Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
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TL;DR: In the proposed approach, optimal site, size, type, and time of distributed energy resources are determined along with optimal allocation of section switches to partitioning conventional distribution system into a number of interconnected MGs.
Abstract: This paper proposes a new stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids (MGs) under uncertainty from economic, technical, reliability and environmental viewpoints. In the proposed approach, optimal site, size, type, and time of distributed energy resources are determined along with optimal allocation of section switches to partitioning conventional distribution system into a number of interconnected MGs. The uncertainties of the problem are considered using scenario modelling and backward scenario reduction technique is implemented to deal with computational burden. In addition, three different risk averse, risk neutral and risk seeker strategies are defined for distribution network operator. The proposed framework is considered as two unparalleled objective functions which the first objective minimizes the investment cost, operation and maintenance cost, power loss cost and pollutants emission cost and the second objective is defined to minimize energy not supplied in both connected and islanded modes of MGs. Finally, multi objective particle swarm optimization is applied to minimize the proposed bi-objective functions and subsequently fuzzy satisfying method is accomplished to select the best solution proportional to risk based strategies. Efficiency of the proposed framework is validated on 85-bus distribution system and obtained results are presented and discussed.
97 citations
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TL;DR: In this paper, a multi-period inventory control problem is formulated as a stochastic programming model with recourse where demand during each pre-hurricane season period is represented as a convolution of the current period's demand and an updated estimate of demand for the ensuing hurricane season.
97 citations
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TL;DR: Eight recently developed stochastic global optimization algorithms representing controlled random search, simulated annealing, and clustering are used to solve global optimization problems from three different fields representing many-body potentials in physical chemistry, optimal control of a chemical reactor, and fitting a statistical model to empirical data.
Abstract: We describe global optimization problems from three different fields representing many-body potentials in physical chemistry, optimal control of a chemical reactor, and fitting a statistical model to empirical data. Historical background for each of the problems as well as the practical significance of the first two are given. The problems are solved by using eight recently developed stochastic global optimization algorithms representing controlled random search (4 algorithms), simulated annealing (2 algorithms), and clustering (2 algorithms). The results are discussed, and the importance of global optimization in each respective field is focused.
97 citations
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TL;DR: Monte Carlo methods utilizing a new network concept, Uniformly Directed Cutsets (UDCs), are presented for analyzing directed, acyclic networks with probabilistic arc durations, providing estimates for project completion time distributions, criticality indices, minimum time distributions and path optimality indices.
97 citations
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11 Feb 2008
TL;DR: FinnLIB: A Library of Financial Optimization Models is a library of financial Optimization models based on six models that were designed to solve the problem of portfolio optimization in the financial industry.
Abstract: Foreword. Preface. Acknowledgements. List of Models. Notation. I. Introduction. 1. An Optimization View of Financial Engineering. 2. Basics of Risk Management. II. Portfolio Optimization Models. 3. Mean-Variance Analysis. 4. Portfolio Models for Fixed Income. 5. Scenario Optimization. 6. Dynamic Portfolio Optimization with Stochastic Programming. 7. Index Funds. 8. Designing Financial Products. 9. Scenario Generation. III. Applications. 10. Application I: International Asset Allocation. 11. Application II: Corporate Bond Portfolios. 12. Application III: Insurance Policies with Guarantees. 13. Application IV: Personal Financial Planning. IV. Library of Financial Optimization Models. 14. FINLIB: A Library of Financial Optimization Models A. Basics of Optimization. B. Basics of Probability Theory. C. Stochastic Processes. Bibliography. Index.
97 citations