Uncertainty Management in Simulation-Optimization of Complex Systems Algorithms and Applications /

Algorithm Analysis and Problem Complexity.

; deterministic, dynamic, stochastic, robust and combinatorial optimization models; solution algorithms, learning

algorithms as applied to planning and scheduling problems. While it is not possible to give a comprehensive

scheduling policies. To obtain optimal and near optimal scheduling policies new models and algorithms are

computational intelligence algorithms. Many solved example problems are included that range from simple to quite

develops the theory of optimal search for a moving target, providing algorithms for computing optimal plans

features free C programs to implement the major algorithms covered, including the two-phase simplex method

algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along

Introduction -- Classical Techniques -- Integer Programming -- Genetic Algorithms -- Scatter Search

algorithms into business processes and create automated insight-driven decisions within the firm. Numerous

analytical character of an optimization problem and the behavior of algorithms used to solve the problem.  As