Stochastic Programming Bibliography

References with primary subject 90C15 (Stochastic Programming)

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  1. F.Ben Abdelaziz, P. Lang, and R Nadeau. Pointwise efficiency in multiobjec- tive stochastic linear programming. J. Oper. Res. Soc. 45, No.11, 1324-1334, 1994.
  2. P Abel. Decisions in stochastic linear programming models under partial information. Z. Angew. Math. Mech. 73, No.7-8, T 737-T 738, 1993.
  3. Peter Abel. Stochastische optimierung bei partieller information. Math- ematical Systems in Economics, 96. Koenigstein/Ts.: Verlagsgruppe Athenaeum/Hain/Hanstein. XII, 257 p. DM 54.00, 1984.
  4. Peter Abel and Reiner Thiel. Mehrstufige stochastische produktionsmodelle. eine praxisorientierte darstellung mit programmierten beispielen. Schriften zur Quantitativen Wirtschaftsforschung, Bd. 5. Frankfurt am Main: Rita G. Fischer Verlag. VII, 269 p. DM 38.00, 1981.
  5. L.M. Abramov and I.I Bochkareva. A stochastic programming problem with probalistic constraints. Optim. Planirov, Novosibirsk 16, 3-9, 1970.
  6. R.A. Agnew and R.B Hempley. Finite statistical games and linear program- ming. Naval Res. Logist. Quart. 18, 99-102, 1971.
  7. I.A. Aleksandrov, V.P. Bulatov, S.B. Ognivtsev, and F.I. Yereshko. Solution of a stochastic programming problem concerning the distribution of water resources. Stochastic optimization, Proc. Int. Conf, Kiev/USSR 1984, Lect. Notes Control Inf. Sci. 81, 258-264, 1986.
  8. V.M. Aleksandrov, V.I. Sysoev, and V.V Semeneva. Stochastische opti- mierung von systemen. Izv. Akad. Nauk SSSR, Tekh. Kibern. 1968, No.5, 14-19, 1968.
  9. F.M. Allen, R.N. Braswell, and P.V Rao. Distribution-free approximations for chance constraints. Operations Res. 22, 610-621, 1974.
  10. M.H. Alrefaei and S. Andradottir. A new search algorithm for discrete stochastic optimization. Proceedings of the 1995 Winter Simulation Con- ference 236-241, 1995.
  11. M.H. Alrefaei and S. Andradottir. Discrete stochastic optimization via a modification of the stochastic ruler method. Proceedings of the 1996 Winter Simulation Conference 406-411, 1996.
  12. A.Z Al'terman. On a realization of random search for the construction of a separating metric. Probl. Sluchajnogo Poiska 7, 307-313, 1978.
  13. G. Anandalingam. A stochastic programming process model for investment planning. Comput. Oper. Res. 14, 521-536, 1987.
  14. S. Andradottir. A method for discrete stochastic optimization. Management Science 1946-1961, 1995.
  15. S. Andradottir. A stochastic approximation algorithm with varying bounds. Operations Research 1037-1048, 1995.
  16. S. Andradottir. A global search method for discrete stochastic optimization. SIAM Journal on Optimization 513-530, 1996. 1 ^L
  17. S. Andradottir. Optimization of the transient and steady-state behavior of discrete event systems. Management Science 717-737, 1996.
  18. S. Andradottir. A scaled stochastic approximation algorithm. Management Science 475-498, 1996.
  19. S. Andradottir. Simulation optimization. Handbook on Simulation (edited by Jerry Banks), Chapter 9, John Wiley and Sons New York, to appear.
  20. Giovanni Andreatta and Wolfgang J. Runggaldier. An approximation scheme for stochastic dynamic optimization problems. Math. Program. Study 27, 118-132, 1986.
  21. Colette Andrieu. Sur les solutions fiables d'un probleme stochastique d'opti- misation sous contrainte. Rev. Roum. Math. Pures Appl. 25, 677-694, 1980.
  22. Colette Andrieu. Sur certaines solutions fiables d'un probleme stochastique de recherche optimale. Math. Operationsforsch. Stat, Ser. Optimization 12, 115-122, 1981.
  23. R. Anghelescu and V. Anghelescu. Recherches sur la programmation lineaire stochastique a recours. (research on linear stochastic programming with re- course). Proc. Symp. Math. Appl, Timisoara/Rom. 1985, 163-165, 1986.
  24. R. Anghelescu and V. Anghelescu. Etude sur la programmation lineaire stochastique. (a study on linear stochastic programming). Mathematics and its applications, Proc. 2nd Symp, Timisoara/Rom. 1987, 217-220, 1988.
  25. Bruno Apolloni and Ferdinando Pezzella. Confidence intervals in the so- lution of stochastic integer linear programming problems. Stochastics and optimization, Sel. Pap. ISSO Meet, Gorguano/Italy 1982, Ann. Oper. Res. 1, 67-78, 1984.
  26. N.I Arbuzova. Ueber die stochastische :-stabilitaet der loesung einer aufgabe der quadratischen programmierung. Izv. Akad. Nauk SSSR, tehn. Kibernet. 1967, Nr. 3, 35-40, 1967.
  27. N.I. Arbuzova and V.L Danilov. Zur erweiterung des begriffs der stabilitaet des problems der linearen programmierung. Kibernetika, Kiev 1970, No.4, 139-140, 1970.
  28. F. Archetti and F. Schoen. A survey on the global optimization problem: General theory and computational approaches. Stochastics and optimization, Sel. Pap. ISSO Meet, Gorguano/Italy 1982, Ann. Oper. Res. 1, 87-110, 1984.
  29. Francesco Archetti and Bruno Betro. Stochastic models and optimization. Boll. Unione Mat. Ital, V. Ser, A 17, 295-301, 1980.
  30. Ronald D. Armstrong and Joseph L Balintfy. A chance constrained multiple choice programming algorithm. Operations Res. 23, 494-510, 1975.
  31. Ronald D. Armstrong and Joseph L Balintfy. A chance constrained multiple choice programming algorithm with applications. Stochastic programming, Proc. int. Conf, Oxford 1974, 301-325, 1980.
  32. Zvi Artstein and Roger J.-B Wets. Sensors and information in optimization under stochastic uncertainty. Math. Oper. Res. 18, No.3, 523-547, 1993. 2 ^L
  33. Zvi Artstein and Roger J.-B Wets. Stability results for stochastic programs and sensors, allowing for discontinuous objective functions. SIAM J. Optim. 4, No.3, 537-550, 1994.
  34. Kelly T. Au, Julia L. Higle, and Suvrajeet Sen. Inexact subgradient methods with applications in stochastic programming. Math. Program. 63A, No.1, 65-82, 1994.
  35. I.L Averbakh. An additive method for optimization of two-stage stochastic systems with discrete variables. Sov. J. Comput. Syst. Sci. 28, No.4, 161-165 ; translation from Izv. Akad. Nauk SSSR, Tekh. Kibern. 1990, No.1, 162-166 (1990), 1990.
  36. I.L Averbakh. An iterative decomposition method in one-step problems of stochastic integer programming. Zh. Vychisl. Mat. Mat. Fiz. 30, No.10, 1467-1476, 1990.
  37. I.L Averbakh. An iterative decomposition method in single-stage stochastic integer- programming problems. U.S.S.R. Comput. Math. Math. Phys. 30, No.5, 133-139 ; translation from Zh. Vychisl. Mat. Mat. Fiz. 30, No.10, 1467-1476 (1990), 1990.
  38. I.L Averbakh. On an algorithm for solving the m-dimensional knapsack problem with random coefficients. Diskretn. Mat. 2, No.3, 3-9, 1990.
  39. I.L Averbakh. An iterative method for solving two-step discrete problems of stochastic programming with additively separable variables. Zh. Vychisl. Mat. Mat. Fiz. 31, No.6, 810-818, 1991.
  40. I.L Averbakh. An iterative method of solving two-stage discrete stochastic programming problems with additively separable variables. Comput. Math. Math. Phys. 31, No.6, 21-27 ; translation from Zh. Vychisl. Mat. Mat. Fiz. 31, No.6, 810-818 (1991), 1991.
  41. I.L Averbakh. An algorithm of solving the m-dimensional knapsack problem with random coefficients. Discrete Math. Appl. 2, No.2, 133-140 ; translation from Diskretn. Mat. 2, No.3, 3-9 (1990), 1992.
  42. M. Avriel and D.J Wilde. Stochastic geometric programming. Proc. Prince- ton Sympos. math. Programming, Princeton 1967, 73-91, 1970.
  43. M. Avriel and A.C Williams. The value of information and stochastic pro- gramming. Oper. Res. 18, 947-954, 1970.
  44. S.V Avrutin. On the formulation and solution of some non-linear stochastic problems. Optimization methods and their applications, 9, Work Collect, Irkutsk 1979, 5-13, 1979.
  45. F. Azadivar and J Talavage. Optimization of stochastic simulation models. Math. Comput. Simulation 22, 231-241, 1980.
  46. Farhad Azadivar and Young-Hae Lee. Optimization of discrete variable stochastic systems by computer simulation. Math. Comput. Simulation 30, No.4, 331-345, 1988.
  47. N Baba. Global optimization of functions by the random optimization method. Int. J. Control 30, 1061-1065, 1979. 3 ^L
  48. N Baba. Convergence of a random optimization method for constrained optimization problems. J. Optimization Theory Appl. 33, 451-461, 1981.
  49. Norio Baba and Akira Morimoto. Three approaches for solving the stochas- tic multiobjective programming problem. Stochastic optimization. Numeri- cal methods and technical applications, Proc. GAMM/IFIP-Workshop, Neu- biberg/Ger. 1990, Lect. Notes Econ. Math. Syst. 379, 93-109, 1992.
  50. Norio Baba and Akira Morimoto. Stochastic approximation method for solv- ing the stochastic multiobjective programming problem. Int. J. Syst. Sci. 24, No.4, 789-796, 1993.
  51. Anna Badach. Application of reliability theory to stochastic linear program- ming problems. Przegl. Stat. 22, 569-584, 1975.
  52. Michael P Bailey. Solving a class of stochastic minimization problems. Oper. Res. 42, No.3, 428-438, 1994.
  53. M.M. Bajzel' and G.S Tarasenko. The investigation of an adaptive opti- mization algorithm in the situation of noise. Probl. Sluchajnogo Poiska 9, 106-124, 1981.
  54. A.V Balakrishnan. Stochastic optimization theory in hilbert spaces. i. Appl. Math. Optimization 1, 97-120, 1974.
  55. Erik J Balder. Existence without explicit compactness in stochastic dynamic programming. Math. Oper. Res. 17, No.3, 572-580, 1992.
  56. J.F. Balducchi, G. Cohen, J.-C. Dodu, M. Goursat, Herz, J.-P. Quadrat, and M. Viot. Three methods for optimizing the capacities of an electrical transmission network. 8th IFAC World Congress, Kyoto, Japan, 1981.
  57. A.K Balyko. A problem of stochastic programming. Eng. Cybern. 20, No.1, 43-49 ; translation from Izv. Akad. Nauk SSSR, Tekh. Kibern. 1982, No.1, 61-67 (1982), 1982.
  58. David P Baron. Information in two-stage programming under uncertainty. Naval Res. Logist. Quart. 18, 169-176, 1971.
  59. A.M. Bartsalkin and D.K. Zambitskij. On an allocation problem with prob- abilistic demands. Mat. Issled. 87, 10-15, 1986.
  60. John Bather. Randomised allocation of treatments in sequential trials. Adv. Appl. Probab. 12, 174-181, 1980.
  61. Vijay S Bawa. On chance constrained programming problems with joint constraints. Management Sci, Theory 19, 1326-1331, 1973.
  62. V.S Bawa. A simple concavity condition for a class of chance-constrained programming problems with joint constraints. Operations Res. 24, 378-380, 1976.
  63. L.G. Bazenov and A.M Gupal. Ueber ein stochastisches analogon der meth- ode der zulaessigen richtungen. Kibernetika, Kiev 1973, Nr. 5, 94-95, 1973.
  64. E.M.L. Beale, G.B. Dantzig, and R.D. Watson. A first order approach to a class of multi-time-period stochastic programming problems. Math. Program. Study 27, 103-117, 1986. 4 ^L
  65. E.M.L. Beale, J.J.H. Forrest, and C.J Taylor. Multi-time-period stochastic programming. Stochastic programming, Proc. int. Conf, Oxford 1974, 387- 402, 1980.
  66. C.N. Beer and B.L Foote. A procedure to rank bases by probability of being optimal using imbedded hyperspheres. Math. Programming 9, 123-128, 1975.
  67. I.V. Beiko and P.N Zin'ko. Some probability estimates for algorithms of stochastic programming with a constant step multiplier. Kibernetika, Kiev 1978, No.2, 91-95, 1978.
  68. Yu.A Belov. Two-level problem of stochastic programming. Dopov. Akad. Nauk Ukr. RSR, Ser. A 1979, 370-374, 1979.
  69. Yu.A Belov. Block stochastic optimization problem with probability con- straints and deterministic solution. Dopov. Akad. Nauk Ukr. RSR, Ser. A 1980, No.8, 61-64, 1980.
  70. Yu.A Belov. The block two-stage stochastic programming problem with continuous distribution of the constraint vector. Cybernetics 16, 145-148 ; translation from Kibernetika 1980, No.1, 128-130 (1980), 1980.
  71. Yu.A Belov. Decomposition of two-stage problems of stochastic block-type programming. Sov. Phys, Dokl. 25, 971-972 ; translation from Dokl. Akad. Nauk SSSR 255, 811-813 (1980), 1980.
  72. Yu.A Belov. Die monotonie bezueglich eines funktionals eines iterativen algorithmus fuer ein blockproblem mit zufaelligen parametern. Zh. Vychisl. Mat. Mat. Fiz. 20, 298-305, 1980.
  73. Yu.A Belov. Monotonicity with respect to functional of an iterative algorithm for the block problem with random parameters. U.S.S.R. Comput. Math. Math. Phys. 20, No.2, 24-32 ; translation from Zh. Vychisl. Mat. Mat. Fiz. 20, 298-305 (1980), 1980.
  74. Yu.A Belov. A one-stage stochastic problem of block type with row proba- bility constraints. Zh. Vychisl. Mat. Mat. Fiz. 21, 1133-1139, 1981.
  75. Yu.A Belov. A single-stage block type stochastic problem with row proba- bility constraints. U.S.S.R. Comput. Math. Math. Phys. 21, No.5, 58-64 ; translation from Zh. Vychisl. Mat. Mat. Fiz. 21, 1133-1139 (1981), 1981.
  76. Yu.A Belov. Two-level optimization for a one-stage problem of stochastic programming with probabilistic restrictions. Vychisl. Prikl. Mat, Kiev 43, 78-85, 1981.
  77. Yu.A Belov. A stochastic normal model with solving distributions for block- type extremal problems. Cybernetics 19, 239-247 ; translation from Kiber- netika 1983, No.2, 72-77 (1983), 1983.
  78. L.S Belyaev. The solution of complex optimization problems under uncertainty conditions. (reshenie slozhnykh optimizatsionnykh zadach v usloviyakh neopredelennosti). Akademiya Nauk SSSR, Sibirskoe Otdelenie, Sibirskij Ehnergeticheskij Institut. Novosibirsk: Izdatel'stvo Nauka, Sibirskoe Otdelenie. 127 p. R. 0.90, 1978.
  79. A. Ben-Tal and M. Teboulle. Penalty functions and duality in stochastic programming via OE- divergence functionals. Math. Oper. Res. 12, 224-240, 1987. 5 ^L
  80. Aharon Ben-Tal. The entropic penalty approach to stochastic programming. Math. Oper. Res. 10, 263-279, 1985.
  81. Aharon Ben-Tal and Marc Teboulle. The duality between expected util- ity and penalty in stochastic linear programming. Stochastic programming, Conf. Gargnano/Italy 1983, Lect. Notes Control Inf. Sci. 76, 151-161, 1986.
  82. Aharon Ben-Tal and Marc Teboulle. Expected utility, penalty functions, and duality in stochastic nonlinear programming. Manage. Sci. 32, 1445-1466, 1986.
  83. A. Benveniste, P. Bernhard, and G. Cohen. On the decomposition of stochas- tic control problems. 1rst IFAC Symposium on Large Scale Systems Theory and Applications, Udine, Italie, 1976.
  84. A. Benveniste and P Crepel. Normalite asymptotique: Approximation diffu- sion pour les algorithmes recursifs: Et applications. Cah. Cent. Etud. Rech. Oper. 23, 3-26, 1981.
  85. Albert Benveniste. Introduction a la methode de l'equation differentielle moyenne pour l'etude des algorithmes recursifs. exemples. Cah. Cent. Etud. Rech. Oper. 22, 219-241, 1980.
  86. Rustem Berc. Robust optimal policy methods for nonlinear models. Op- erations research proceedings 1990, Pap. 19th Annu. Meet. DGOR, Vi- enna/Austria 1990, 262-271, 1992.
  87. Rustem Berc. Robust optimal policy methods for nonlinear models. Op- erations research proceedings 1990, Pap. 19th Annu. Meet. DGOR, Vi- enna/Austria 1990, 264-271, 1992.
  88. B Bereanu. On stochastic linear programming. Comun. Acad. Republ. popul. Romine 13, 517-522, 1963.
  89. B Bereanu. On the distribution of the optimum in stochastic linear pro- gramming. An. Univ. Bucuresti, Ser. Sti. Natur, Mat.-Mec. 14, No.2, 41-47, 1965.
  90. B Bereanu. Programmation stochastique et quelques-unes de ses applications economiques. Publ. econometr. 5, 143-161, 1972.
  91. B Bereanu. Linear optimization with mixed indeterminacies in data. Stud. Cercet. Mat. 33, 601-610, 1981.
  92. B Bereanu. Minimum risk criterion in stochastic optimization. Econ. Com- put. Econ. Cybern. Stud. Res. 1981, No.2, 31-39, 1981.
  93. B. Bereanu and G Peeters. A wait-and-see problem in stochastic linear programming. an experimental computer code. Cah. Cent. Etud. Rech. Oper. 12, 133-148, 1970.
  94. Bernard Bereanu. Quasi-convexity, strictly quasi-convexity and pseudo- convexity of composite objective functions. Revue Franc. Automat. Inform. Rech. operat. 6, Nr. R-1, 15-26, 1972.
  95. Bernard Bereanu. The cartesian integration method in stochastic linear programming. Numer. Meth. Optimierungsaufg, Tagung Oberwolfach 1971, ISNM 17, 9-20, 1973. 6 ^L
  96. Bernard Bereanu. On stochastic linear programming. iv: Some numerical methods and their computer implementation. Proc. 4th Conf. Probab. The- ory, Brasov 1971, 13-36, 1973.
  97. Bernard Bereanu. Stable stochastic linear programs and applications. Math. Operationsforsch. Statistik 6, 593-607, 1975.
  98. Bernard Bereanu. The generalized distribution problem of stochastic linear programming. Symp. math. 19, Program. mat. Appl, Convegno 1974, 229- 267, 1976.
  99. Bernard Bereanu. On some distribution-free, optimal solutions/bases, in stochastic linear programming. Revue Roumaine Math. pur. appl. 21, 643- 657, 1976.
  100. Bernard Bereanu. Stochastic-parametric linear programs. i. Revue Roumaine Math. Pur. Appl. 22, 1367-1380, 1977.
  101. Bernard Bereanu. A unifying approach to stochastic linear programming. Proc. 5th Conf. Probab. Theory, Brasov 1974, 23-31, 1977.
  102. Bernard Bereanu. Some numerical methods in stochastic linear programming under risk and uncertainty. Stochastic programming, Proc. int. Conf, Oxford 1974, 169-205, 1980.
  103. Bernard Bereanu. Stochastic-parametric linear programs. ii. Recent results in stochastic programming, Proc, Oberwolfach 1979, Lect. Notes Econ. Math. Syst. 179, 3-20, 1980.
  104. Bernard Bereanu. An approach to complexity of optimization linear models. Probability theory, Proc. 6th Conf, Brasov/Romania 1979, 385-394, 1981.
  105. Bernard Bereanu. Programming with composite indeterminacies in data. Methods Oper. Res. 41, 117-120, 1981.
  106. Bernard Bereanu. Programming with objective and conventional perturba- tion in data. Studies in probability and related topics, Pap. Hon. O. Onicescu on his 90th Birthday, 21-38, 1983.
  107. V.A Bereznev. A stochastic programming problem with probalistic con- straints. Izv. Akad. Nauk SSSR, tehn. Kibernet. 1971, No.4, 27-33, 1971.
  108. Adam J. Berger, John M. Mulvey, and Andrzej Ruszczynski. An extension of the dqa algorithm to convex stochastic programs. SIAM J. Optim. 4, No.4, 735-753, 1994.
  109. Adam J. Berger, John M. Mulvey, and Andrzej Ruszczynski. Restarting strategies for the dqa algorithm. Hager, W. W. (ed.) et al, Large scale opti- mization. State of the art. Papers presented at the conference, held February 15-17, 1993 at the University of Florida, Gainesville, FL, USA. Dordrecht: Kluwer Academic Publishers, (ISBN 0-7923-2798-5/hbk). 1-25, 1994.
  110. E.M Berkovic. The approximation of two-stage stochastic extremal problems. U.S.S.R. Comput. Math. math. Phys. 11, Nr. 5, 69-88 (1973), 1971.
  111. E.M Berkovich. The approximation of two-stage stochastic extrem problems. Zh. vycislit. Mat. Mat. Fiz. 11, 1150-1165, 1971.
  112. D.P Bertsekas. Stochastic optimization problems with nondifferentiable cost functionals. J. Optimization Theory Appl. 12, 218-231, 1973. 7 ^L
  113. D.P Bertsekas. Stochastic optimization problems with nondifferentiable cost functionals. J. Optimization Theory Appl., 1973.
  114. Malay Bhattacharyya. Joint randomized decisions in chance-constrained pro- gramming. J. Oper. Res. Soc. 35, 355-357, 1984.
  115. John R Birge. The value of the stochastic solution in stochastic linear pro- grams with fixed recourse. Math. Program. 24, 314-325, 1982.
  116. John R. Birge. Aggregation bounds in stochastic linear programming. Math. Program. 31, 25-41, 1985.
  117. John R. Birge. Decomposition and partitioning methods for multistage stochastic linear programs. Oper. Res. 33, 989-1007, 1985.
  118. John R. Birge and M.A.H Dempster. Optimal match-up strategies in stochas- tic scheduling. Discrete Appl. Math. 57, No.2-3, 105-120, 1995.
  119. John R. Birge and Jose H Dula. Bounding separable recourse functions with limited distribution information. Ann. Oper. Res. 30, 277-298, 1991.
  120. John R. Birge and Francois V. Louveaux. A multicut algorithm for two-stage stochastic linear programs. Eur. J. Oper. Res. 34, No.3, 384-392, 1988.
  121. John R. Birge and Robert L. Smith. Random procedures for nonredundant constraint identification in stochastic linear programs. Am. J. Math. Manage. Sci. 4, 41-70, 1984.
  122. John R. Birge and Stein W. Wallace. Refining bounds for stochastic linear programs with linearly transformed independent random variables. Oper. Res. Lett. 5, 73-77, 1986.
  123. John R. Birge and Stein W. Wallace. A separable piecewise linear upper bound for stochastic linear programs. SIAM J. Control Optimization 26, No.3, 725-739, 1988.
  124. John R. Birge and Roger J.-B. Wets. Designing approximation schemes for stochastic optimization problems, in particular for stochastic programs with recourse. Math. Program. Study 27, 54-102, 1986.
  125. John R. Birge and Roger J.-B. Wets. Computing bounds for stochastic programming problems by means of a generalized moment problem. Math. Oper. Res. 12, 149-162, 1987.
  126. John R. Birge and Roger J.-B. Wets. Sublinear upper bounds for stochastic programs with recourse. Math. Program, Ser. A 43, No.2, 131-149, 1989.
  127. J.R. Birge. An l-shaped method computer code for multistage stochastic linear programs. Numerical techniques for stochastic optimization, Springer Ser. Comput. Math. 10, 255-266, 1988.
  128. J.R. Birge. The relationship between the l-shaped method and dual basis factorization for stochastic linear programming. Numerical techniques for stochastic optimization, Springer Ser. Comput. Math. 10, 267-272, 1988.
  129. J.R. Birge and M.A.H. Dempster. Stochastic programming approaches to stochastic scheduling. Journal of Global Optimization 9:417-451, 1996. 8 ^L
  130. J.R. Birge and C.J. Donohue. An upper bound on the expected value of a non-increasing convex function with convex marginal return functions. Op- erations Research Letters 18:213-221, 1996.
  131. J.R. Birge, C.J. Donohue, D.F. Holmes, and O.G. Svintsiski. A parallel im- plementation of the nested decomposition algorithm for multistage stochastic linear programs. Mathematical Programming 75:327-352, 1996.
  132. J.R. Birge, C.J. Donohue, D.F. Holmes, and O.G. Svintsitski. A parallel im- plementation of the nested decomposition algorithm for multistage stochastic linear programs. Mathematical Programming, 75(2):327-352, 1996.
  133. J.R. Birge and D.F Holmes. Efficient solution of two-stage stochastic linear programs using interior point methods. Comput. Optim. Appl. 1, No.3, 245- 276, 1992.
  134. J.R. Birge and C.H. Rosa. Incorporating investment uncertainty into green- house policy models. Energy Journal, 17:79-90, 1996.
  135. J.R. Birge and C.H. Rosa. Incorporating investment uncertainty into green- house policy models. The Energy Journal 17:79-90, 1996.
  136. J.R. Birge and C.H. Rosa. Parallel decomposition of large scale stochastic nonlinear programs. Annals of Operations Research, 64:39-65, 1996.
  137. Roger A Blau. Stochastic programming and decision analysis: An apparent dilemma. Management Sci, Theory 21, 271-276, 1974.
  138. I.I Bochkareva. The algorithm for the solution of a certain class of stochas- tic programming problems with probalistic constraints. Optim. Planirov, Novosibirsk 16, 10-15, 1970.
  139. C.E Bocvarova. Ueber die wahl der garantierten wahrscheinlichkeiten in problemen der stochastischen programmierung mit wahrscheinlichkeits- beschraenkungen. Izv. Akad. Nauk SSSR, tehn. Kibernet. 1975, Nr. 6, 47-54, 1975.
  140. Juergen Boettcher. Stochastische lineare programme mit kompensation. (stochastic linear programs with recourse). Mathematical Systems in Eco- nomics, 115. Frankfurt a. M.: Athenaeum (ISBN 3-610-09814-7). x, 139 S. DM 48.00, 1989.
  141. A.N. Bordunov, A.I. Yastremskij, and N. Utenliev. On duality relations and optimality conditions in linear problems of stochastic programming with discretely distributed random variables. Issled. Oper. ASU 29, 27-37, 1987.
  142. N.N. Bordunov. Extremum conditions for a multistep stochastic program- ming problem with smooth constraints. Cybernetics 20, 863-870 ; translation from Kibernetika 1984, No.6, 77-82, 1984.
  143. N.N Bordunov. Dynamical optimization models under conditions of un- certainty: multistage problems of stochastic programming (from iiasa re- ports). (dinamicheskie optimizatsionnye modeli v usloviyakh neopredelen- nosti: mnogoehtapnye zadachi stokhasticheskogo programmirovaniya (po materialam iiasa)). Kiev: Institut Kibernetiki AN USSR, 17 p. R. 0.20, 1991. 9 ^L
  144. Carlos Bouza Herrera. Bounding the expected approximation error in stochastic linear programming with complete fixed recourse. Kall, Peter (ed.), System modelling and optimization. Proceedings of the 15th IFIP con- ference, Zurich, Switzerland, September 2-6, 1991. Berlin: Springer-Verlag, (ISBN 3-540-55577-3). Lect. Notes Control Inf. Sci. 180, 541-545, 1992.
  145. Aziz Bouzaher and Susan Offutt. A stochastic linear programming model for corn residue production. J. Oper. Res. Soc. 43, No.9, 843-857, 1992.
  146. Friedrich Bozena and Klaus Tammer. Untersuchungen zur stabilitaet einiger ersatzprobleme der stochastischen optimierung in bezug auf aenderungen des zugrunde gelegten zufallsvektors. Wiss. Z. Humboldt-Univ. Berlin, Math.- Naturwiss. Reihe 30, 373-376, 1981.
  147. Noel A. Bryson and Saul I Gass. Solving discrete stochastic linear programs with simple recourse by the dualplex algorithm. Comput. Oper. Res. 21, No.1, 11-17, 1994.
  148. J.J. Buckley. Possibility and necessity in optimization. Fuzzy Sets Syst. 25, 1-13, 1988.
  149. J.J. Buckley. Stochastic versus possibilistic programming. Fuzzy Sets Syst. 34, No.2, 173-177, 1990.
  150. W Buehler. Ein kombiniertes kompensations-chance-constrained modell der stochastischen linearen programmierung. Operations Res.-Verf. 12, IV. Oberwolfach-Tag. Operations Res. 1971, 61-68, 1972.
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