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

, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its

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

optimization -- Multi-criteria portfolio optimization using support vector machines and genetic algorithms.

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

covered include algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization

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

to the use of algorithms for ensuring the confidentiality of data, information splitting techniques

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

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

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