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: A new metric is presented for evaluating supply chain design and planning projects in which there are significant elements of uncertainty and thus risk and an effective polytope integration method for evaluation of expected values and variances of revenue is adopted.
139 citations
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TL;DR: A multi-objective optimization model for the combined gas and electricity network planning is presented, wherein the Elitist Non-dominated Sorting Genetic Algorithm II is employed to capture the optimal Pareto front and the Primal–Dual Interior-Point (PDIP) method combined with the point-estimate method is adopted to evaluate the objective functions.
139 citations
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TL;DR: A model for optimal DES design under uncertainty is presented and is formulated as a Two-stage Stochastic Mixed-Integer Linear Program that contrasts in terms of technology selection and energy consumption shares among fossil fuels, grid electricity and renewable energy.
139 citations
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TL;DR: This work considers the problem of optimally meeting a stochastically growing demand for capacity over an infinite horizon and shows that this stochastic problem can be transformed into an equivalent deterministic problem.
Abstract: We consider the problem of optimally meeting a stochastically growing demand for capacity over an infinite horizon. Under the assumption that demand for product follows either a nonlinear Brownian motion or a non-Markovian birth and death process, we show that this stochastic problem can be transformed into an equivalent deterministic problem. Consistent with earlier work by A. Manne, the equivalent problem is formed by replacing the stochastic demand by its deterministic trend and discounting all costs by a new interest rate that is smaller than the original, in approximate proportion to the uncertainty in the demand.
139 citations
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TL;DR: In this paper, a model of the foundations versus flexibility trade-off enables the competing options to be optimized by balancing the expected profits that may arise from future expansion, and the cost of enhancing the foundation.
Abstract: Infrastructure facilities are generally heavy, fixed, and normally irreversible once construction has been completed. As existing facilities, they may confront economic competition of an increased space demand and the need for future expansion. Due to economic-based irreversibility, the expansion of a constructed facility requires the foundation and, to a lesser degree, columns to be enhanced and options for expansion to be accounted for at the very beginning of construction. Enhancing the foundation and columns represents an up-front cost, but has a return in flexibility for future expansion. This trade-off can be viewed as an investment problem, in that a premium has to be paid first for an option that can be exercised later. A model of the foundations versus flexibility trade-off enables the competing options to be optimized by balancing the expected profits that may arise from future expansion, i.e., the value of flexibility, and the cost of enhancing the foundation. Use of the model is demonstrated for the construction of a public parking garage, with the optimal foundation size determined. The evolution of parking demand is modeled with a trinomial lattice. Stochastic dynamic programming is used to determine the optimal expansion process. A model that does not consider the value of flexibility is compared with two value-flexible models. The value of flexibility in this case study is so significant that failure to account for flexibility is not economical. Valuation modeling such as discounted cash flow analysis with uncertainty modeling is important to capitalize on the worth of flexibility.
139 citations