Using VUKU for logistics

Managing resources,
maximising utilisation

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Using VUKU for logistics

Managing resources,
maximising utilisation

Using VUKU for logistics

Logistics industry critically depends on how it can optimally allocate its resources to address dynamically changing demand. Customers are continually looking for ways to maximise resource utilisation to grow their margins. Many companies in this industry have been using traditional operations research based approaches that tend to fall short in optimising due to the interconnected nature of resource allocations and uncertainties in demand.

VUKU uses its advanced probabilistic modelling techniques to learn from history and create multiple models of possible “tomorrows,” each with different demand for resources such as, pick-up & delivery trucks or taxis or shared rides, as the case may be. These models are then used by multiple Reinforcement Learning (RL) agents, each with a different policy to optimally allocate the resources to the demand. Multi-Agent System components of the platform make RL agents collaborate with each other, to maximise the returns across all strategies for the entire operation.

Probabilistic Modelling Icon
  • Probabilistic models predict the demand for tomorrow
  • A series of possible tomorrows are simulated (“Monte Carlo” samples)
Reinforcement Learning Icon
Multi-Agent System Icon
  • For each possible “tomorrow”, we solve the resource allocation problem in simulation
  • See the effects of decisions (e.g. number of trucks)

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