Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
Papers published on a yearly basis
Papers
More filters
•
08 Jan 2007TL;DR: In this article, the authors propose a simplex method for robust optimization in finance, using linear programming, nonlinear programming, and Quadratic programming, with the use of robust optimization tools.
Abstract: 1. Introduction 2. Linear programming: theory and algorithms 3. LP models: asset/liability cash flow matching 4. LP models: asset pricing and arbitrage 5. Nonlinear programming: theory and algorithms 6. NLP volatility estimation 7. Quadratic programming: theory and algorithms 8. QP models: portfolio optimization 9. Conic optimization tools 10. Conic optimization models in finance 11. Integer programming: theory and algorithms 12. IP models: constructing an index fund 13. Dynamic programming methods 14. DP models: option pricing 15. DP models: structuring asset backed securities 16. Stochastic programming: theory and algorithms 17. SP models: value-at-risk 18. SP models: asset/liability management 19. Robust optimization: theory and tools 20. Robust optimization models in finance Appendix A. Convexity Appendix B. Cones Appendix C. A probability primer Appendix D. The revised simplex method Bibliography Index.
261 citations
••
TL;DR: A heuristic based on tabu search, which takes into account the stochastic nature of this problem, is proposed, and some testing instances with different properties are established to investigate the algorithmic performance.
261 citations
••
TL;DR: In this article, an optimization-via-simulation algorithm, called COMPASS, was proposed for estimating the performance measure via a stochastic, discrete-event simulation, and the decision variables were integer ordered.
Abstract: We propose an optimization-via-simulation algorithm, called COMPASS, for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables are integer ordered. We prove that COMPASS converges to the set of local optimal solutions with probability 1 for both terminating and steady-state simulation, and for both fully constrained problems and partially constrained or unconstrained problems under mild conditions.
261 citations
••
TL;DR: In this article, an interval-parameter multi-stage stochastic linear programming (IMSLP) method has been developed for water resources decision making under uncertainty, where penalties are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised water allocation targets are violated.
261 citations
••
TL;DR: A supply chain design problem modeled as a sequence of splitting and combining processes, where the first-stage decisions are strategic location decisions, whereas the second stage consists of operational decisions.
260 citations