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

Wenjing Hong,Tonglin Liu

doi:10.1109/access.2022.3140783

Abstract

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 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 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.

Highlights

The Capacitated Arc Routing Problem (CARP), with numerous practical applications such as garbage collection and post-delivery [1] is an important NP-hard combinatorial optimization problem that has attracted researchers for decades of investigations [2]–[5]
The main reason is that these approaches are mostly heuristic search algorithms that typically start from scratch to tackle a CARP problem through a trail-anderror procedure [6], [7]
EXPERIMENTS In following experiments, Memetic Algorithm with Extended Neighborhood Search (MAENS) [7], a well-known CARP solver is used as the heuristic method which Deep Arc Routing Solver (DARS) should learn from

Summary

Introduction

The Capacitated Arc Routing Problem (CARP), with numerous practical applications such as garbage collection and post-delivery [1] is an important NP-hard combinatorial optimization problem that has attracted researchers for decades of investigations [2]–[5]. Emerging applications, such as autonomous driving systems, require almost realtime (online) solving of CARP instances. The main reason is that these approaches are mostly heuristic search algorithms that typically start from scratch to tackle a CARP problem through a trail-anderror procedure [6], [7] They could neither be parallelized nor leverage on experience to rapidly achieve a sufficiently good solution. Motivated by the fast development of deep learning techniques [8]–[11], a new potential paradigm for solving

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Methods

Conclusion