It’s not a simple job! Unless you have an unlimited fleet of
trucks that don’t use fuel, and a group of drivers willing to make ridiculous
trips at no cost, you will be motivated to design optimal routes. Added to
these obvious economic reasons, you should also be considering pollution and traffic
congestion!!
OK, don’t panic... There are several ways of tackling this
problem, and one of them is the Wright’s Saving Algorithm, a deterministic
method that groups clients in order to reduce the total distance travelled by
trucks.
The procedure is very simple, and results are suprising! It consists of pairing all orders, and calculating the kms saved by grouping these two suppliers. This image shows how in this case, grouping suppliers 1 and 2 would generate a saving: S=d13+d02-d12.
The procedure is very simple, and results are suprising! It consists of pairing all orders, and calculating the kms saved by grouping these two suppliers. This image shows how in this case, grouping suppliers 1 and 2 would generate a saving: S=d13+d02-d12.
The negative term in the saving comes because pairing suppliers adds a new route: the one connecting both suppliers. If this distance is greater than the positive terms, then pairing these suppliers will have a "negative saving", indicating that its best to visit both separately (if we consider distance as the only cost indicator).
The algorithm then analyzes all combinations, ordered by greater saving, and assigns orders to the available trucks, considering capacity and fuel restrictions.
You can try the model we developed here: http://www.runthemodel.com/models/825/
In the example, you have to locate the suppliers in the green area (by clicking and dragging) and click the START button. You will see how new orders that arrive each day are assigned to the different trucks. Try pausing the model when orders arrive and thinking how you would assign the demand to the available trucks. Then compare your ideas with the algorithm! With few suppliers you will probably be able to match the results (or even beat the algorithm!), but try adding suppliers, and the problem will become exponentially more complex!!
The algorithm then analyzes all combinations, ordered by greater saving, and assigns orders to the available trucks, considering capacity and fuel restrictions.
You can try the model we developed here: http://www.runthemodel.com/models/825/
In the example, you have to locate the suppliers in the green area (by clicking and dragging) and click the START button. You will see how new orders that arrive each day are assigned to the different trucks. Try pausing the model when orders arrive and thinking how you would assign the demand to the available trucks. Then compare your ideas with the algorithm! With few suppliers you will probably be able to match the results (or even beat the algorithm!), but try adding suppliers, and the problem will become exponentially more complex!!
Milk Runs tour re-make
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 | Developed with simulation software AnyLogic |
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