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- W4385377096 abstract "This paper presents a novel app roach for solving the multi-objective vehicle routing problem (MOVRP) using deep reinforcement learning. The MOVRP considered in this study involves two objectives: travel distance and altitude difference. To address this problem, we employ a decomposition strategy based on weights to decompose the multi-objective problem into multiple scalar subproblems. We then use a pointer network to solve each subproblem and train the policy network’s parameters using the policy gradient algorithm of reinforcement learning to obtain the Pareto front solutions of the entire problem. The proposed method provides an effective solution to the MOVRP, and experimental results demonstrate its superiority over traditional optimization methods in terms of solution quality and computational efficiency. Furthermore, the method exhibits strong generalization and adaptability, enabling it to handle multi-objective vehicle routing problems of varying sizes and features with significant flexibility and practicality. The proposed method’s distinct advantages make it a promising solution to the MOVRP." @default.
- W4385377096 created "2023-07-30" @default.
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- W4385377096 date "2023-01-01" @default.
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- W4385377096 title "Deep Reinforcement Learning for Solving Multi-objective Vehicle Routing Problem" @default.
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- W4385377096 doi "https://doi.org/10.1007/978-981-99-4755-3_13" @default.
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