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: The developed FBISP method can deal with uncertainties expressed as probability distributions and fuzzy-boundary intervals and is useful for generating a range of decision alternatives under various system conditions and thus helping decision makers to identify desired water resources management policies under uncertainty.
98 citations
••
TL;DR: In this paper, a non-linear programming model for optimization of a forest biomass power plant supply chain was reformulated into a linear programming model and a two-stage stochastic programming model was proposed to balance the risk and profit.
98 citations
••
TL;DR: In this paper, the optimal resources allocation strategies for a canal command in a semiarid region of Indian Punjab are developed in a stochastic regime, considering the competition of the crops in a season, both for irrigation water and area of cultivation.
Abstract: Optimal resources allocation strategies for a canal command in the semiarid region of Indian Punjab are developed in a stochastic regime, considering the competition of the crops in a season, both for irrigation water and area of cultivation. The proposed strategies are divided into two modules using a multilevel approach. The first module determines the optimal seasonal allocation of water as well as optimal cropping pattern. This module is subdivided into two stages. The first stage is a single crop intraseasonal model that employs a stochastic dynamic programming algorithm. The stochastic variables are weekly canal releases and evapotranspiration of the crop that are fitted to different probability distribution functions to determine the expected values at various risk levels. The second stage is a deterministic dynamic programming model that takes into account the multicrop situation. An exponential seasonal crop-water production function is used in this stage. The second module is a single crop stochastic dynamic programming intraseasonal model that takes the output of the first module and gives the optimal weekly irrigation allocations for each crop by considering the stress sensitivity factors of crops.
98 citations
••
TL;DR: The problem of determining the optimal investment level that a firm should maintain in the presence of random price and/or demand fluctuations is considered, by means of a geometric Brownian motion, and general running payoff functions are considered.
Abstract: We consider the problem of determining the optimal investment level that a firm should maintain in the presence of random price and/or demand fluctuations. We model market uncertainty by means of a geometric Brownian motion, and we consider general running payoff functions. Our model allows for capacity expansion as well as for capacity reduction, with each of these actions being associated with proportional costs. The resulting optimization problem takes the form of a singular stochastic control problem that we solve explicitly. We illustrate our results by means of the so-called Cobb-Douglas production function. The problem that we study presents a model in which the associated Hamilton-Jacobi-Bellman equation admits a classical solution that conforms with the underlying economic intuition but does not necessarily identify with the corresponding value function, which may be identically equal to $\infty$. Thus, our model provides a situation that highlights the need for rigorous mathematical analysis when addressing stochastic optimization applications in finance and economics, as well as in other fields.
98 citations
••
TL;DR: In this study, a two-stage stochastic revenue-maximization model is presented to determine a long-term strategy under uncertainty for a large-scale real-world paper recycling company.
Abstract: Paper is an example of a valuable material that can be recycled and recovered. In this study, a two-stage stochastic revenue-maximization model is presented to determine a long-term strategy under uncertainty for a large-scale real-world paper recycling company. This network-design problem includes optimal recycling center locations and optimal flow amounts between the nodes in the multi-facility environment. The proposed model is formulated with two-stage stochastic mixed-integer and robust programming approaches. The models are solved by commercial software GAMS 21.6/CPLEX 9.0 and the results are compared. The study is followed by the analyses of the results.
98 citations