Logistics route optimization
Modeled complex route assignment across trucks, electric vehicles, and drones.
Outcomes
4 Scenarios analyzed
trucks, EVs, drones Fleet types
routes plus costs Output
The problem
Route planning gets hard when the fleet is mixed, demand varies by product, and distribution centers have their own constraints. A simple path is not the real answer if it ignores capacity, recharge cost, maintenance, or supply limits.
Scenario matrix
| Scenario | Constraint focus | Output |
|---|---|---|
| Base routing | Standard delivery constraints | Initial optimized routes |
| Cost evaluation | Loading, distance, recharge, and maintenance costs | Cost breakdowns |
| Supply management | Distribution-center capacity | Feasible allocation and routing |
| Multi-product demand | Heterogeneous product demand | More realistic route assignment |
Approach
I treated the project as an operations model, not just a notebook. The useful part is the structure around the solver: which constraints matter, which scenarios change the result, and how the output is explained visually.
- Explicit constraints. Capacity, vehicle type, costs, supply, and demand are represented as first-class pieces of the model.
- Scenario comparison. Each scenario isolates one layer of operational complexity so tradeoffs can be inspected.
- Visual outputs. Generated route maps and cost tables make the result reviewable, not just computable.
Outcome
The project shows the bridge between algorithmic modeling and business operations: a model is valuable when it makes the next decision clearer.
Repository
The public project lives at LogisticsRouteOptimization.