The Role of Software in Optimization and Operations Research

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OPTIMIZATION AND OPERATIONS RESEARCH – The Role of Software in Optimization and Operations Research - Harvey J. Greenberg Figure 13: IMPS Components More visualization has been a key to modern software systems, and modelers have come to expect graphic user interfaces (GUI). Algorithm animation has not fully penetrated optimization software, but this is also coming. As visualization increases in importance, we can expect to see more on the web. Websim is the current term that refers to web-based simulation, and it is one of the current research areas. There are Java Applets presently on the web to allow people to model and solve optimization problems. These are small, designed for pedagogical purposes, but we can expect this to develop further into remote capabilities for production. Acknowledgement The author is grateful to Professor, Dr. U. Derigs, for the opportunity to contribute this chapter to the EOLSS and for his patience while I learned the formatting requirements. ©Encyclopedia of Life Support Systems (EOLSS)
OPTIMIZATION AND OPERATIONS RESEARCH – The Role of Software in Optimization and Operations Research - Harvey J. Greenberg Glossary Algorithm: A sequence of precisely specified computations to reach a solution. Algorithm Parameters that affect tactics within an algorithm class, such as Controls: whether to conduct some test. Approximation For an NP-hard problem, this is a procedure that takes a Algorithm: polynomial amount of time to obtain a solution that is guaranteed to be within some percent of optimal. CLP: Constrained Logic Programming. Data structure: How we store information in a computer. GUI: Graphic user interface. IMPS: Intelligent Mathematical Programming System. Infeasibility: An instance of a mathematical program for which there is no feasible solution. Load Balancing: The goal of having each of many processors perform the same amount of computation. Message Passing: The transfer of information from one processor to another in a parallel computing environment. Metaheuristics: A general framework for searching a space to solve hard problems (meta is a prefix, as in a meta-language being a language to describe languages). Model Assistant: A component of an IMPS designed to assist model formulation. MPS: Mathematical Programming System. NP-Hard: A problem class for, which there is no known algorithm that can solve any member in polynomial time. Speedup: The fraction of how close to perfect distribution of computation an algorithm implementation comes. Bibliography Ashford R.W. and Daniel R.C. (1986). LP-MODEL: XPRESS-LP's model builder. IMA Journal of Mathematics in Management 1(163), 176. [Downloads are available at ] Bentley P.J. ed. (1992, 1999). Evolutionary Design by Computers, 446 pp. San Francisco, CA: Morgan Kaufman. [This is a basic reference for evolutionary algorithms/programming.] Bertsekas D.P. and Tsitsiklis J.N. (1989). Parallel and Distributed Computation, 715 pp. Englewood Cliffs, NJ: Prentice-Hall. [This presents the fundamentals of parallel algorithms, with special attention to optimization.] Chandru V. and Hooker J.N. (1953, 1999). Optimization Methods for Logical Inference, 365 pp. New York: John Wiley & Sons. [This organizes the developments in applying integer programming to the sort of logical inference done in artificial intelligence. Emphasis is given to the authors' work during the past decade.] Chinneck J.W. (1997). Computer codes for the analysis of infeasible linear programs. Journal of Operational Research Society 47(1), 61-72. [This describes the author's development of computing IISs in various optimization programs.] Chinneck J.W. (1997). Feasibility and viability. Advances in Sensitivity Analysis and Parametric Programming, (ed. T. Gal and Greenberg H.J.), pp. 14-1-14-41, Boston, MA: Kluwer Academic Publishers. [This surveys the state-of-the-art in practical methods for helping an analyst understand why a ©Encyclopedia of Life Support Systems (EOLSS)
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