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

Min-max routing problems aim to minimize the maximum tour length among agentsas they collaboratively visit all cities, i.e., the completion time. Theseproblems include impactful real-world applications but are known as NP-hard.Existing methods are facing challenges, particularly in large-scale problemsthat require the coordination of numerous agents to cover thousands of cities.This paper proposes a new deep-learning framework to solve large-scale min-maxrouting problems. We model the simultaneous decision-making of multiple agentsas a sequential generation process, allowing the utilization of scalabledeep-learning models for sequential decision-making. In the sequentiallyapproximated problem, we propose a scalable contextual Transformer model,Equity-Transformer, which generates sequential actions considering an equitableworkload among other agents. The effectiveness of Equity-Transformer isdemonstrated through its superior performance in two representative min-maxrouting tasks: the min-max multiple traveling salesman problem (min-max mTSP)and the min-max multiple pick-up and delivery problem (min-max mPDP). Notably,our method achieves significant reductions of runtime, approximately 335 times,and cost values of about 53% compared to a competitive heuristic (LKH3) in thecase of 100 vehicles with 1,000 cities of mTSP. We provide reproducible sourcecode: https://github.com/kaist-silab/equity-transformer

Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context