Stochastic Programming Bibliography

Maarten H. van der VlerkDepartment of OperationsUniversity of Groningen

PO Box 800, NL-9700 AV Groningen, The NetherlandsE-mail: m.h.van.der.vlerk@rug.nl

October 8, 2007

One of the sources for this bibliography has been the list of books on Stochastic Programming compiledby J. Dupacova, which can be found in Wets and Ziemba [4033].

Please send additions (preferably in BibTeX format) or comments to the e-mail address mentionedabove.

This bibliography can be cited as

Maarten H. van der Vlerk. Stochastic Programming Bibliography. World Wide Web,http://mally.eco.rug.nl/spbib.html, 1996-2007.

The BibTex entry I use is

@MISC{SPB9607,author = {Maarten H. {van der Vlerk}},title = {Stochastic Programming Bibliography},year = {1996-2007,howpublished = {World Wide Web,

\url{http://mally.eco.rug.nl/spbib.html}}}

where the macro \url is defined in the LATEX style file url.sty.

References[1] I.N. Kamal Abadi, Nicholas G. Hall, and Chelliah Sriskandarajah. Minimizing cycle time in a

blocking flowshop. Oper. Res., 48(1):177–180, 2000.

[2] J. Abaffy and E. Allevi. A modified L-shaped method. J. Optim. Theory Appl., 123(2):255–270,2004.

[3] Moncef Abbas and Fatima Bellahcene. Cutting plane method for multiple objective stochasticinteger linear programming. European J. Oper. Res., 168(3):967–984, 2006.

[4] N. E. Abboud, M. Y. Jaber, and N. A. Noueihed. Economic lot sizing with the consideration ofrandom machine unavailability time. Comput. Oper. Res., 27(4):335–351, 2000.

[5] P. Abel. Decisions in stochastic linear programming models under partial information. Z. Angew.Math. Mech. 73, No.7-8, T 737-T 738, 1993.

[6] Peter Abel. Stochastische Optimierung bei partieller Information, volume 96 of MathematicalSystems in Economics. Verlagsgruppe Athenaum/Hain/Hanstein, Konigstein/Ts., 1984.

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[7] Peter Abel. Stochastic linear programming with recourse under partial information. In Probabilityand Bayesian statistics (Innsbruck, 1986), pages 1–6. Plenum, New York, 1987.

[8] Peter Abel and Reiner Thiel. Mehrstufige stochastische Produktionsmodelle. Eine praxisorientierteDarstellung mit programmierten Beispielen. Schriften zur Quantitativen Wirtschaftsforschung, Bd.5. Frankfurt am Main: Rita G. Fischer Verlag., 1981.

[9] Jinane Abounadi, Dimitri P. Bertsekas, and Vivek Borkar. Stochastic approximation for nonexpan-sive maps: application to Q-learning algorithms. SIAM J. Control Optim., 41(1):1–22 (electronic),2002.

[10] L.M. Abramov and I.I. Bockareva. A stochastic programming problem with probabilistic con-straints. Optimal. Planirovanie, 16:3–9, 1970.

[11] G.M. Adamenko. Solution of extremal problems under conditions of incomplete information.Automat. Control Comput. Sci., 14(4):48–55, 1980.

[12] M. Ju. Afanas’ev. An example of the cycling of a stochastic integer algorithm in a bilevel multi-commodity problem. In Methods of function analysis in mathematical economics (Russian), pages111–114. Izdat. “Nauka”, Moscow, 1978.

[13] P. K. Agarwal, B. K. Bhattacharya, and S. Sen. Improved algorithms for uniform partitions ofpoints. Algorithmica, 32(4):521–539, 2002.

[14] R.A. Agnew and R.B. Hempley. Finite statistical games and linear programming. Naval Res.Logist. Quart. 18, 99-102, 1971.

[15] Saligrama Agnihothri, Uday S. Karmarkar, and Peter Kubat. Stochastic allocation rules. Oper. Res.30, 545-555, 1982.

[16] G.A. Agranovich and L.N. Kanov. A method of computing the gradient and the Hessian of thequality criterion in parametric optimization of continuous-discrete stochastic systems. J. Math.Sci., 82(3):3412–3415, 1996. Dynamical systems, No. 13.

[17] S.C. Agrawal. On mixed integer quadratic programs. Naval Res. Logist. Quart., 21:289–297, 1974.

[18] Vipul Agrawal and Sridhar Seshadri. Distribution free bounds for service constrained .Q; r/ in-ventory systems. Naval Res. Logist., 47(8):635–656, 2000.

[19] C.C. Agunwamba. Optimality condition: constraint regularization. Math. Programming, 13(1):38–48, 1977.

[20] Rudolf Ahlswede and Ingo Wegener. Search problems. (Zadachi poiska). Transl. from the German.Moskva: Mir., 1982.

[21] S. Ahmed and N. V. Sahinidis. Robust process planning under uncertainty. Industrial & Engineer-ing Chemistry Research, 37(5):1883–1892, 1998.

[22] Shabbir Ahmed. Mean-risk objectives in stochastic programming. Stochastic Programming E-PrintSeries, http://www.speps.org, 2004.

[23] Shabbir Ahmed. Convexity and decomposition of mean-risk stochastic programs. Math. Program.,106(3, Ser. A):433–446, 2006.

[24] Shabbir Ahmed. Smooth minimization of two-stage stochastic linear programs. OptimizationOnline, http://www.optimization-online.org, 2006.

[25] Shabbir Ahmed, Ulas Cakmak, and Alexander Shapiro. Coherent risk measures in inventory prob-lems. Stochastic Programming E-Print Series, http://www.speps.org, 2006.

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[26] Shabbir Ahmed, Alan J. King, and Gyana Parija. A multi-stage stochastic integer program-ming approach for capacity expansion under uncertainty. Stochastic Programming E-Print Series,http://www.speps.org, 2001.

[27] Shabbir Ahmed, Alan J. King, and Gyana Parija. A multi-stage stochastic integer program-ming approach for capacity expansion under uncertainty. Optimization Online, http://www.optimization-online.org, 2001.

[28] Shabbir Ahmed and Alexander Shapiro. The sample average approximation methodfor stochastic programs with integer recourse. Optimization Online, http://www.optimization-online.org, 2002.

[29] Shabbir Ahmed, Mohit Tawarmalani, and Nikolaos V. Sahinidis. A finite branch-and-bound algo-rithm for two-stage stochastic integer programs. Math. Program., 100(2, Ser. A):355–377, 2004.

[30] Shabbir Ahmed, Mohit Tawarmalani, and Nikolas V. Sahinidis. A finite branch and boundalgorithm for two-stage stochastic integer programs. Stochastic Programming E-Print Series,http://www.speps.org, 2000.

[31] Byong-Hun Ahn and Bo-Woo Nam. Multiperiod optimal power plant mix under demand uncer-tainty. J. Oper. Res. Soc. Jap. 31, No.3, 353-370, 1988.

[32] M. Aicardi, G. Casalino, F. Davoli, R. Minciardi, and R. Zoppoli. A decentralized closed-loopsolution to the routing problem in networks. Annu. Rev. Autom. Program. 13, Part 2, 9-17, 1986.

[33] Z.Zh. Akhmetzhanova and G.M. Bakan. Solution of a programming problem with inexactly spec-ified initial data. Sov. J. Autom. Inf. Sci. 21, No.2, 55-58 translation from Avtomatika 1988, No.2,54-56 (1988)., 1988.

[34] Hisham Al-Mharmah and James M. Calvin. Optimal random non-adaptive algorithm for globaloptimization of Brownian motion. J. Global Optim., 8(1):81–90, 1996.

[35] Hisham A. Al-Mharmah and James M. Calvin. Comparison of one-dimensional composite andnon-composite passive algorithms. J. Global Optim., 15(2):169–180, 1999.

[36] Aureli Alabert i Romero. On the optimization of hydroelectric power generation with randomwater inflows. Questiio, 15(3):307–348, 1991.

[37] Chris M. Alaouze and Peter J. Lloyd. A generalization of Gurland’s theorem, with applications toeconomic behavior under uncertainty. Am. Stat. 40, 70-71, 1986.

[38] Horst Albach. Capital budgeting and risk management. In Quant. Wirtsch.-Forsch., W. Krelle zum60. Geb., 7-24, 1977.

[39] Maria Albareda-Sambola and Elena Fernandez. The stochastic generalised assignment problemwith Bernoulli demands. Top, 8(2):165–190, 2000.

[40] Maria Albareda-Sambola, Maarten H. van der Vlerk, and Elena Fernandez. Exact solutions toa class of stochastic generalized assignment problems. European J. Oper. Res., 173(2):465–487,2006.

[41] Ya. Alber. Dynamical processes of stochastic approximation. Funct. Differ. Equ., 4(3-4):239–256(1998), 1997.

[42] Ya. I. Al’ber and S.V. Shil’man. Stochastic programming methods: convergence and nonasymptoticestimation of the convergence rate. In Stochastic optimization (Kiev, 1984), volume 81 of LectureNotes in Control and Inform. Sci., pages 249–257. Springer, Berlin, 1986.

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[43] Susanne Albers, Rolf H. Mohring, Georg Ch. Pflug, and Rudiger Schultz. 05031 Summary – Al-gorithms for Optimization with Incomplete Information. In S. Albers, R.H. Mohring, G.Ch. Pflug,and R. Schultz, editors, Dagstuhl Seminar 05031: Algorithms for Optimization with IncompleteInformation, http://www.dagstuhl.de/05031, 2005.

[44] V. Albornoz, J. Arrate, and L. Contesse. Solucion de modelos de dimensionamiento de lotes nocapacitados bajo incertidumbre en las demandas. Revista del Instituto Chileno de InvestigacionOperativa, 6(1-2):52–62, 2001.

[45] V. Albornoz and C. Canales. Planificacion de la conservacion y explotacion del langostino coloradousando un modelo de optimizacion estoc stica no-lineal con recurso. Informacion Tecnologica,13(4):??, 2002.

[46] V. Albornoz and L. Contesse. Modelos de optimizacion robusta para un problema de planificacionagregada de la produccion bajo incertidumbre en las demandas. Investigacion Operativa, 7(3):1–16, 1999.

[47] A. Albrecht, S.K. Cheung, K.C. Hui, K.S. Leung, and C.K. Wong. Optimal placements of flexibleobjects. I. Analytical results for the unbounded case. IEEE Trans. Comput., 46(8):890–904, 1997.

[48] A. Albrecht, S.K. Cheung, K.C. Hui, K.S. Leung, and C.K. Wong. Optimal placements of flexibleobjects. II. A simulated annealing approach for the bounded case. IEEE Trans. Comput., 46(8):905–929, 1997.

[49] S.Christian Albright. A Markov-decision-chain approach to a stochastic assignment problem. Op-erations Res. 22, 61-64, 1974.

[50] Michael Albritton, Alexander Shapiro, and Mark Spearman. Finite capacity production planningwith random demand and limited information. Stochastic Programming E-Print Series, http://www.speps.org, 2000.

[51] David Aldous. Minimization algorithms and random walk on the d-cube. Ann. Probab., 11(2):403–413, 1983.

[52] I.A. Aleksandrov, V.P. Bulatov, S.B. Ognivtsev, and F.I. Yereshko. Solution of a stochastic pro-gramming problem concerning the distribution of water resources. In Stochastic optimization (Kiev,1984), volume 81 of Lecture Notes in Control and Inform. Sci., pages 258–264. Springer, Berlin,1986.

[53] V.M. Aleksandrov, V.I. Sysoev, and V.V. Semeneva. Stochastische Optimierung von Systemen. Izv.Akad. Nauk SSSR, Tekh. Kibern. 1968, No. 5, 14-19, 1968.

[54] A. Alessandri and T. Parisini. Nonlinear modelling of complex large-scale plants using neuralnetworks and stochastic approximation. IEEE Transactions on Systems, Man, and Cybernetics –A, 27:750–757, 1997.

[55] David L. J. Alexander, David Bulger, James M. Calvin, H. Edwin Romeijn, and Ryan L. Sherriff.Approximate implementations of pure random search in the presence of noise. J. Global Optim.,31(4):601–612, 2005.

[56] M. Montaz Ali, Charoenchai Khompatraporn, and Zelda B. Zabinsky. A numerical evaluation ofseveral stochastic algorithms on selected continuous global optimization test problems. J. GlobalOptim., 31(4):635–672, 2005.

[57] M.M. Ali and C. Storey. Topographical multilevel single linkage. J. Global Optim., 5(4):349–358,1994.

[58] M.M. Ali, A. Torn, and S. Viitanen. A numerical comparison of some modified controlled randomsearch algorithms. J. Global Optim., 11(4):377–385, 1997.

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[59] Montaz M. Ali. A probabilistic hybrid differential evolution algorithm. In Models and algorithmsfor global optimization, volume 4 of Springer Optim. Appl., pages 173–184. Springer, New York,2007.

[60] F.M. Allen, R.N. Braswell, and P.V. Rao. Distribution-free approximations for chance constraints.Operations Res., 22(3):610–621, 1974.

[61] Sira Allende and Carlos Bouza. Stochastic programming approaches to the estimation of the meanin stratified population. Investigacion Oper., 14(2-3):109–118, 1993. Workshop on StochasticOptimization: the State of the Art (Havana, 1992).

[62] Sira Allende and Carlos Bouza. Random demands: optimum lot size and the newsboy problem.Investigacion Oper., 23(3):124–129, 2002.

[63] A. Alonso-Ayuso, L. F. Escudero, C. Pizarro, H. E. Romeijn, and D. Romero Morales. On solvingthe multi-period single-sourcing problem under uncertainty. Comput. Manag. Sci., 3(1):29–53,2006.

[64] Mahmoud H. Alrefaei. Stochastic optimization using the standard clock simulation. Int. J. Appl.Math., 8(3):317–333, 2002.

[65] Mahmoud H. Alrefaei and Ameen J. Alawneh. Solution quality of random search methods fordiscrete stochastic optimization. Math. Comput. Simulation, 68(2):115–125, 2005.

[66] Mahmoud H. Alrefaei and Mohammad Almomani. Subset selection of best simulated systems. J.Franklin Inst., 344(5):495–506, 2007.

[67] Mahmoud H. Alrefaei and Sigrun Andradottir. A simulated annealing algorithm with constanttemperature for discrete stochastic optimization. Management Science, 45:748–764, 1999.

[68] Mahmoud H. Alrefaei and Sigrun Andradottir. A modification of the stochastic ruler method fordiscrete stochastic optimization. European J. Oper. Res., 133(1):160–182, 2001.

[69] Mahmoud H. Alrefaei and Sigrun Andradottir. Discrete stochastic optimization using variants ofthe stochastic ruler method. Naval Res. Logist., 52(4):344–360, 2005.

[70] M.H. Alrefaei and S. Andradottir. A new search algorithm for discrete stochastic optimization.Proceedings of the 1995 Winter Simulation Conference 236–241, 1995.

[71] M.H. Alrefaei and S. Andradottir. Discrete stochastic optimization via a modification of thestochastic ruler method. Proceedings of the 1996 Winter Simulation Conference 406–411, 1996.

[72] A.Z. Al’terman. On a realization of random search for the construction of a separating metric.Probl. Sluchajnogo Poiska 7, 307-313, 1978.

[73] A. Altman, M. Amann, G. Klaassen, A. Ruszczynski, and W. Schopp. Cost-effective sulphuremission under uncertainty. European Journal of Operational Research, 90:395–412, 1996.

[74] Adel A. Aly and John A. White. Probabilistic formulations of the multifacility Weber problem.Naval Res. Logist. Quart., 25(3):531–547, 1978.

[75] Yakov Amihud. The effect of uncertainty in input quantities on the optimal expected input combi-nation. Manage. Sci. 23, 957-962, 1977.

[76] H. M. Amman and D. A. Kendrick. Stochastic policy design in a learning environment with rationalexpectations. J. Optim. Theory Appl., 105(3):509–520, 2000. Special Issue in honor of ProfessorDavid G. Luenberger.

[77] Hans M. Amman, David A. Kendrick, and Sudhakar Achath. Solving stochastic optimizationmodels with learning and rational expectations. Econom. Lett., 48(1):9–13, 1995.

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[78] G. Anandalingam. A stochastic programming process model for investment planning. Comput.Oper. Res. 14, 521-536, 1987.

[79] Yu. G. Anastasyan, V.I. Gershovich, B.A. Yaroshevich, E. I. Nenakhov, O.T. Burlak, M.B. Shchep-akin, and G.G. Murauskas. O nekotorykh algoritmakh negladkoi optimizatsii i diskretnogo pro-grammirovaniya, volume 6 of Preprint 81. Akad. Nauk Ukrain. SSR Inst. Kibernet., Kiev, 1981.

[80] E. J. Anderson and A. B. Philpott. On supply function bidding in electricity markets. In C. Green-gard and A. Ruszczynski, editors, Decision Making under Uncertainty: Energy and Power, volume128 of IMA volumes on Mathematics and its Applications, pages 115–134. Springer-Verlag, 2002.

[81] S. Andradottir. A method for discrete stochastic optimization. Management Science 1946–1961,1995.

[82] S. Andradottir. A stochastic approximation algorithm with varying bounds. Operations Research1037–1048, 1995.

[83] S. Andradottir. A global search method for discrete stochastic optimization. SIAM Journal onOptimization 513–530, 1996.

[84] S. Andradottir. Optimization of the transient and steady-state behavior of discrete event systems.Management Science 717–737, 1996.

[85] S. Andradottir. A scaled stochastic approximation algorithm. Management Science 475–498, 1996.

[86] S. Andradottir. Simulation optimization. Handbook on Simulation (edited by Jerry Banks), Chapter9, John Wiley and Sons New York, to appear.

[87] Mikhail Andramonov, Jerzy Filar, Panos Pardalos, and Alexander Rubinov. Hamiltonian cycleproblem via Markov chains and min-type approaches. In Approximation and complexity in numer-ical optimization (Gainesville, FL, 1999), pages 31–47. Kluwer Acad. Publ., Dordrecht, 2000.

[88] G. Andreatta. Shortest path models in stochastic networks. In G. Andreatta, F. Mason, and P. Ser-afini, editors, Stochastics in Combinatorial Optimization, pages 178–186, Singapore, 1987. CISM,Udine, World Scientific Publishing Co. Pte. Ltd.

[89] G. Andreatta and F. Mason. k-Eccentricity and absolute k-centrum of a probabilistic tree. Eur. J.Oper. Res. 19, 114-117, 1985.

[90] G. Andreatta and G. Romanin-Jacur. Aircraft flow management under congestion. TransportationScience, 21(4):249–253, 1987.

[91] G.B. Andreatta, G. Salinetti, and R.J.-B. Wets, editors. Stochastic programming. Baltzer SciencePublishers BV, Amsterdam, 1995. Papers from the Sixth International Conference held in Udine,September 14–18, 1992, Ann. Oper. Res. 56 (1995).

[92] Giovanni Andreatta and Luciano Romeo. Stochastic shortest paths with recourse. Networks 18,No.3, 193-204, 1988.

[93] Giovanni Andreatta and Wolfgang J. Runggaldier. An approximation scheme for stochastic dy-namic optimization problems. Math. Programming Stud., 27:118–132, 1986. Stochastic program-ming 84. I.

[94] Colette Andrieu. Sur les solutions fiables d’un probleme stochastique d’optimisation sous con-trainte. Rev. Roumaine Math. Pures Appl., 25(5):677–694, 1980.

[95] Colette Andrieu. Sur certaines solutions fiables d’un probleme stochastique de recherche optimale.Math. Operationsforsch. Statist. Ser. Optim., 12(1):115–122, 1981.

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[96] Y.P. Aneja and K.P.K. Nair. Maximal expected flow in a network subject to arc failures. Networks10, 45-57, 1980.

[97] R. Anghelescu and V. Anghelescu. Recherches sur la programmation lineaire stochastique a re-cours. (Research on linear stochastic programming with recourse). In Proc. Symp. Math. Appl.,Timisoara/Rom. 1985, 163-165, 1986.

[98] R. Anghelescu and V. Anghelescu. etude sur la programmation lineaire stochastique. In Pro-ceedings of the Second Symposium of Mathematics and its Applications (Timisoara, 1987), pages217–220, Timisoara, 1988. Res. Centre Acad. SR Romania.

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[101] Rodica Anghelescu and V. Anghelescu. Methods in determinating the optimal solution in problemsof linear stochastic programming. Bul. Stiint. Tehn. Univ. Tehn. Timisoara Mat. Fiz., 38(52):94–101, 1993.

[102] Z.P. Anisimova and O.R. Petrenko. Limit theorems of stochastic optimization procedures inMarkov random environments. In Stochastic systems and their applications (Russian), pages 4–8.Akad. Nauk Ukrain. SSR Inst. Mat., Kiev, 1990.

[103] A.N. Antamoshkin and M.A. Valishevskij. Complete effectivity investigation of methods opti-mizing functionals with Boolean derivatives taking random search as example. In Applications ofrandom search to the solution of applied problems, Collect. sci. Works, Kemerovo 1982, 48-54,1982.

[104] K.A. Antanavichyus and S.S. Chirba. A stochastic programming problem for preparing rationalproduction programs for branch complexes. Ehkon. Mat. Metody 21, 1048-1057, 1985.

[105] K.A. Antanavichyus and S.S. Chirba. A problem of stochastic programming for preparation ofrational production programs for branch complexes. Ekonom. i Mat. Metody, 21(6):1048–1057,1985.

[106] P.L. Antonelli and J.M. Skowronski. Adaptive identification of environmental stress for the man-agement of plant growth. Math. Comput. Modelling 10, No.1, 27-35, 1988.

[107] G.E. Antonov and V. Ja. Katkovnik. A generalization of the concept of statistical gradient. Avtomat.i Vycisl. Tehn. (Riga), 4:25–30, 1972.

[108] I.L. Antonov. A steady-state process in a two-dimensional extremal system in the presence of pro-hibited regions and a random method of search. Vestnik Moskov. Univ. Ser. I Mat. Meh., 25(5):117–122, 1970.

[109] I.L. Antonov. A transition process in a two-dimensional extremal system in the presence of for-bidden domains, in a random method of search. In Problems of statistical optimization (Russian),pages 69–80. Izdat. “Zinatne”, Riga, 1971.

[110] Bruno Apolloni and Ferdinando Pezzella. Confidence intervals in the solution of stochastic in-teger linear programming problems. In Stochastics and optimization, Sel. Pap. ISSO Meet.,Gorguano/Italy 1982, Ann. Oper. Res. 1, 67-78, 1984.

[111] N.I. Arbuzova. Ueber die stochastische :-Stabilitaet der Loesung einer Aufgabe der quadratischenProgrammierung. Izv. Akad. Nauk SSSR, tehn. Kibernet. 1967, Nr. 3, 35-40, 1967.

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[112] N.I. Arbuzova. Interdependence of the stochastic �-stabilities of linear and linear fractional pro-gramming problems of a special form. Ekonom. i Mat. Metody, 4:108–110, 1968.

[113] N.I. Arbuzova and V.L. Danilov. Zur Erweiterung des Begriffs der Stabilitaet des Problems derlinearen Programmierung. Kibernetika, Kiev 1970, No.4, 139-140, 1970.

[114] F. Archetti. Evaluation of random gradient techniques for unconstrained optimization. Calcolo 12,83-94, 1975.

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[116] F. Archetti and F. Frontini. The application of a global optimization method to some technologicalproblems. In Towards global optimisation 2, Work shops Varenna 1976 and Bergamo 1977, 179-188, 1978.

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[119] Francesco Archetti. Analysis of stochastic strategies for the numerical solution of the global op-timization problem. In Numerical techniques for stochastic systems, Conf., Gargnano/Italy 1979,275-295, 1980.

[120] Francesco Archetti. A probabilistic algorithm for global optimization problems with a dimension-ality reduction technique. In Optimization techniques (Proc. Ninth IFIP Conf., Warsaw, 1979),Part 2, volume 23 of Lecture Notes in Control and Information Sci., pages 36–42, Berlin, 1980.Springer.

[121] Francesco Archetti and Bruno Betro. Stochastic models and optimization. Boll. Un. Mat. Ital. A(5), 17(2):295–301, 1980.

[122] T. W. Archibald, C. S. Buchanan, K. I. M. McKinnon, and L. C. Thomas. Nested Benders de-composition and dynamic programming for reservoir optimisation. Journal of the OperationalResearch Society, 50(5):468–479, 1999.

[123] T. W. Archibald, C. S. Buchanan, L. C. Thomas, and K. I. M. McKinnon. Controlling multi-reservoir systems. European Journal of Operational Research, 129(3):619–626, 2001.

[124] R. Ardanuy and A. Alcala. Weak infinitesimal operators and stochastic differential games. Stochas-tica, 13(1):5–12, 1992.

[125] K. A. Ariyawansa, C. Cacho, and A. J. Felt. A family of stochastic programming test problemsbased on a model for tactical manpower planning. J. Math. Model. Algorithms, 4(4):369–390,2005.

[126] K. A. Ariyawansa and Andrew J. Felt. On a new collection of stochastic linear programming testproblems. INFORMS J. Comput., 16(3):291–299, 2004.

[127] K. A. Ariyawansa and Yuntao Zhu. Stochastic semidefinite programming: a new paradigm forstochastic optimization. 4OR, 4(3):239–253, 2006.

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[129] K.A. Ariyawansa. Performance of a benchmark implementation of the Van Slyke and Wets algo-rithm for stochastic programs on the alliant FX/8. In Dongarra, Jack (ed.) et al., Proceedings of thefifth SIAM conference on parallel processing for scientific computing, held in Houston, TX, USA,March 25-27, 1991. Philadelphia, PA: SIAM, (ISBN 0-89871-303-X/pbk). 186- 192, 1992.

[130] Vadim I. Arkin. Stochastic optimization approach to dynamic problems with jump changing struc-ture. In Stochastic programming methods and technical applications (Neubiberg/Munich, 1996),pages 104–110. Springer, Berlin, 1998.

[131] V.I. Arkin. Economic dynamics and discretely varying technology. Probabilistic approach. InProbability and mathematical economics, Moskva, 3-30, 1988.

[132] V.I. Arkin and I.V. Evstigneev. Probabilistic models of control and economic dynamics. (Veroyat-nostnye modeli upravleniya i ehkonomicheskoj dinamiki). Moskva: ”Nauka”., 1979.

[133] V.I. Arkin, A. Shiraev, and R. Wets, editors. Stochastic Optimization. Proceedings of the Interna-tional Conference, Kiev 1984. Springer, Berlin, 1986. LN in Control and Information Sciences81.

[134] V.I. Arkin and S.A. Smolyak. On the structure of optimality criteria in stochastic optimizationmodels. In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984, Lect. Notes Control Inf. Sci.81, 275-286, 1986.

[135] Ronald D. Armstrong and Joseph L. Balintfy. A chance constrained multiple choice program-ming algorithm with applications. In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,Oxford, 1974), pages 301–325. Academic Press, London, 1980.

[136] Klaus-Peter Arnold. Stochastische Transportprobleme. Verlag Dr. Kovac, Hamburg, 1987.

[137] H. Arsham. Performance extrapolation in discrete-event systems simulation. International Journalof Systems Science, 27(9):863–869, 1996.

[138] H. Arsham. Stochastic optimization of discrete event systems simulation. International Journal ofMicroelectronics and Reliability, 36(10):1357–1368, 1996.

[139] H. Arsham. Goal seeking problem in discrete event systems simulation. International Journal ofMicroelectronics and Reliability, 37(3):391–395, 1997.

[140] H. Arsham. Algorithms for sensitivity information in discrete-event systems simulation. SimulationPractice and Theory, 6(1):1–22, 1998.

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