Vehicle Routing Problem with Spreadsheet

Reduce computation time in the vehicle routing problem with time windows by selecting a justified, smaller big M value based on the time horizon, time matrix, and service times.

The lecture defines waiting time as the vehicle's idle time at a customer (wjk) and shows how minimizing total elapsed time—travel plus waiting—outperforms distance-only approaches while revealing trade-offs.

Minimize the number of vehicles in a vehicle routing problem by increasing vehicle capacity, revising constraints and adopting goal programming to obtain feasible solutions and reduce distance.

Explain the traveling salesman problem and how vrp transforms into tsp by using a vehicle with large capacity and wide time windows, noting subtour elimination and time-window constraints.

Explore split delivery models in the vehicle routing problem, addressing integrality constraints with split delivery variables, fixed charge constraints, and big M to satisfy customer demand within limited capacity.

Conclude this course on vehicle routing problem by outlining VRP variants, objective functions, and modeling approaches using spreadsheets or languages like Gurobi and Python to obtain efficient optimum solutions.

Speed up a VRP time window model by switching to the Gurobi linear solver, dramatically reducing solve time. Leverage Gurobi as the fastest linear and integer programming solver.