Modeling production planning of a shop floor is essential in improving operational efficiency of the supply chain and thus significantly reducing costs. The uncertainties in processing times are highly undesirable for production managers. Classical linear programming models and various hybrid models have been unsuccessful to accurately represent both stochasticity in processing levels and precedence constraints. This book proposes a modified hybrid discrete event simulation-optimization model that uses chance constraints to deal with stochasticity. A comprehensive analysis has been done on the model for various levels of uncertainties and variations in input parameters. Computational studies have also been performed to test the effects of dispatching sequences and backlogging. The analysis would help in better production planning and scheduling in shop floor and should be especially useful to professionals in Manufacturing and Industrial Engineering, and to anyone else who may be researching in production planning and control.

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