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On the use of computers in planning under conditions of uncertainty

Über die Verwendung von Computern in der Planung mit Unsicherheitsfaktoren

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Abstract

In this paper there are presented some recent results in stochastic linear programming which require for obtaining numerical solutions mainly the existing efficient programs for linear and quadratic programming. It is also shown that for stochastic linear programs with simple randomization the minimum risk solution does not depend on the probability distribution of coefficients and can be obtained by linear programming. The relevance of the results to planning under uncertainty is illustrated and numerical examples and computation experience is reported. All the methods presented can provide numerical results for problems of dimensions met in applications.

Zusammenfassung

Es werden neuere Ergebnisse der stochastischen linearen Programmierung aufgezeigt, die zur Erzielung numerischer Lösungen im wesentlichen die bestehenden wirksamen Programme für die lineare und quadratische Programmierung erfordern. Es wird ferner dargelegt, daß die Lösung mit minimalem Risiko für stochastische lineare Programme mit einfacher Umrechnung auf Zufallszahlen nicht von der Wahrscheinlichkeitsverteilung der Koeffizienten abhängig ist und mittels linearer Programmierung erreichbar ist. Die Relevanz der Ergebnisse für die Planung mit Unsicherheitsfaktoren wird erläutert; ferner werden numerische Beispiele gegeben und über die Erfahrungen in der Berechnung berichtet. Mit allen beschriebenen Methoden lassen sich numerische Ergebnisse für die in der Anwendung auftretenden Dimensionsprobleme gewinnen.

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References

  1. Bereanu, B.: Renewal processes and some stochastic programming problems in economics. SIAM J. Appl. Math.19, 2, 306–322 (1970).

    Article  Google Scholar 

  2. Sengupta, J. K., Tintner, G., Rao, V.: An application of stochastic linear programming to development planning. Metroeconomica14, 25–41 (1962).

    Google Scholar 

  3. Tintner, G.: Stochastic linear programming with applications to agricultural economics, in: Antosiewicz, H. A. (ed.): Proceedings of the Second Symposium in Linear Programming, Vol. 1, 197–228. Washington D. C.: National Bureau of Standards, USAF. 1955.

    Google Scholar 

  4. Mihoc, G.: Some clarifications concerning the applications of linear programming (Romanian). Revista de statistica12, 13–18 (1959).

    Google Scholar 

  5. Bereanu, B.: The Distribution Problem in Stochastic Linear Programming. The Cartesian Integration Method. Bucharest: Report No. 7103, Center of Mathematical Statistics, 1971.

  6. Simonnard, M.: Programmation linéaire. Paris: Dunod. 1962.

    Google Scholar 

  7. Bereanu, B.: Régions de décision et répartition de l'optimum dans la programmation linéaire. C. R. Acad. Sci. Paris259, 1383–1386 (1964).

    Google Scholar 

  8. Bereanu, B.: On stochastic linear programming III. Distribution problems, stochastic technology matrix. Z. für Wahrscheinlichkeitstheorie und verw. Gebiete8, 148–152 (1967).

    Article  Google Scholar 

  9. Bereanu, B.: A property of convex piecewise linear functions with applications to mathematical programming. Unternehmensforschung9, 112–119 (1965).

    Article  Google Scholar 

  10. Bereanu, B.: On stochastic linear programming I. Distribution problems: a single random variable. Rev. Math. Pures et appl.8, 683–697 (1963).

    Google Scholar 

  11. Mathematical Programming System Extended (MPSX). Linear and separable programming. Program description. New York: IBM SH20-0968-0, 1971.

  12. Bereanu, B., Peetres, G.: A “wait-and-see” problem in stochastic linear programming. An experimental computer code. Cahiers Centre d'Etudes de Recherche Opérationelle12, 133–148 (1970).

    Google Scholar 

  13. Bereanu, B., Peeters, G.: A “wait-and-see” problem in stochastic linear programming. An experimental computer code. Heverlee: DP 6815, Center for Oper. Res. and Econometrics, Catholic Univ. of Louvain, 1968.

  14. Bereanu, B.: The Cartesian Integration Method in stochastic linear programming, in: Collatz, L., Wetterling, W. (eds.): Numerische Methoden bei Optimierungsaufgaben, Proceedings of the Conference at Oberwolfach, November 1971, 14–22 (ISNM Vol. 17). Basel-Stuttgart: Birkhäuser 1973.

    Google Scholar 

  15. Bereanu, B.: Stable stochastic linear programs and applications. Stanford California: Technical Report 73-11, Department of Operations Research, Stanford University. November 1973.

  16. Stroud, A. H., Secrest, D.: Gaussian quadrature formulas. Englewood Cliffs, N. J.: Prentice Hall 1966.

    Google Scholar 

  17. Bereanu, B.: On the convergence of Cartesian multi-dimensional quadrature formulas. Numerische Math.10, 348–350 (1972).

    Article  Google Scholar 

  18. Charnes, A., Cooper, W. W.: Deterministic equivalents for optimizing and satisfying under chance constraints. Oper. Res.11, 18–39 (1963).

    Google Scholar 

  19. Bereanu, B.: The distribution problem in stochastic linear programming and minimum risk solutions (Romanian). Bucharest: University of Bucharest. Dissertation 1963.

    Google Scholar 

  20. Bereanu, B.: Programme de risque minimal en programmation linéaire stochastique. C. R. Acad. Sci. Paris259, 981–983 (1964).

    Google Scholar 

  21. Bereanu, B.: Distribution-free optimal solutions in stochastic linear programming. Bucharest: Report No. 7303, Center of Mathematical Statistics of the Academy of the Socialist Republic of Romania. June 1973.

  22. Bereanu, B.: Minimum risk solution in linear programming (Romanian). Analele Univ. Buc., Mathematics and Mechanics Series13, 121–140 (1964).

    Google Scholar 

  23. Geoffrion, A. M.: Stochastic programming with aspiration or fractile criteria. Management Science13, 672–679 (1967).

    Google Scholar 

  24. Kataoka, S.: Stochastic programming: Maximum probability model. Hitotsubashi J. Arts Sci.8, 51–59 (1967).

    Google Scholar 

  25. Bergthaller, C.: A quadratic equivalent for the minimum risk problem. Rev. Roum. Math. pures et appl.15, 17–23 (1970).

    Google Scholar 

  26. Dragomirescu, M.: An algorithm for the minimum risk problem of stochastic programming. Operations Res.20, 154–160 (1972).

    Google Scholar 

  27. Markowitz, H. M.: Portfolio Selection: Efficient diversification of investments. New York: J. Wiley 1959.

    Google Scholar 

  28. Dantzig, G. B.: Linear programming under uncertainty: Management Sci.1, 197–206 (1955).

    Google Scholar 

  29. Beale, M. L.: On minimizing a convex function subject to linear inequalities. J. Royal Stat. Soc.B17, 173–184 (1955).

    Google Scholar 

  30. Charnes, A., Cooper, W. W., Symonds, G. H.: Cost horizons and certainty equivalents: an approach to stochastic programming of heating oil. Management Sci.4, 235–263 (1958).

    Google Scholar 

  31. Vajda, S.: Probabilistic Programming. New York-London: Academic Press 1972.

    Google Scholar 

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Bereanu, B. On the use of computers in planning under conditions of uncertainty. Computing 15, 11–32 (1975). https://doi.org/10.1007/BF02252833

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