A new variant of multi-depot vehicle routing problem with time windows is studied. In the new variant, the depot where the vehicle ends is flexible, namely, it is not entirely the same as the depot that it starts from. An integer programming model is formulated with the minimum total traveling cost under the constrains of time window, capacity and route duration of the vehicle, the fleet size and the number of parking spaces of each depot. As the problem is an NP-Hard problem, a hybrid genetic algorithm with adaptive local search is proposed to solve it. Finally, the computational results show that the proposed method is competitive in terms of solution quality. Compared with the classic MDVRPTW, allowing flexible choice of the stop depot can further reduce total traveling cost.
Motion planning is a crucial, basic issue in robotics, which aims at driving vehicles or robots towards to a given destination with various constraints, such as obstacles and limited resource. This paper presents a new version of rapidly exploring random trees (RRT), that is, liveness-based RRT (Li-RRT), to address autonomous underwater vehicles (AUVs) motion problem. Different from typical RRT, we define an index of each node in the random searching tree, called "liveness" in this paper, to describe the potential effectiveness during the expanding process. We show that Li-RRT is provably probabilistic completeness as original RRT. In addition, the expected time of returning a valid path with Li-RRT is obviously reduced. To verify the efficiency of our algorithm, numerical experiments are carried out in this paper.
With the rapid growth in the number of vehicles, energy consumption and environmental pollution in urban transportation have become a worldwide problem. Efforts to reduce urban congestion and provide green intelligent transport become a hot field of research. In this paper, a multimetric ant colony optimization algorithm is presented to achieve real-time dynamic path planning in complicated urban transportation. Firstly, four attributes are extracted from real urban traffic environment as the pheromone values of ant colony optimization algorithm, which could achieve real-time path planning. Then Technique for Order Preference by Similarity to Ideal Solution methods is adopted in forks to select the optimal road. Finally, a vehicular simulation network is set up and many experiments were taken. The results show that the proposed method can achieve the real-time planning path more accurately and quickly in vehicular networks with traffic congestion. At the same time it could effectively avoid local optimum compared with the traditional algorithms.
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