FASTER CAPACITATED ARC ROUTING: A SEQUENCE-TO-SEQUENCE APPROACH

Faster Capacitated Arc Routing: A Sequence-to-Sequence Approach

Faster Capacitated Arc Routing: A Sequence-to-Sequence Approach

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The Capacitated Arc Routing Problem (CARP) is an NP-hard optimization problem that has been investigated for decades.Heuristic search methods are commonly used to solve it.However, given a CARP instance, most heuristic search algorithms Upholstered Barstool require plenty of time to iteratively search for the solution from scratch, and hence may be impractical for emerging applications that need a solution to be obtained in a very short time period.In this work, a novel approach to efficiently solve CARP is presented.

The proposed approach replaces the heuristic search process with the inference phase of a trained Deep Neural Network (DNN), which is trained to take a CARP instance as the input and outputs a solution to the instance.In this way, CARP could be solved by a direct Spike mapping rather than by iterative search, and hence could be more efficient and more easily accelerated by the use of GPUs.Empirical study shows that the DNN-based solver can achieve significant speed-up with minor performance loss, and up to hundreds of times acceleration in extreme cases.

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