The Vehicle Route Optimization (VRO) solver is used to determine optimal delivery routes for vehicles. Its primary objective is to minimize travel time and the number of transportation modes used, enabling the consolidation of deliveries. This approach reduces fuel expenses, driver costs, and CO2 emissions.

To invoke the VRO solver, create a new scenario from a base scenario:

- Enter a name for the Output Scenario

- Choose the desired Base Scenario

- Select the Vehicle Routing Optimization solver from the Solver section

Once the settings appear in the right pane, you can select parameters as desired:

If selected, solver will look at solutions for any similar optimizations that have been solved to use as a starting point. Giving an IFS can help complicated optimizations converge faster. The better the starting point, the faster a solution will converge.

- The solver automatically sends an IFS by searching for solved scenarios with similar scenario names.

- If chosen, IFS is not a valid solution to the running scenario, the solver will proceed as if no IFS was found.

- This option will not cause harm if selected, it will just speed up the process if previous scenarios can help it.

Vehicle Route model cost-minimization optimizations are solved by continually finding lower cost feasible solutions (values for all variable that satisfy all constraints) and comparing the cost of that solution against the highest cost best bound (no actual solution can be lower than the best bound). The percent difference between the lowest feasible solution and the highest best bound is called the optimality gap. In other words, the gap (percentage) is the difference between a perfect solution (lowest cost solution that satisfies all constraints) and a solution that may fit best with your company or project. We use the gap to acknowledge that achieving the perfect solution may be unsustainable or very hard to attain.

Instructions: When the optimality gap of an active solve reaches this value, the optimization will terminate and return the current best solution.

- The value given should be entered as a float, which will then convert to a percentage. i.e. 1 = 1%, .01 = .01%.

- A value of 0 defaults to .01%, the lowest possible value

The maximum time (in minutes) that the solve will run for before returning the best solution found up to that point.

Definition: Big M constraints are solver generated constraints based on user created fixed costs and count constraints which use a constant to enforce relationships. If this value is too low, problems are infeasible. If this value is too high, solve will be infeasible and user will see the error message “Best bound is infinite”.

Percentage to add to native Supply Chain Architect scaling. 25% to 500% would be the normal range to try if experiencing Big M related issues.

Supply Chain Architect dynamically scales Big M based on problem characteristics of each model to be solved. However, in cases that are extremely complicated i.e. models with chains of multiple BOM’s or resources. Then, it may be necessary to scale.