A Dynamic and Learning-Based Vehicle Routing Solver

Implementation Time:
24 months
Solution Provider: AI Singapore

VersaFleet is a transport management Software-as-a-Service (TMS SaaS) that automates logistics operations with route optimisation, electronic Proof-Of-Delivery, instant notifications and real-time job status tracking. VersaFleet powers thousands of drivers worldwide, automating operations one successful delivery at a time for various household FMCG brands.

VersaFleet’s existing solvers, as with most deterministic models, do not learn Trom real-time events or improve Its solution outputs over time.

  • Its classical algorithms employ meta-heuristic approaches that produce near-optimal solutions nonetheless Indeed. these problems are known to b e very difficult to solve (np-hard).

However, models of such problems are an approximation of realitv.

  • Solution outputs are sometimes manually adjusted by operators
  • Frequent unpredicted events e.g.. drivers taking urgent leave, road accidents, sudden vehicle unavailability, and delivery cancellations cause disturbances and inefficiencies batch-planned routes

How can VersaFleet use Al to tackle issues such as operators’ constant manual adjustments, and to better handle dynamic routing?

New solvers were developed to handle a broader range of different VRP types and which can produce feasible solutions within reasonable compute times. These could also be deployed within a SaaS web architecture and produce results that were well accepted by end-users.

Outcomes:

  • The technology deployed introduced an improved service into market, leading to both product and process improvements.
  • The R&D effort supported subsequent 3rd-party financing, and higher revenues, creating and sustaining new job roles in tech
  • Total value-added arising from the project exceeded S$1M

 

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Implementation Time

24 months

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