Applying Decomposition Methods to Solve a Stochastic Available-To-Promise Problem




Pangarad, Arm

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The available-to-promise (ATP) model is a mechanism that provides recommendations about when to accept customer orders that takes into account both product availability information, current customer orders and future orders in order to maximize overall profits. It becomes an important tool in a decision making process for manufacturing businesses. In this thesis, we present the stochastic available-to-promise problem which addresses the problem of needing to accept-or-reject in real-time orders for customizable computer configurations where the manufacturer cannot predict when the most profitable customers might arrive, but does have some probabilistic information about the likelihood of order arrivals and their requirements. Because the problem is stochastic, modeling all possible future scenarios results in an exponentially large problem. Even when one limits the total number of scenarios considered, solving the problem by off-the-shelf commercial solvers such as CPLEX results in computation times that are too large to be usable. We study the underlying structure of the model and propose decomposition methods to solve it. We test both a Dantzig-Wolfe decomposition (“Column-generation approach) and a Bender’s decomposition (a row-oriented decomposition). We compare solution times of both methods to solution times of CPLEX.



Available-to-promise, Stochastic, ATP, Decomposition method