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

Heavy goods vehicles are vital backbones of the supply chain delivery systembut also contribute significantly to carbon emissions with only 60% loadingefficiency in the United Kingdom. Collaborative vehicle routing has beenproposed as a solution to increase efficiency, but challenges remain to makethis a possibility. One key challenge is the efficient computation of viablesolutions for co-loading and routing. Current operations research methodssuffer from non-linear scaling with increasing problem size and are thereforebound to limited geographic areas to compute results in time for day-to-dayoperations. This only allows for local optima in routing and leaves globaloptimisation potential untouched. We develop a reinforcement learning model tosolve the three-dimensional loading capacitated vehicle routing problem inapproximately linear time. While this problem has been studied extensively inoperations research, no publications on solving it with reinforcement learningexist. We demonstrate the favourable scaling of our reinforcement learningmodel and benchmark our routing performance against state-of-the-art methods.The model performs within an average gap of 3.83% to 8.10% compared toestablished methods. Our model not only represents a promising first steptowards large-scale logistics optimisation with reinforcement learning but alsolays the foundation for this research stream.

Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem