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"Route planning"
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A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning
Faced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are heavily constrained by well-established route options, and it is challenging to accommodate dynamic passenger flows effectively and with a good level of resource utilization performance. Inspired by the philosophy of sharing economy, many of the available transport resources on the road, such as minibuses and private vehicles, can offer opportunities for improvement if they can be effectively incorporated and exploited. In this regard, this paper proposes a metric learning-based prediction algorithm which can effectively capture the demand pattern and designs a route planning optimizer to help bus operators effectively deploy fixed routing and cooperative buses with traffic dynamics. Through extensive numerical studies, the performance of our proposed metric learning-based Generative Adversarial Network (GAN) prediction model outperforms existing ways. The effectiveness and robustness of the prediction-supported routing planner are well demonstrated for a real-time case. Further, managerial insights with regard to travel time, bus fleet size, and customer service levels are revealed by various sensitivity analysis.
Journal Article
A coupled high-resolution hydrodynamic and cellular automata-based evacuation route planning model for pedestrians in flooding scenarios
Flooding is now becoming one of the most frequent and widely distributed natural hazards, with significant losses to human lives and property around the world. Evacuation of pedestrians during flooding events is a crucial factor in flood risk management, in addition to saving people’s lives and increasing time for rescue. The key objective of this work is to propose a shortest evacuation path planning algorithm by considering the evacuable areas and human instability during floods. A shortest route optimization algorithm based on cellular automata is established while using diagonal distance calculation methods in heuristic search algorithms. The Morpeth flood event that occurred in 2008 in the UK is used as a case study, and a highly accurate and efficient 2D hydrodynamic model is adopted to discuss the flood characteristics in flood plains. Two flood hazard assessment approaches [i.e., empirical and mechanics-based and experimental calibrated (M&E)] are chosen to study human instability. A comprehensive analysis shows that extreme events are better identified with mechanics-based and experimental calibration methods than with an empirical method. The result of M&E is used as the initial condition for the Morpeth evacuation scenario. Evacuation path planning in Morpeth shows that this algorithm can realize shortest route planning with multiple starting points and ending points at the microscale. These findings are of significance for flood risk management and emergency evacuation research.
Journal Article
Route-Planning Method for Plant Protection Rotor Drones in Convex Polygon Regions
Aiming at the problem of low operating efficiency due to the poor endurance of plant protection rotor drones and the small volume of pesticide carried, this paper proposes a route-planning algorithm for convex polygon regions based on the initial heading angle. First, a series of coordinate conversion methods ranging from the Earth coordinate system to the local plane coordinate system are studied. Second, in the local plane coordinate system, a route generation method based on subregion is proposed; therefore, multiple routes can be generated with different initial heading angles. Lastly, the optimal route and the best initial heading angle can be obtained after the comparison according to the three evaluation criteria: number of turns, route distance, and pesticide waste rate. The simulation results show that, compared with the common grid method, the route generation method based on subregion reduces the route distance and pesticide waste rate by 2.27% and 13.75%, respectively. Furthermore, it also shows that, compared with the route generated by the initial heading angle of 0°, the optimal route reduces the number of turns, route distance, and pesticide waste rate by 60%, 17.65%, and 38.18%, respectively. The route was optimized in three aspects and reached the best overall result using this method, which in turn proved its feasibility.
Journal Article
Efficient Route Planning for Real-Time Demand-Responsive Transit
Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetables and determines the stop and the start according to the demands. This study explores the optimization of dynamic vehicle scheduling and real-time route planning in urban public transportation systems, with a focus on bus services. It addresses the limitations of current shared mobility routing algorithms, which are primarily designed for simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. The research introduces an route planning algorithm designed to dynamically accommodate passenger travel needs and enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based, multi-objective heuristic A* approach, offering a solution to the inflexibility and limited coverage of suburban bus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based on Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, route length, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposed algorithm significantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existing models, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. The study concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalable solution for improving urban mobility.
Journal Article
Multicriteria Ship Route Planning Method Based on Improved Particle Swarm Optimization–Genetic Algorithm
With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.
Journal Article
Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey
Automated Guided Vehicles (AGVs) have been introduced into various applications, such as automated warehouse systems, flexible manufacturing systems, and container terminal systems. However, few publications have outlined problems in need of attention in AGV applications comprehensively. In this paper, several key issues and essential models are presented. First, the advantages and disadvantages of centralized and decentralized AGVs systems were compared; second, warehouse layout and operation optimization were introduced, including some omitted areas, such as AGVs fleet size and electrical energy management; third, AGVs scheduling algorithms in chessboardlike environments were analyzed; fourth, the classical route-planning algorithms for single AGV and multiple AGVs were presented, and some Artificial Intelligence (AI)-based decision-making algorithms were reviewed. Furthermore, a novel idea for accelerating route planning by combining Reinforcement Learning (RL) and Dijkstra’s algorithm was presented, and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.
Journal Article
Tourism route optimization based on improved knowledge ant colony algorithm
With the rapid development of tourism in the economy, popular demand for tourism also increases. Unreasonable distribution arises a series of problems such as reduction of tourist satisfaction and decrease of the income in tourist attractions. Based on consideration of tourism route planning, a mathematical model which takes the maximization of the overall satisfaction of all tourist groups as the objective function is established by taking the age and preferences of tourists, the upper limits of the tourist carrying capacity in various tourism routes, etc. as constraints. It aims to maximize income in tourist attractions while improving tourist satisfaction. Based on the tourist data of a travel agency, the statistical ideas of hierarchical clustering and random sampling are utilized to process the acquired data to obtain the simulation examples in the article. Aiming at this model, a knowledge-based hybrid ant colony algorithm is designed. On this basis, the mechanism of bacterial foraging algorithm is introduced. It improves the performance of the algorithm and avoids the generation of local optimal solution. At the same time, two knowledge models are in addition to improve the solution quality of the algorithm. Typical simulation indicates that the improved ant colony algorithm can find the optimal solution at a higher efficiency when solving the tourism route planning problem. The model can also satisfy the economic benefit of enterprises and achieves favorable path optimization effect under different optional routes, thus further verifying the effect liveness of the model.
Journal Article
An integrated scheduling approach considering dispatching strategy and conflict-free route of AMRs in flexible job shop
To reveal the profound impact of dispatching strategy and route of autonomous mobile robot (AMR) on scheduling in the flexible job shop with AMRs, this study presents the bi-level programming model for integrated scheduling with machines and AMRs (ISMV), subdividing distributed shared dispatching strategy (DSDS) and following dispatching strategy (FDS) for AMRs. The integrated scheduling model is developed at the upper level with the objective of minimizing the makespan, and the AMR conflict-free route planning (CFRP) model is formulated at the lower level to minimize travel time. To solve the model, a novel algorithmic framework (SLGA-D) composed of the self-learning genetic algorithm (SLGA) and Dijkstra with time window (DijkstraTW) is designed. The SLGA is formed by embedding the Q-learning into genetic algorithm to intelligently adjust crossover probability and mutation probability. Several experiments are implemented to validate the SLGA-D and the model proposed in this study. The results of experiments prove that reinforcement learning mechanism within Q-learning successfully enhances the global search ability of the algorithm, and production scheduling and distribution route are optimized synergistically by the model. Sensitivity analyses reveal that, as the number of AMRs rises, the makespan tends to stabilize after decreasing rapidly to a threshold value. Once the number of AMRs exceeds a certain threshold (w=5 in this study), it is not significant to shorten the makespan by continuing to invest AMRs. When the number of production tasks to be processed does not exceed the number of AMRs, following dispatching strategy of AMR is more favorable; conversely, distributed shared dispatching strategy is superior. The results provide guideline and inspiration for managers who are committed to manufacturing schedule and control.
Journal Article
A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields
The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection.
Journal Article