Route Optimization for energy efficiency in transport

  • Current logistics and supply chain practices are not sustainable in the long term and cause environmental imbalance by emitting large quantities of CO2.
  • Statistical Route Optimization techniques generally do not consider energy efficiency.
  • Real life constraints are difficult to implement in current vehicle routing problem algorithms.

Zasti uses artificial neural networks to minimize route optimization problem algorithms using GPS data, GIS data, historical data, data about vehicles (type, weight), data about freight (type, number of articles, weight of articles), duration of time windows between supply chains. Constraints such as traffic conditions, number of vehicles, work shift duration, variation in production and supply, supply of freight, etc., would be taken into consideration while developing AI models. The models develop a more realistic cost function and predict routes which reduce – energy consumption. The Zasti model generates the route with the best fuel economy.

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