Charging in a Distribution Network using Multi-agent Model

An Improved Bilevel Algorithm Based on Ant Colony Optimization and Adaptive Large Neighborhood Search for Routing and Charging Scheduling of Electric Vehicles

An Improved Bilevel Algorithm Based on Ant Colony Optimization and Adaptive Large Neighborhood Search for Routing and Charging Scheduling of Electric Vehicles

Environmental benefits of taxi ride-sharing in New York City

This paper explores the potential environmental benefits of ride-sharing in New York City by finding a balance between supply and demand; for the supply side considering factors such as distance and emissions while taking into account demand-side factors like waiting time and deviation from ride time (DRT). A heuristic algorithm called ADARTW (Advanced Dial-A-Ride problems with Time Windows) is used for a time-constrained version of the Dial-A-Ride problem. The algorithm creates a "pick-up window" for each request and assigns customers to vehicles by finding feasible customer insertions into the work schedules of vehicles. Furthermore, a cost function is employed to optimize the insertion process to select the best customer insertion within the algorithm. This cost function takes into consideration several key factors. Then employs a nonlinear objective function to guide the insertion process and estimate the potential reduction in the number of vehicles required for transportation. The study reveals that ride-sharing could reduce the number of vehicles by 52% and greenhouse gas emissions by 35% in NYC.

Digital Twin-Driven Reinforcement Learning Method for Marine Equipment Vehicles Scheduling Problem

Digital Twin-Driven Reinforcement Learning Method for Marine Equipment Vehicles Scheduling Problem

Spatio-temporal information enhance graph convolutional networks: A deep learning framework for ride-hailing demand prediction.

Ride-hailing demand prediction is essential in fundamental research areas such as optimizing vehicle scheduling, improving service quality, and reducing urban traffic pressure. Therefore, achieving accurate and timely demand prediction is crucial. To solve the problems of inaccurate prediction results and difficulty in capturing the influence of external spatiotemporal factors in demand prediction of previous methods, this paper proposes a demand prediction model named as the spatiotemporal information enhance graph convolution network. Through correlation analysis, the model extracts the primary correlation information between external spatiotemporal factors and demand and encodes them to form feature units of the area. We utilize gated recurrent units and graph convolutional networks to capture the spatiotemporal dependencies between demand and external factors, respectively, thereby enhancing the model's perceptiveness to external spatiotemporal factors. To verify the model's validity, we conducted comparative and portability experiments on a relevant dataset of Chengdu City. The experimental results show that the model's prediction is better than the baseline model when incorporating external factors, and the errors are very close under different experimental areas. This result highlights the importance of external spatiotemporal factors for model performance enhancement. Also, it demonstrates the robustness of the model in different environments, providing excellent performance and broad application potential for ride-hailing prediction studies.

Hybrid Tabu-Grey wolf optimizer algorithm for enhancing fresh cold-chain logistics distribution.

The increasing public demand for fresh products has catalyzed the requirement for cold chain logistics distribution systems. However, challenges such as temperature control and delivery delays have led a significant product loss and increased costs. To improve the current situation, a novel approach to optimize cold chain logistics distribution for fresh products will be presented in the paper, utilizing a hybrid Tabu-Grey wolf optimizer (TGWO) algorithm. The proposed hybrid approach combines Tabu search (TS) and Grey wolf optimizer (GWO), employing TS for exploration and GWO for exploitation, aiming to minimize distribution costs in total and establish efficient vehicle scheduling schemes considering various constraints. The effectiveness of the TGWO algorithm is demonstrated through experiments and case studies compared to other heuristic algorithms. Comparative analysis against traditional optimization methods, including Particle swarm optimization (PSO), Whale optimization algorithm (WOA), and original GWO, highlights its superior efficiency and solution quality. This study contributes theories by demonstrating the efficacy of hybrid optimization techniques in complex supply chain networks and dynamic market environments. The practical implication lies in the implementation of TGWO to bolster distribution efficiency, cost reduction, and product quality maintenance throughout the logistics process, offering valuable insights for operational and strategic improvements by decision-makers. However, the study has limitations in generalizability and assumptions, suggesting future research areas including exploring new search operators, applying additional parameters, and using the algorithm in diverse real-life scenarios to improve its effectiveness and applicability.

Development of Synchronous Transportation Model for Trackless Vehicle Dispatching During the Construction of Long‐Distance Single‐Headed Shield Tunnel

With the increasing demands for long‐distance single‐headed tunnels, trackless vehicles have been widely accepted for transporting construction materials from storage yard to working face. During the trackless transportation of construction materials, the synchronization of material supply and shield tunneling is critical to ensure tunneling efficiency. However, there is still a lack of a reliable synchronous transportation model for trackless vehicle dispatching. In the present study, the material transportation processes and vehicle transportation parameters have been investigated based on the Sutong GIL Yangtze River Crossing Cable Tunnel. The synchronous transportation model has been established to improve transportation efficiency during the construction stage. An optimized scheme of vehicle schedule has been proposed to minimize trackless vehicle and retention time. Comparative analysis has been carried out to evaluate the trackless vehicle and retention time between the optimized scheme and the original scheme. The results indicate that the number of dual‐headed trucks required for material transportation decreases from nine to six, and the maximum retention time decreases from 166 to 35 min. Compared with the original scheme, the optimized scheme is available for reducing the number of trackless vehicles and improving the efficiency of material transportation. The present study contributes to optimizing material transport scheduling and synchronizing material supply with tunnel excavation in long‐distance single‐headed shield tunnels.

A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

A Joint Distributed Optimization Framework for Voltage Control and Emergency Energy Storage Vehicle Scheduling in Community Distribution Networks

Exploring Symmetry-Induced Divergence in Decentralized Electric Vehicle Scheduling

Due to the low computational efficiency and high communication pressure caused by centralized control, decentralized coordination of electric vehicles (EVs) has become a research hotspot in recent years. However, the integration of numerous EVs with homogeneous parameters brings unprecedented challenges for the optimal scheduling of the power system. To explore symmetry-induced divergence in decentralized electric vehicle scheduling, the established centralized optimization scheduling model of electric vehicles is decomposed into a distributed optimization model by Lagrange relaxation, achieving parallel control of each EV aggregator (EVA). To solving the oscillation caused by parameter symmetry in the process of optimization, we propose a convergence acceleration algorithm based on a perturbation function, which is a random distribution function used to break parameter symmetry within an acceptable calculation error range. On the premise of sensitivity analysis, a closed form of the perturbation limit is theoretically derived. Case studies based on different scales of users validate the effectiveness and computational efficiency of the proposed method.

Automated Guided Vehicle Scheduling Problem in Manufacturing Workshops: An Adaptive Parallel Evolutionary Algorithm

Automated Guided Vehicle Scheduling Problem in Manufacturing Workshops: An Adaptive Parallel Evolutionary Algorithm

Optimal Solution for the Integrated Model of the Problems in Capacitated Vehicle Routing and Vehicle Scheduling at the Multi-door Depot

To have an efficient Supply Chain (SC), the coordination and integration of the activities in the SC are mandatory. Routing vehicles is an optimization problem in the SC. When the routed vehicles return to the depot and the doors at the depot are busy, the returned vehicles must wait to unload the accumulated shipments. Therefore, properly scheduling these vehicles to those doors at the depot to minimize the waiting time is an optimization problem in the SC. Therefore, in this study, routing vehicles to collect the shipments from suppliers and scheduling vehicles to doors at the depot, based on a first-come-first-serve basis, are simultaneously solved. Hence, the objective of this integrated vehicle routing and scheduling problem (VR&SP) is to minimize the total cost which contains the following components: vehicle travelling cost between suppliers, loading cost at the suppliers, vehicle waiting cost, unloading cost at the depot and vehicle operations cost. A Mixed Integer Quadratic Programming (MIQP) model is developed to solve the integrated VR&SP. The Branch and Bound algorithm method is employed to obtain the exact optimal solution to this MIQP using LINGO optimization software. The compatibility of the developed MIQP model is verified by the randomly generated small-scale instances of VR&SP. Therefore, it can be concluded that, this model solves the vehicle routing to suppliers and vehicle scheduling to doors at the depot simultaneously. Since VR&SP is a NP-hard problem, heuristics or meta-heuristic methods are recommended to solve the large-scale instances of VR&SP. Moreover, it can be recommended for further studies to amend this model by incorporating additional constraints to make it more applicable to real-world scenarios.

Parallel drone scheduling vehicle routing problems with collective drones

We study last-mile delivery problems where trucks and drones collaborate to deliver goods to final customers. In particular, we focus on problem settings where either a single truck or a fleet with several homogeneous trucks work in parallel to drones, and drones have the capability of collaborating for delivering missions. This cooperative behavior of the drones, which are able to connect to each other and work together for some delivery tasks, enhance their potential, since connected drone has increased lifting capabilities and can fly at higher speed, overcoming the main limitations of the setting where the drones can only work independently.In this work, we contribute a Constraint Programming model and a valid inequality for the version of the problem with one truck, namely the Parallel Drone Scheduling Traveling Salesman Problem with Collective Drones and we introduce for the first time the variant with multiple trucks, called the Parallel Drone Scheduling Vehicle Routing Problem with Collective Drones. For the latter version of the problem, we propose two Constraint Programming models and a Mixed Integer Linear Programming model.An extensive experimental campaign leads to state-of-the-art results for the problem with one truck and some understanding of the presented models’ behavior on the version with multiple trucks. Some insights about future research are finally discussed.

Consistent routing for local same-day delivery via micro-hubs

An increasing number of local shops offer same-day delivery in order to compete with the online giants. However, the distribution of parcels from individual shops to customers reduces the rare consolidation opportunities in the last mile even further. Thus, shops start collaborating on urban same-day delivery by using shared vehicles and micro-depots for consolidated transportation of parcels. At this, many stakeholders (storekeepers, drivers, and customers) need to be coordinated. Consistent routes between micro-hubs simplify the distribution process and increase reliability for all stakeholders involved. The shared vehicles thus conduct consistent daily routes between micro-hubs in the city, serving as transshipment and consolidation centres. This allows stores to bring orders to the next micro-hub, where the parcel is picked up by a vehicle and delivered to the micro-hub closest to its destination—if it is feasible with respect to the vehicle’s consistent daily schedule. Creating effective schedules is therefore very important. The difficulty of finding an effective consistent route is amplified by the daily uncertainty in order placements. We model the problem as a two-stage stochastic program. While the first stage determines the vehicle schedules, the second stage optimises the flow of realised orders. The goal is to satisfy as many orders per day as possible with the shared vehicles. We propose a time-expanded network formulation of the problem which is solved to optimality using commercial MIP-software. We assess our model against a non-consistent upper bound and a practically-inspired heuristic to evaluate the cost of consistency and the consolidation of goods. We analyse the performance of our method for a variety of instance settings. We observe that collaborative delivery via micro-hubs is worthwhile for delivery time promises of two hours or more. Noticeably, for these service promises, the costs of consistency are surprisingly low.

Research on Vehicle Scheduling Optimization Model of Precast Concrete Components Considering Carbon Emission Cost

The distribution of precast concrete (PC) components has the characteristics of high time penalty costs, transportation costs, long-term accumulated carbon emission, and long unloading waiting time. These characteristics result in increased distribution costs. Aiming at this problem, the vehicle scheduling (VS) optimization model was proposed to minimize total distribution costs, including transportation costs, unloading waiting costs, carbon emissions costs, and so forth. The proposed model was solved using the genetic algorithm. Finally, the proposed model is verified by a simulation study. The main conclusions are: 1) A PC-component VS model aiming at minimum cost is established, which can provide a better scheme for PC-component VS on the basis of minimizing fixed cost, transportation cost, unloading waiting cost, penalty cost, and carbon cost. 2) This paper constructs a VS model that reflects the loading characteristics of PC components. The type of PC component is added as a judgment variable in the model, and the actual loading weight is calculated according to the type of PC component and the loading requirements. It improves the accuracy of PC component VS results and expands the PC component VS model. 3) Based on this model, some valuable parameters have been sensitively analyzed and some valuable conclusions have been drawn to provide some references for similar cases. For example, carbon emission increases with the increase of freight volume and so on. The result of this study is to provide a cost-minimization VS model with PC components.