addresses linear programming in general with an emphasis on the development, presentation, and illustration (with examples) of the fundamentals necessary to model, solve, and analyze linear programs. covers recent results with regard to alternative methods to the simplex algorithm e.g., the affine scaling variants of the Karmarkar algorithm. deals with the use of linear programming in information technology e.g., as a means to analyze large amounts of data. explores prediction/ forecasting, pattern classification/pattern recognition, clustering analysis, input-output analysis, and the design and training of neural networks all achieved by means of linear programming explores the important but often neglected area of heuristic programming and extends it to such currently popular heuristic methods as genetic algorithms, simulated annealing, and various related techniques that are often associated with the field of artificial intelligence.

addresses the topic of multiple objective optimization using an original, unified approach to both modeling and solution the multiplex concept. considers various multiobjective philosophies, their models, and their solution and analysis via a single algorithm discusses and demonstrates how such problems may be solved via conventional linear programming algorithms and software.