Benchmark & Case study

Algorithm for Routing Optimisation

Challenge





initial situation

Problems


  • Slow and manual routing process
  • No visibility of the impact of the different variables


Root causes


  • Available tools are heavy and perform poorly
  • Solutions dependent on one resource and his knowledge
  • Little flexibility to improve solutions through adding/removing stops and drivers
  • Difficulty performing sensitivity analysis to study the impact of each restriction

Solutions


final situation



  • Creation of a mathematical model and algorithm to define the initial solution
  • Development of a genetic algorithm (based on meta heuristics) to optimise routes
  • Solutions displayed in a visual format to ease understanding
  • Set-up of a user-friendly interface to guarantee successful deployment of the tool

Results

Gains

Process Lead Time

Process Lead Time decreased by 90%.

Gains 2

Costs : FTE + km

Costs reduced by 10%.

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