Classification Approaches to Identify Intermittent Demand Series -- Consistent Forecast-Based Inventory Model … probability distribution by assuming an integer-valued autoregressive process as demand process. The developed algorithms can be … used to identify, estimate, and predict the demand as well as optimize the inventory decision of intermittent demand series …

to construct relevant input variables, such as latent demand and customer price sensitivity. He proves that the …

theory is applied to integrate long-run decisions and different types of demand into the analysis. Aside from its … reinvestment in production, financing and management compensation, in markets with fluctuating demand. The method of supergame …

Introduction -- Part I: Service Demand of Consumers -- Part II: Cost Analyses for Congestion-Prone Service Systems …-making theorems for consumers and suppliers is developed. The resulting demand and cost functions incorporate service time as the …

-of-chapter exercises. In addition to the general statistical methods, the book also includes Monte Carlo simulation and optimization. The … Statistical Analysis of Data -- Modeling and Simulation: Part I -- Modeling and Simulation: Part II -- Solver, Scenarios, and Goal …

This book contains the refereed proceedings of the International Conference on Modeling and Simulation in Engineering … University Jaume I. This edition of the conference paid special attention to modeling and simulation in diverse fields of … topical sections on modeling and simulation in finance and accounting; modeling and simulation in business management and …

research advances in theory and methodology. Leading contributors cover such topics as discrete optimization via simulation …The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization …, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent …

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving … area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques … coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous …