On the truck dispatching problem

Classification of resource management approaches in fog/edge paradigm and future research prospects: a systematic review

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications. 

https://www.techrxiv.org/articles/preprint/Cooperative_Platoon_Formation_of_Connected_and_Autonomous_Vehicles_Towards_Efficient_Merging_Coordination_at_Unsignalized_Intersections/21152128/2/files/38685982.pdf

Cooperative Platoon Formation of Connected and Autonomous Vehicles: Toward Efficient Merging Coordination at Unsignalized Intersections

Automated Guided Vehicle (AGV) have received a lot of attention in recent years in terms of both hardware and software research. Nowadays, the AGV offer more adaptable and effective industrial and transportation system solutions. An AGV navigation techniques is essential to its operation. The decision to use AGV navigation is not simple, though to be suite to the application and sufficient. This paper survey the navigation approaches applied in AGV in the past five years of published academic research. In doing so, this work responds to three related questions: (1) is the AGV classical navigation techniques still relevant to the current application area; (2) is the heuristic navigation techniques itself able to optimize AGV movement in term of guide and strategy; (3) is the use of Artificial Intelligence (AI) in AGV navigation techniques able to increase system performance. As a result, numerous techniques for the AGV navigation have been developed globally. On the other hand, the most popular navigation approaches are provided below for more research. 

/pdf/a-survey-on-navigation-approaches-for-automated-guided-17wn8tng.pdf

A Survey on Navigation Approaches for Automated Guided Vehicle Robots in Dynamic Surrounding

A Multi-Layer Collaboration Framework for Industrial Parks with 5G Vehicle-to-Everything Networks

An improved NSGA-II with local search for multi-objective integrated production and inventory scheduling problem

Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

In future transportation, on board unit (OBU) is a key component of connected vehicles with limited computing resources, and may not tackle the heavy computing burden from V2X networks. For these cases, we herein employ multi ‐ access edge cloud (MEC) and remote cloud to schedule the OBUs' tasks. This schedule tries to minimise the total completion time of all tasks and the number of computing units of the MEC server. We first introduce a multi ‐ objective optimisation model considering the tasks and cloud ‐ edge collaboration. Then, we propose a task scheduling strategy considering the resource matching degree for this model. In this strategy, we propose an improved hybrid genetic algorithm and employ the resource matching measure between the tasks and computing units in terms of computing, storage and network bandwidth resources to obtain better solutions for generations. The numerical results showed the effectiveness of our strategy.

Vehicular task scheduling strategy with resource matching computing in cloud-edge collaboration

This paper formulates the airport ferry vehicle scheduling problem as unrelated parallel machine scheduling (UPMS) problem to achieve a better reduction in the weighted sum of flight tardiness. In the UPMS model, the departure time and the estimated end time of each ferry vehicle for each ferry task is simultaneously planned with the allocation of ferry vehicles, and the specific journey of each ferry vehicle for each task is arranged. A variable neighborhood search (VNS) algorithm is proposed to address the model. Three scheduling rules including random allocation, service time in turn (STT), and minimum maximum completion time (MCT) are designed to combine with VNS as comparisons, together with five neighborhood structures suited for the airport ferry vehicle scheduling problem. Comparative experiments are carried out by using two weeks of real flight data from a major international airport in China. Experimental results show the superiority of the proposed MCT initialization combined with VNS (MCT_VNS) for the airport ferry vehicle scheduling problem. The solutions obtained by MCT_VNS can provide the allocation of the airport ferry vehicles to flights that need ferry services. 

A variable neighborhood search algorithm for airport ferry vehicle scheduling problem

The signal control system for multi-intersection traffic management is an extensive complex nonlinear and non-equilibrium system and usually leads to a weak control strategy. Existing studies of traffic dynamics mainly focus on the applications to the single network, whereas how the structure of multiple intersections affects the dynamical behaviors is unclear. Connected vehicles in recent studies have shown the potential to eliminate traffic communication barriers and balance the traffic load. For this consideration, we propose a new multi-intersection model based on Vehicle-to-Everything (V2X) network to capture the features of traffic flow, which employs vehicular communication to obtain real-time traffic information and share it among the adjacent intersections. Focus on smoothing traffic flow without stop-and-go driving and improving the coordination of multiple traffic lights, we develop a multi-intersection coordination algorithm based on V2X (MICA-V for short) to maximize the green waveband in the arterial road. Due to the establishment of registration list yielding the average saturated headway time, our method is of high efficiency—the distributed process of data makes the convergence to be very fast with nearly linear time. Simulation results via MATLAB with Simulation of Urban Mobility (SUMO) showed the effectiveness of the proposed MICA-V in improving traffic capacity, reducing the number of stop behaviors, and improve the average speed of vehicles moving on the arterial road.

/pdf/a-coordination-algorithm-for-signalized-multi-intersection-43cqppnqbn.pdf

A Coordination Algorithm for Signalized Multi-Intersection to Maximize Green Wave Band in V2X Network

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Papers

An improved NSGA-II with local search for multi-objective integrated production and inventory scheduling problem

Scheduling multi–mode resource–constrained tasks of automated guided vehicles with an improved particle swarm optimization algorithm

Vehicular task scheduling strategy with resource matching computing in cloud-edge collaboration

A variable neighborhood search algorithm for airport ferry vehicle scheduling problem

A Coordination Algorithm for Signalized Multi-Intersection to Maximize Green Wave Band in V2X Network