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Allocation Optimizer models - Managing oversupply situations
Applies to: Allocation Optimizer all versions
Date: September 15, 2006
Allocation Optimizer (AO) models link the supply of forest products to their end destinations, and captures the cost to deliver said products to said destinations. Balancing forest supply with destination demands can be complicated, especially if mangement options are relatively rigid and destinations’ processing capacities are fixed.
Oversupply situations can result in infeasible solutions, and depending the combinations of forest origins, products and end destinations, it can be tricky to determine the source of the problem. The purpose of this tech tip is to describe a simple addition that you can make to AO models to avoid infeasibilites due to oversupply in highly constrained models.
Oversupply situtations typically arise when the supply model is constrained to produce some minimum amount of product — either as a formal constraint in the Optimize section, or via an Lpschedule — and there is insufficient capacity at destinations to process the full compliment of supply.
One tactic you can employ to counteract this is to create a destination that accepts all products that has no capacity (processing) constraints. This way, the supply model has a so-called pressure relief valve in the event that the capacities of the ‘real’ destinations are not sufficient. To make this work sensibly, you will need to apply a high cost to deliver product to this ‘phantom’ destination, so that the model will only send product to it in an oversupply situation.
Another benefit of using a phantom destination is that you can report the products that are shipped to it. This way you can determine exactly which products are problematic, and the magnitude and timing of the problems.
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Stora Enso Predicts a 2.5% Savings
“We asked, ‘If we had made the decision and optimized the problems, how much money would we have saved?’” The answer was approximately 2.5 percent.”
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