English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Recently, Deep reinforcement learning (DRL) models have shown promisingresults in solving routing problems. However, most DRL solvers are commonlyproposed to solve node routing problems, such as the Traveling Salesman Problem(TSP). Meanwhile, there has been limited research on applying neural methods toarc routing problems, such as the Chinese Postman Problem (CPP), since theyoften feature irregular and complex solution spaces compared to TSP. To fillthese gaps, this paper proposes a novel DRL framework to address the CPP withload-dependent costs (CPP-LC) (Corberan et al., 2018), which is a complex arcrouting problem with load constraints. The novelty of our method is two-fold.First, we formulate the CPP-LC as a Markov Decision Process (MDP) sequentialmodel. Subsequently, we introduce an autoregressive model based on DRL, namelyArc-DRL, consisting of an encoder and decoder to address the CPP-LC challengeeffectively. Such a framework allows the DRL model to work efficiently andscalably to arc routing problems. Furthermore, we propose a new bio-inspiredmeta-heuristic solution based on Evolutionary Algorithm (EA) for CPP-LC.Extensive experiments show that Arc-DRL outperforms existing meta-heuristicmethods such as Iterative Local Search (ILS) and Variable Neighborhood Search(VNS) proposed by (Corberan et al., 2018) on large benchmark datasets forCPP-LC regarding both solution quality and running time; while the EA gives thebest solution quality with much more running time. We release our C++implementations for metaheuristics such as EA, ILS and VNS along with the codefor data generation and our generated data athttps://github.com/HySonLab/Chinese_Postman_Problem

Graph Attention-based Deep Reinforcement Learning for solving the Chinese Postman Problem with Load-dependent costs