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

Abstract : 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.
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Communication dans un congrès
Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.348-355, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_30〉
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Viacheslav Shalamov, Andrey Filchenkov, Anatoly Shalyto. Genetic Search of Pickup and Delivery Problem Solutions for Self-driving Taxi Routing. Lazaros Iliadis; Ilias Maglogiannis. 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. IFIP Advances in Information and Communication Technology, AICT-475, pp.348-355, 2016, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-319-44944-9_30〉. 〈hal-01557588〉

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