Abstract
In many nonconvex programming problems, it is possible to locate local optima, but the global optimum may be difficult to determine. In such cases, a search procedure is often used, with random starting solutions, to find alternate local optima. This search can be terminated by a stopping rule, based upon Bayesian revised probability distributions, which determines the optimal number of iterations. The application of this rule to a resource allocation problem in project scheduling is illustrated.
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References
Reiter, S., andSherman, G.,Discrete Optimizing, SIAM Journal on Applied Mathematics, Vol. 13, pp. 864–889, 1965.
Breiman, L.,Stopping Rule Problems, Applied Combinatorial Mathematics, Edited by E. Bechenbach, John Wiley and Sons, New York, New York, 1964.
Randolph, P. H., Swinson, G., and Ellingsen, C.,Stopping Rules for Sequencing Problems, Operations Research, Vol. 21, pp. 1309–1315, 1973.
Raiffa, H., andSchlaifer, R.,Applied Statistical Decision Theory, Harvard University, Boston, Massachusetts, 1961.
Reinschmidt, K. F., andFrank, W. E.,Construction Cash Flow Management System, Journal of the Construction Division, ASCE, Vol. 102, pp. 615–627, 1976.
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Communicated by G. Leitmann
This work was supported in part by grants from the National Science Foundation and the Rochester Gas and Electric Corporation to the Massachusetts Institute of Technology.
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Reinschmidt, K.F. A stopping rule for optimization using randomized search procedures. J Optim Theory Appl 23, 119–124 (1977). https://doi.org/10.1007/BF00932302
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DOI: https://doi.org/10.1007/BF00932302