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Abstract

We study a two‐stage resource pooling problem with multiple resources and customers. The central decision‐maker decides the capacity level of the resources within a total budget before the realization of uncertain demand. Then, the fulfillment policy is determined by individual service‐level requirements. We use a robust satisficing framework to formulate the problem and allow ambiguity in the distribution of demand. Moreover, we introduce a new utility‐based probability distance allowing the model to be solved exactly using a column and constraint generation algorithm. We also provide a more efficient solution method for some special cases. Finally, we conduct experiments for a process flexibility problem, which is an example of the resource pooling problem. We show the advantage of our model over some benchmark approaches, and show the impact of the flexibility structure and correlation between demands.

Keywords

column and constraint generation algorithm distributionally robust optimization resource pooling robust satisficing target‐based decision making

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Target‐based resource pooling problem

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