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Statistics & Optimization/Operations research /Project 10 of 10 M.S. coursework

Cargo Network Optimization

A linear programming model that routes cargo across a multi-node network while meeting destination demand and respecting hub and focus-city capacity.

Python Operations research Linear programming PuLP Optimization M.S. coursework

Business impact

Compares feasible routing plans and identifies the least-cost flow under the model’s demand, capacity, and flow-balance constraints.

$200,863.75Modeled minimum cost
OptimalSolver status
95,650 tonsCVG hub capacity used

Business question

How should cargo move through a multi-node network to satisfy demand at minimum modeled shipping cost?

Approach

How the work was done

  • Represented shipment quantities across hubs, focus cities, and downstream demand centers as decision variables.
  • Minimized total shipping cost while enforcing hub capacity, focus-city processing capacity, demand satisfaction, flow balance, and non-negativity.
  • Solved the model with PuLP and checked each saved flow against the network constraints.

Results

What the analysis shows

  • The solver returned an optimal modeled cost of $200,863.75.
  • CVG was fully utilized at 95,650 tons; AFW carried 38,097 of 44,350 available tons.
  • Saved verification outputs show hub, focus-city, demand, and flow-balance constraints were satisfied.

From the project

Capacity use and shipment routing

Solved cargo plan showing CVG using 95,650 of 95,650 tons, AFW using 38,097 of 44,350 tons, and 87,502 direct versus 46,245 transfer tons.

The solved shipping plan

The plan serves 133,747 tons across 65 demand centers. CVG is fully used and AFW has 6,253 tons available. The notebook separately verifies hub limits, transfer flow balance and every destination requirement.

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Business relevance

What this makes easier to decide

The model gives planners a costed feasible routing baseline for comparing network decisions. The total is the optimization result under the stated model inputs and constraints.