Simulation–optimization is often used in enterprise decision-making processes, both operational and tactical. This paper proposes a data-driven, declarative approach to enterprise optimization. In our generic simulation–optimization approach, a declarative constraint satisfaction problem (CSP) is automatically customized given a simulation model and a problem instance. We construct the declarative model by training a neural network on the simulation model and embedding the trained network in the CSP. The approach allows the embedding of a wide variety of complex problems, is able to handle multi-objective problems, is flexible to changing multiple objectives simultaneously, and allows the modeler to focus on what problem needs to be solved by a computer instead of how the computer should solve it. We demonstrate the value of this neuro-symbolic approach in experiments on two problem domains. Furthermore, we show how low discrepancy sampling, weighted composite objectives, and linear activation functions can improve the performance of neural embeddings in CSP.
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