I am implementing vrp solve in a company that distributes food in vehicles of various capacities 2ton, 3.5ton, 5 ton , 10 ton etc.
I need to know the options that the software offers to increase the occupation of the capacity of vehicles (routes), I have read the VRP Solve bibliography, but I couldn´t find a variable that controls this parameter.
The options that I already worked are:
None of the above gave a positive result.
The software respects time windows, optimizes resources but underutilizes load capacity. An example of this, a vehicle with a capacity of 10 tons dispatches it with deliveries equivalent to 3 tons, a vehicle with a capacity of 3.5 tons dispatches it with 2 tons, etc., for the company that distributes mass consumption products. This is not efficient since the cost of transport per ton rises, even though it respects time windows and other restrictions.
Example: The table on right side represents the results of the vrp, while the table on left side represents the expected results. The goal is to increase the average of %UseCapacity, in this test the costs were zero for all routes.
I would appreciate to your suggestions or study material where there are options to solve this requirement.
For the different routes that you have in the problem are there other constraints that are different between them? Starting/Ending Depot, max total time/travel time/distance, max order count, etc?
Also do you have breaks set up for the routes?
Thanks for answering.
Yes, all routed vehicles have the same restrictions:
Break time window 9 am-9:30am, start and end in the same deposit, maximum total time of 720minutes, max travel time does not have, is null, maximum distance is null, maximum number of orders 100 orders, but it is a very large that does not affect the choice to improve the occupancy capacity of vehicles.
I understand that the model may be restricted to these restrictions but I did the test without them and the result is similar.
My conclusion is that because the fleet is of different capacities and there is a big difference between each of them, it is not possible to control the fleet capacity utilization percentage.
Maybe in another company they have had this problem and they have been able to solve it, I would like to know how they solved it or some study material related to the occupation of capacities.
I would appreciate to your suggestions
After the routes have been created the solver has a post processing routine that attempts to move them to cheaper routes where possible. However, there is a known issue for this post processing routine where it does not happen if there are breaks on the routes.
For now the work around option is to remove the breaks if the routes can be adjusted time wise because of not having time windows, reduce the max total time by the time of the break, and then add in a fixed cost that shows the difference in capacity making the smaller capacity trucks cheaper. The solver is looking to make routes cheaper so there needs to be a cost function way of knowing that smaller capacity vehicles are preferred so a higher capacity percentage is reached.
Alternatively, since you know that the routes have the same characteristics other then the capacity that you can let the solver come up with the optimized assignment of orders to a route and sequencing and then adjust which vehicle is actually assigned to it afterwards.
Hope this helps