Smart Routes: A System for Development and Comparison of Algorithms for Solving Vehicle Routing Problems with Realistic Constraints

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Задача оптимизации маршрутов с реалистичными ограничениями становится крайне актуальной в условиях глобального роста городского населения. Подходы к оптимизации маршрутов, включая точные методы, сталкиваются с проблемой экспоненциальной сложности при увеличении размера задачи оптимизации маршрутов. В работе сравнивается точный решатель SCIP с эвристическими методами (LKH, 2-OPT, 3-OPT, ORTools) и моделью глубокого обучения JAMPR+. Для задач размером 50 глубокое обучение и классические эвристики достигают точности, сравнимой с SCIP, но требуют меньше времени. Для задач размером 100, эвристики и нейронные сети значительно опережают SCIP как по времени, так и по качеству первого найденного решения. Для проведения экспериментов разработана платформа Smart Routes для решения задачи оптимизации маршрутов, которая включает в себя точные, эвристические и нейросетевые модели и облегчает удобное интегрирование собственных алгоритмов и наборов данных.

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