It’s easy to calculate with money!
As soon as CO2 emissions start to cost money, integrating them into the optimization is child’s play: Either the fuel price will increase or you add the CO-2 costs to the fuel consumption of the scooters and increase the kilometer costs accordingly. Such adjustments can be made without having to adjust anything else, the model does not have to be changed in this case.
Substituting one (or more) scooters with cargo bikes does indeed have an effect on the model. The rapid prototype from part 1 has so far only reduced the length of the routes and in doing so lowered the actual fuel costs have only been reduced indirectly. However, scooters and cargo bikes have different costs per kilometer. Yet even these changes require little effort because no change has been made to the planning, we’ve just added new data to it.
The model adjusts itself
Instead of developing a model directly, first ask yourself the following questions:
- Does the problem change fundamentally or is it simply a variation which the model has already responded to?
- Which data has changed?
- Which new data do we need?
It is often the case that new questions can already be answered. This means that the model normally does not need to be developed anew as it is a tool. It does not assume the task of stipulating the daily tours but it does help to find the most favorable ones. However, it does not decide what is defined as favorable – this is what the planner does; in this case using the kilometer price.
So, we are still looking for a cost-optimal tour composition. The data has changed, though, because there is no longer a flat-rate kilometer price; insteady there are two prices: one for the scooters and one for the energy consumption of the cargo bike.
But is that really all? Basically, yes, but this change to the initial situation has a couple of cross-influences because now we also have vehicle classes to consider.
This results in a couple of adjustments to the specific formulation. These slight changes are customary for this kind of data adjustment.