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

SNCF, the French public train company, is experimenting to develop new typesof transportation services by tackling vehicle routing problems. While manydeep learning models have been used to tackle efficiently vehicle routingproblems, it is difficult to take into account time related constraints. Inthis paper, we solve the Capacitated Vehicle Routing Problem with Time Windows(CVRPTW) and the Capacitated Pickup and Delivery Problem with Time Windows(CPDPTW) with a constructive iterative Deep Learning algorithm. We use anAttention Encoder-Decoder structure and design a novel insertion heuristic forthe feasibility check of the CPDPTW. Our models yields results that are betterthan best known learning solutions on the CVRPTW. We show the feasibility ofdeep learning techniques for solving the CPDPTW but witness the limitations ofour iterative approach in terms of computational complexity.

A deep learning Attention model to solve the Vehicle Routing Problem and the Pick-up and Delivery Problem with Time Windows