Vehicle Dispatching System Research Articles

Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform

The vehicle dispatching system is one of the most critical problems in online ride-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle dispatching problem called deep dispatching, by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle dispatching algorithm are designed based on industrial-scale real-world data and the workflow of online ride-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle dispatching problem is translated in analogy with the load balancing problem in computer networks. Inspired by the recommendation system, the problem of high concurrency of dispatching requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to existing benchmarks. It is also worth noting that the proposed approach won first place in the vehicle dispatching task of KDD Cup 2020.

Research on overall optimization for a vehicle-dispatching system

Research on overall optimization for a vehicle-dispatching system

Application of ant colony optimization to logistics vehicle dispatching system

The thorough research on ant colony algorithm points out that the ant colony algorithm has superiority in solving large nonlinear optimization problem.Through careful analysis of the deficiencies that genetic algorithm and particle swarm algorithm solve the problem of vehicle dispatching system,based on the advantage of ant colony algorithm and the own characteristics of vehicle dispatching system,the basic ant colony algorithm was improved in the paper,and the algorithm framework was created.Based on the linear programming theory,the article established mathematical model and operation objectives and constraints for vehicle dispatching system,and got the optimal solution of vehicle dispatching system problem with the improved ant colony algorithm.According to the optimal solution and the dispatching criterion real-time scheduling was achieved.The article used Java language to write a simulation program for comparing the improved particle swarm optimization algorithm and ant colony algorithm.Through the comparison,it is found a result that the improved ant colony algorithm is correct and effective to solve the vehicle dispatching optimization problem.

Research on Monitoring Image Compression and Transmission for Airport Special Vehicle Dispatching System

Research on Monitoring Image Compression and Transmission for Airport Special Vehicle Dispatching System

A real-time vehicle-dispatching system for consolidating milk runs

This study investigates the parameter settings of a real-time vehicle-dispatching system for consolidating milk runs. Seven modules are used to implement the real-time system and the parameters are determined by a comprehensive experimental design. The real-time vehicle-dispatching system will be demonstrated in different milk run scenarios. The results of the experiments suggest that the system should have an initial vehicle dispatching module and an inter-route improvement module. It is also recommended that the Best Fit algorithm for initial vehicle dispatch and the 2-Exchange algorithm for inter-route improvement are most suitable for the real-time system.

Vehicle dispatching system based on Taguchi-tuned fuzzy rules

This paper presents the development of a dispatching system for a fleet of automated guided vehicles in a flexible manufacturing environment which is based on a hybrid Fuzzy–Taguchi approach. A fuzzy decision-making system emulates the human behavior necessary for multi-objective directed decision making in a dynamically evolving environment. A statistical approach based on the Taguchi method tunes the fuzzy rules to achieve near optimal performance. Simulation results demonstrate the effectiveness of this marriage of computational tools in dealing with the well-known NP-complete scheduling problem.