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Problem Statement: We are given an optimisation problem; with production centres, source airport, destination airports, transfer points and finally delivered to the customers. This is better explained in the following picture. Problem Statement

Objective function 1: Minimise costs = inventory costs + transportation costs + penalty costs + loading/unloading costs

  1. Inventory costs = inventory cost at source airport + inventory costs at distribution centres

  2. Transportation costs = cost of transporting cargo from production centre to source airport (via trucks) + cost of transporting cargo through itineraries (via flight) + cost of transporting cargo from distribution centre to transfer points (via trucks) + cost of transporting cargo from transfer point to customers (via drones)

  3. Penalty costs = cost of operating flight routes and delay penalty costs

  4. Loading/unloading costs = cost of loading cargo on trucks at production centres + cost of unloading cargo from trucks at the transfer point

Mathematical Solution (Using IBM CPLEX solver / Docplex): The complete python code (.ipynb file) with the formulation is present in this Google Drive Link. This gives an optimal solution.

Query: Is there any non-mathematical, non-formulation based method to solve this problem statement? Something on the lines of Reinforcement Learning? If any implementation is also provided, it will be icing on the cake.

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