Vehicle Routing Problem with Spreadsheet

Model the vehicle routing problem with time windows in a spreadsheet using a three-index formulation in OpenSolver. Explore time-window constraints and big M, and compare against two-index formulations.

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.

Explore service duration in the vehicle routing problem with time windows by incorporating a 0.15 service time in the Excel model and adjusting time constraints.

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.

Extend the VRP with time windows to a heterogeneous fleet by incorporating different vehicle capacities and adjusting the capacity constraints to obtain a feasible solution.

Explore the vehicle routing problem with multiple time windows using disjunctive 'either-or' constraints, binary variables, and big M modeling to schedule service within one of several intervals.

Explore capacitated VRP by transforming time window VRP into CVRP, showing how time windows become nonbinding, and achieving the same 545-kilometer optimum as the two-index formulation, noting time limits.

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.

Master the multi-depot vehicle routing problem with time windows using a spreadsheet model, adding a second depot and distance and time matrices, enforcing departure and return to the same depot.

Examine multi-depot vehicle routing with open depots by removing the constraint to return to the same depot, reducing distance and enabling cross-depot trips.

Explore the simultaneous delivery and pickup vrp, using a spreadsheet formulation with yij and zij and capacity constraints. Compare solutions with and without time windows and note distance changes.

Explore simultaneous split delivery and pickup models with and without time windows, using yij and zij to handle pickups and deliveries under capacity constraints.

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.