Discrete flow networks: bottleneck analysis and fluid approximations
Mathematics of Operations Research
Stochastic discrete optimization
SIAM Journal on Control and Optimization
Sample-path optimization of convex stochastic performance functions
Mathematical Programming: Series A and B
Asymptotically optimal algorithms for job shop scheduling and packet routing
Journal of Algorithms
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
A multistage stochastic programming algorithm suitable for parallel computing
Parallel Computing - Special issue: Parallel computing in numerical optimization
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Generating Cutting Planes for Mixed Integer Programming Problems in a Parallel Computing Environment
INFORMS Journal on Computing
Flexible experimentation and analysis for hybrid DEVS and MPC models
Proceedings of the 38th conference on Winter simulation
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We design a generic framework to integrate distributed simulation and optimization models. Many problems require the integration of these two types of models. For example, stochastic programming can use simulation models as a scenario generator for optimization models; in some other cases, simulation models need optimization models to help determine system parameters. The framework is shown to be able to provide various services to help the integration of simulation and optimization models. We illustrate our implementation with a product-mix example. The example integrates a discrete event simulation of a product-mix problem with a linear programming (optimization) model of such a system. The simulation updates the parameters in the optimization model, which as a result will generate a new production plan.