Contents -- Acknowledgements -- List of Contributors -- Preface -- Books and Collections of Papers on Stochastic Programming -- 1. Introduction and Summary -- Part I. Papers in Finance -- 2. Longevity Risk Management for Individual Investors Woo Chang Kim, John M. Mulvey, Koray D. Simsek and Min Jeong Kim -- 1 Introduction -- 2 Model -- 3 Numerical results -- 3.1 First example: Retirement planning without longevity risk consideration -- 3.2 Second example: Impact of longevity risk to retirement planning -- 3.3 Third example: Longevity risks in pension benefits -- 4 Conclusions -- References -- 3. Optimal Stochastic Programming-Based Personal Financial Planning with Intermediate and Long-Term Goals Vittorio Moriggia, Giorgio Consigli and Gaetano Iaquinta -- 1 Introduction -- 2 The asset-liability management model -- 2.1 Individual wealth, consumption and investment targets -- 2.2 Random coefficients and scenarios -- 2.3 The optimization problem -- 3 Numerical implementation and case study -- 3.1 Decision tool modular structure -- 3.1.1 Individual policy statement -- 3.1.2 Scenario manager -- 3.1.3 Output -- 3.2 Case study -- 3.2.1 Optimal solutions -- 4 Conclusion -- References -- 4. Intertemporal Surplus Management with Jump Risks Mareen Benk -- 1 Introduction -- 2 An intertemporal surplus management model with jump risks - a three-fund theorem -- 3 Risk preference, and funding ratio -- 4 Conclusions -- Appendix I: Derivation of the asset specific risk factor of the first jump component -- Appendix II: Derivation of equation (16) -- Appendix III: Derivation of equation (17) -- References -- 5. Jump-Diffusion Risk-Sensitive Benchmarked Asset Management Mark Davis and Sebastien Lleo -- 1 Introduction -- 2 Analytical setting -- 2.1 Factor dynamics -- 2.2 Asset market dynamics -- 2.3 Benchmark modelling -- 2.4 Portfolio dynamics.

2.5 Investment constraints -- 2.6 Problem formulation -- 3 Dynamic programming and the value function -- 3.1 The risk-sensitive control problems under Ph -- 3.2 Properties of the value function -- 3.3 Main result -- 4 Existence of a classical (C1,2) solution under affine drift assumptions -- 5 Existence of a classical (C1,2) solution under standard control assumptions -- 6 Verification -- 6.1 The unique maximizer of the supremum (60) is the optimal control, i.e. h*(t,Xt) = h (t,Xt,D (t,Xt)) -- 6.2 Verification -- 7 Conclusion -- References -- 6. Dynamic Portfolio Optimization under Regime-Based Firm Strength Chanaka Edirisinghe and Xin Zhang -- 1 Introduction -- 2 DEA-based relative firm strength -- 2.1 Financial DEA model -- 2.2 Parameters of RFS -- 2.3 Correlation analysis -- 3 Modeling market regimes -- 3.1 Regime analysis (1971-2010) -- 3.2 Regime-based firm-RFS -- 4 Portfolio optimization under regime-based RFS -- 4.1 RFS-based stock selections -- 4.2 Decisions under regime-scenarios -- 4.3 Transactions cost model -- 4.4 Budget constraints -- 4.5 Risk-return framework -- 4.6 Two-period optimization model -- 5 Model application -- 5.1 RFS estimation and firm selections -- 5.2 Portfolio analysis -- 6 Conclusions -- Acknowledgement -- References -- 7. Options Portfolio Management as a Chance Constrained Problem Dmitry Golembiovsky and Anatoliy Abramov -- 1 Introduction -- 2 Model of options market -- 3 A scenario model for stochastic programming -- 4 The problem of option portfolio optimization -- 5 Solving the problem and some simulation results -- 6 Conclusions and future research -- References -- 8. Stochastic Models for Optimizing Immunization Strategies in Fixed-Income Security Portfolios under Some Sources of Uncertainty Larraitz Aranburu, Laureano F. Escudero, M. Araceli Garin and Gloria Perez -- 1 Introduction.

2 The deterministic LP optimization model -- 3 Two sources of uncertainty: interest rate and credit risk -- 4 Traditional two-stage stochastic optimization approach -- 5 Risk adverse models -- 5.1 Mean-risk immunization strategy -- 5.2 Value-at-Risk (VaR) strategy -- 5.3 Stochastic dominance strategy -- 6 Two-stage fixed-income security portfolio immunization based on the VaR strategy -- 7 A more accurate solution: a multistage stochastic scheme -- 7.1 Multistage maxmin immunization strategy -- 7.2 Multistage stochastic dominance strategy -- 7.3 Multistage VaR & stochastic dominance strategy -- 8 Case study -- 8.1 Deterministic LP model -- 8.2 Two-stage scheme -- 8.3 Two-stage stochastic model (DEM1) -- 8.4 Mean-risk immunization model (DEM-MR) -- 8.5 Two-stage maxmin immunization model (DEM2) -- 8.6 Two-stage VaR model (DEM3) -- 8.7 Multistage scheme -- 8.8 Multistage maxmin immunization model (DEM4) -- 8.9 Multistage stochastic dominance model (DEM5) -- 8.10 Multistage VaR & stochastic dominance model (DEM6) -- 9 Models comparison and conclusions -- References -- 9. Stochastic Programming and Optimization in Horserace Betting William T. Ziemba -- 1 Introduction -- 2 The importance of getting the mean right -- 3 The favorite-longshot bias -- 4 Place and show optimization with transactions costs -- 5 The place pick all -- 6 Some stochastic programming formulations -- 7 The Pick3 and Pick4: Theory of pricing the bets -- 8 ThePick4 -- 9 Example of Pick4 with embedded Pick3's and Doubles: Travers Day at Saratoga -- 10 The Pick6: Theory of pricing the bets -- 11 The one that got away: the hittable 2 million Pick 6 at the 2009 Breeders' Cup -- 12 Professional racetrack betting syndicates -- 13 Conclusion -- References -- Part II. Papers in Production Planning and Logistics.

10. Multi-Stage Stochastic Programming for Natural Gas Infrastructure Design with a Production Perspective Lars Hellemo, Kjetil Midthun, Asgeir Tomasgard and Adrian Werner -- 1 Introduction -- 2 Literature review -- 3 Uncertainty -- 3.1 Uncertainty in the context of our model -- 3.2 The multi-horizon scenario tree -- 4 Modelstructure -- 4.1 Time structure, investment and operational decisions -- 4.2 Modularity -- 5 Coremodel -- 5.1 Objective function -- 5.2 Investment constraints -- 5.3 Operational constraints -- 5.4 Objective function terms -- 6 Selected extension modules -- 6.1 Pressure dynamics -- 6.2 Reservoir modeling -- 6.3 Quality/multi-component flow modeling -- 7 Case study -- 8 Conclusions -- Acknowledgments -- References -- A Notation -- A.1 Sets and indices -- A.2 Decision variables -- A.3 Variables/functions -- A.4 Parameters/constants -- A.5 Profiles -- 11. A Stochastic Programming Model for Optimizing the Production of Farmed Atlantic Salmon Martin B. Hæreid, Peter Schutz and Asgeir Tomasgard -- 1 Introduction -- 2 Salmon Farming -- 2.1 Production in seawater -- 2.2 Uncertainty in production -- 2.2.1 Biomass development -- 2.2.2 Loss in production -- 2.2.3 Consequences of the uncertainty in biomass development -- 2.3 The Norwegian regulatory framework -- 3 The Market for Atlantic Salmon -- 3.1 Supply -- 3.2 Price -- 3.3 Other sources of uncertainty -- 4 Biomass Growth Model -- 5 Model Formulation -- 5.1 Model properties -- 5.2 Notation -- 5.3 Stochastic variables and time structure -- 5.4 Mathematical formulation -- 5.4.1 Objective function -- 5.4.2 Constraints -- 6 Conclusions -- References -- 12. Prioritizing Network Interdiction of Nuclear Smuggling Dennis P. Michalopoulos, David P. Morton and J. Wesley Barnes -- 1 Introduction -- 2 Computing detection probabilities -- 3 Prioritized bipartite stochastic network interdiction.

3.1 Model assumptions -- 3.2 Formulation -- 4 The value of prioritization -- 5 Improving the formulation of PrBiSNIP -- 5.1 Restricting the size of Kω -- 5.2 Threat scenario aggregation -- 5.3 Row generation algorithm -- 6 Computational experiments and results -- 6.1 Russian model -- 6.2 US model -- 6.3 Heuristic generation of granular priority lists -- 7 Conclusion -- Acknowledgements -- References -- 13. Saw mill Production Planning Under Uncertainty: Modelling and Solution Approaches Masoumeh Kazemi Zanjani, Mustapha Nourelfath and Daoud Ait-Kadi -- 1 Introduction -- 2 Production planning in the sawing units of sawmill -- 2.1 Sawing process in sawmills -- 2.2 A deterministic LP model for sawmill production planning -- 2.2.1 Notation -- 2.2.2 The deterministic LP model -- 3 Production planning in sawing units by taking into account the random yield -- 3.1 Modelling the random process yields -- 3.2 A two-stage stochastic model with recourse for sawmill production planning -- 3.3 Validation of the stochastic sawmill production planning model by Monte Carlo simulation -- 3.4 Application of the proposed stochastic production planning model for a prototype sawmill -- 3.4.1 Prototype sawmill -- 3.4.2 Comparison between the stochastic and deterministic sawmill production planning models -- 3.5 Taking into account service robustness in the stochastic sawmill production planning model -- 3.5.1 Robust optimization models for sawmill production planning -- 3.5.1.1 Notations -- 3.5.1.2 The robust optimization models -- 3.5.2 Application of robust optimization approach for the sawmill example -- 3.5.2.1 RO-(UPM-2) model results -- 3.5.2.2 RO-(UPV) model results -- 3.5.2.3 Performance comparison between RO-(UPM-2) and RO-UPV models -- 4 Production planning in sawing units by taking into account the random yield and random demand.

4.1 Modelling the uncertain yield and demand.