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Two-stage stochastic optimization is a useful tool for making optimal decisions under uncertainty. Frederike Neise describes two concepts to handle the classic linear mixed-integer two-stage stochastic optimization problem: The well-known mean-risk modeling, which aims at finding a best solution in terms of expected costs and risk measures, and stochastic programming with first order dominance constraints that heads towards a decision dominating a given cost benchmark and optimizing an additional objective. For this new class of stochastic optimization problems results on structure and stability are proven. Moreover, the author develops equivalent deterministic formulations of the problem, which are efficiently solved by the presented dual decomposition method based on Lagrangian relaxation and branch-and-bound techniques. Finally, both approaches – mean-risk optimization and dominance constrained programming – are applied to find an optimal operation schedule for a dispersed generation system, a problem from energy industry that is substantially influenced by uncertainty.

Integer programming. --- Programming (Mathematics) --- Mathematical programming --- Goal programming --- Algorithms --- Functional equations --- Mathematical optimization --- Operations research --- Distribution (Probability theory. --- Mathematics. --- Probability Theory and Stochastic Processes. --- Mathematics, general. --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk

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This book constitutes the refereed proceedings of the 11th International Conference on Theory and Applications of Satisfiability Testing, SAT 2008, held in Guangzhou, P. R. China, in May 2008. The 17 revised full papers presented together with 8 revised short papers and 2 invited talks were carefully selected from 70 submissions. All current research issues in propositional and quantified Boolean formula satisfiability testing are covered, including but not limited to proof systems, proof complexity, search algorithms, heuristics, analysis of algorithms, hard instances, randomized formulae, problem encodings, industrial applications, solvers, simplifiers, tools, case studies and empirical results.

Propositional calculus --- Decision making --- Computer algorithms --- Algebra, Boolean --- Calcul propositionnel --- Prise de décision --- Algorithmes --- Algèbre de Boole --- Congresses. --- Congrès --- Mathematical Theory --- Mathematics --- Physical Sciences & Mathematics --- 681.3*F41 <63> --- 681.3*I23 <063> --- 681.3*I28 <063> --- 681.3*F22 <063> --- 681.3*G16 <063> --- Mathematical logic: computability theory; computational logic; lambda calculus; logic programming; mechanical theorem proving; model theory; proof theory;recursive function theory--See also {681.3*F11}; {681.3*I22}; {681.3*I23}--Congressen --- Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence)--Congressen --- Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--Congressen--See also {681.3*F22} --- Nonnumerical algorithms and problems: complexity of proof procedures; computations on discrete structures; geometrical problems and computations; pattern matching --See also {?681.3*E2-5}; {681.3*G2}; {?681.3*H2-3}--Congressen --- Optimization: constrained optimization; gradient methods; integer programming; least squares methods; linear programming; nonlinear programming (Numericalanalysis) --- Congressen --- 681.3*I23 <063> Deduction and theorem proving: answer/reason extraction; reasoning; resolution; metatheory; mathematical induction; logic programming (Artificial intelligence)--Congressen --- Boolean algebra --- Boole's algebra --- Calculus, Propositional --- Computer science. --- Operating systems (Computers). --- Algorithms. --- Mathematical logic. --- Numerical analysis. --- Artificial intelligence. --- Computer Science. --- Mathematical Logic and Formal Languages. --- Algorithm Analysis and Problem Complexity. --- Operating Systems. --- Numeric Computing. --- Artificial Intelligence (incl. Robotics). --- Mathematical Logic and Foundations. --- Logic, Symbolic and mathematical --- Algebraic logic --- Set theory --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Mathematical analysis --- Algebra of logic --- Logic, Universal --- Mathematical logic --- Symbolic and mathematical logic --- Symbolic logic --- Algebra, Abstract --- Metamathematics --- Syllogism --- Algorism --- Algebra --- Arithmetic --- Computer operating systems --- Computers --- Disk operating systems --- Systems software --- Informatics --- Science --- Foundations --- Operating systems --- Computer software. --- Electronic data processing. --- Logic, Symbolic and mathematical. --- Artificial Intelligence. --- ADP (Data processing) --- Automatic data processing --- Data processing --- EDP (Data processing) --- IDP (Data processing) --- Integrated data processing --- Office practice --- Software, Computer --- Computer systems --- Automation --- Propositional calculus. --- Machine theory. --- Formal Languages and Automata Theory. --- Numerical Analysis. --- Abstract automata --- Abstract machines --- Automata --- Mathematical machine theory --- Algorithms --- Recursive functions --- Robotics