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
••
TL;DR: A multiperiod stochastic linear programming model ALM is developed that includes the essential institutional, legal, financial, and bank-related policy considerations, and their uncertainties, yet is computationally tractable for realistically sized problems and generates superior policies.
Abstract: In managing its assets and liabilities in light of uncertainties in cash flows, cost of funds and return on investments, a bank must determine its optimal trade-off between risk, return and liquidity. In this paper we develop a multiperiod stochastic linear programming model ALM that includes the essential institutional, legal, financial, and bank-related policy considerations, and their uncertainties, yet is computationally tractable for realistically sized problems. A version of the model was developed for the Vancouver City Savings Credit Union for a 5-year planning period. The results indicate that ALM is theoretically and operationally superior to a corresponding deterministic linear programming model, and that the effort required for the implementation of ALM, and its computational requirements, are comparable to those of the deterministic model. Moreover, the qualitative and quantitative characteristics of the solutions are sensitive to the model's stochastic elements, such as the asymmetry of cash flow distributions. We also compare ALM with the stochastic decision tree SDT model developed by S. P. Bradley and D. B. Crane. ALM is computationally more tractable on realistically sized problems than SDT, and simulation results indicate that ALM generates superior policies.
324 citations
••
TL;DR: The model is based on an inexact chance-constrained programming method, which improves upon the existing inexact and stochastic programming approaches by allowing both distribution information in B and uncertainties in A and C to be effectively incorporated within its optimization process.
324 citations
••
01 Dec 1986TL;DR: This paper gives a short survey of Monte Carlo algorithms for stochastic optimization, with emphasis on the analysis of convergence rate.
Abstract: This paper gives a short survey of Monte Carlo algorithms for stochastic optimization. Both discrete and continuous parameter stochastic optimization are discussed, with emphasis on the analysis of convergence rate. Some future research directions for the area are also indicated.
323 citations
•
04 Dec 2013TL;DR: This paper presents a meta-modelling framework called GAMS Codes, which automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and automating the various stages of stochastic production.
Abstract: Introduction.- Renewable Energy Sources - Modeling and Forecasting.- Clearing the Day-Ahead Market with a High Penetration of Stochastic Production.- Balancing Markets.- Managing Uncertainty with Flexibility.- Impact of Stochastic Renewable Energy Generation on Market Quantities.- Trading Stochastic Production in Electricity Pools.- Virtual Power Plants.- Facilitating Renewable Integration by Demand Response.- Random Variables and Stochastic Processes.- Basics of Optimization.- Introduction to Stochastic Programming.- Introduction to Robust Optimization.- GAMS Codes.
323 citations
••
TL;DR: The present paper is intended to review the existing literature on multi-objective combinatorial optimization (MOCO) problems and examines various classical combinatorials problems in a multi-criteria framework.
Abstract: In the last 20 years many multi-objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real-world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply combining discrete programming methods and multi-objective programming methods.
The present paper is intended to review the existing literature on multi-objective combinatorial optimization (MOCO) problems. Various classical combinatorial problems are examined in a multi-criteria framework. Some conclusions are drawn and directions for future research are suggested.
320 citations