Data and Knowledge Driven Optimization Model Generation for Flow Based Optimization Problems

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Optimization can provide exceptional value, but requires significant time and skills to implement, thereby significantly limiting its widespread use. In this talk, we will show how optimization models for flow-based problems can be automatically generated from a combination of data and easily specifiable knowledge. We will describe the underlying technology and present a brief demonstration. In addition, we will discuss several approaches for overcoming the inherent inaccuracies existing in automatically generated models, so as to ensure the quality of the provided optimization solutions.