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Genetic Search of Pickup and Delivery Problem Solutions for Self-driving Taxi Routing

机译:自动驾驶出租车路线取送问题解决方案的遗传搜索

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Self-driving cars belong to rapidly growing domain of cyber-physical systems with many open problems. In this paper, we study routing problem for taxis. In mathematical terms, it is well-known Pickup and Delivery problem (PDP). We use with the standard small-moves technique, which is to apply small changes to a solution for PDP in order to obtain a better one; and an approach that works with small-moves as mutations in genetic algorithms. We propose a strategy-based framework for managing set of small changes and suggest different strategies. We tested algorithms for routing on real-world dataset on taxi orders to airports in United Kingdom. The results show that algorithms using mixed strategies outperform algorithms using a single small move.
机译:无人驾驶汽车属于网络物理系统快速增长的领域,存在许多未解决的问题。在本文中,我们研究出租车的路线问题。用数学术语来说,这是众所周知的取件和传送问题(PDP)。我们使用标准的小动作技术,该技术是对PDP解决方案进行小的更改,以获得更好的解决方案。以及一种在遗传算法中以小动作作为突变的方法。我们提出了一个基于策略的框架来管理一组小的更改,并提出了不同的策略。我们测试了将出租车订单上的真实数据集路由到英国机场的算法。结果表明,使用混合策略的算法要比使用单个小步骤的算法要好。

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