The experiments and different scenarios are conducted with the same distances and same average truck
velocity of 55 miles per hour. All distance units are in miles and time units are in hours. In the first
scenario, we assumed a constant rate of 4 orders per hour placed through each of the three warehouses.
This excessively high order rate was chosen deliberately in order to test our system in the worst case
scenario. We present in Table 1 the order size and the orders’ due dates placed by each customer that are
generated according to triangular distributions (i.e., TRI (minimum, most likely, maximum)). We used
triangular distributions for the order sizes and due dates since there is only limited sample data concerning
those parameters. The values of the distributions (minimum, most likely, maximum), were chosen based on a knowledge of the minimum and maximum possible values, or simply by “inspired guess” [1].
The experiments and different scenarios are conducted with the same distances and same average truckvelocity of 55 miles per hour. All distance units are in miles and time units are in hours. In the firstscenario, we assumed a constant rate of 4 orders per hour placed through each of the three warehouses.This excessively high order rate was chosen deliberately in order to test our system in the worst casescenario. We present in Table 1 the order size and the orders’ due dates placed by each customer that aregenerated according to triangular distributions (i.e., TRI (minimum, most likely, maximum)). We usedtriangular distributions for the order sizes and due dates since there is only limited sample data concerningthose parameters. The values of the distributions (minimum, most likely, maximum), were chosen based on a knowledge of the minimum and maximum possible values, or simply by “inspired guess” [1].
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