Solving the capacitated vehicle routing problem with time windows via graph convolutional network assisted tree search and quantum-inspired computing
نویسندگان
چکیده
Vehicle routing problems are a class of NP-hard combinatorial optimization which attract lot attention, as they have many practical applications. In recent years there been new developments solving vehicle with the help machine learning, since learning how to automatically solve has potential provide big leap in technology. Prior work on using mainly focused auto-regressive models, connected high computational costs when combined classical exact search methods model be evaluated every step. This paper proposes method for approximately capacitated problem time windows (CVRPTW) via supervised deep learning-based approach non-autoregressive manner. The uses neural network assist finding solutions by providing probability distribution is used guide tree search, resulting assisted heuristic. built upon architecture, called graph convolutional network, particularly suited tasks. Furthermore, formulation CVRPTW form quadratic unconstrained binary (QUBO) presented and solved quantum-inspired computing cooperation Fujitsu, where learned reduction based proposed applied circumvent limitations concerning usage quantum large instances. Computational results show that models perform very well small medium sized instances compared state-of-the-art solution terms quality, outperform commercial solvers
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ژورنال
عنوان ژورنال: Frontiers in Applied Mathematics and Statistics
سال: 2023
ISSN: ['2297-4687']
DOI: https://doi.org/10.3389/fams.2023.1155356