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Optimization
• The truck exchanges the beverage tank for a new one of the same size. There is a \$1 per drink beverage charge plus a delivery charge of \$1,000 per tank.
• So that the beverage company will know when to deliver tanks and what size tank you will use for the day, arrangements regarding tank size and delivery frequency must be made at the beginning of the day.
• People purchase drinks according to a random distribution; the cost per drink is \$2.50.
• Ifthebeveragetankbecomesempty,youloseanestimated\$100perminuteinsalesbecause customers already in line go to another stand and new customers are discouraged from get- ting in line.
• Ifyouexchangethetanktoooften,youlosemoneybecausethebeverageinsidetheoldtank gets taken away along with the tank.
• This model ignores other expenses, such as labor.
• Themodelrunsforasimulationtimeperiodof480minutes(8hours).
The goal of this tutorial is to optimize both how big of a tank you should order and how often the tanks are exchanged. It is a simple continuous model, but a good example to show some of the optimization techniques you will use in any type of large model.
☞ For comparison purposes, the final model for this example, “Optimize 2”, is located in the \Examples\How To\Optimization folder.
Open the Optimize 1 model from the \Examples\How To\Optimization folder.
To provide a starting point for the optimization, this model has been populated with initial assumptions for the decision variables: deliveries are repeated every 240 minutes and the tank size is 1,000 drinks.
So that the example file isn’t overwritten, give the command File > Save Model As and save the model as “MyOptimizer”.
How To

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