Deep Reinforcement Learning for the Capacitated Pickup and Delivery Problem with Time Windowsстатья
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Статья опубликована в журнале из перечня ВАК
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 15 февраля 2024 г.
Аннотация:The vehicle routing problem with pickup and delivery is one of the most important problems in the context of global urban population growth. Although these kinds of small-size problems can be solved using various classical approaches, a fast (or real-time) route optimizer under real-world constraints (such as throughput and time window constraints) for medium- and large-size problems is still a challenge. In this work, we first successfully applied a deep reinforcement learning approach (a modified JAMPR model) to solve the capacitated pickup and delivery problem with time windows (CPDPTW). We obtained a robust model that gives a fast optimal solution for small- to medium-size problems and gives a fast suboptimal solution for large-size (>200) problems.