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 multistage fuzzy-stochastic programming (MFSP) model is developed for tackling uncertainties presented as fuzzy sets and probability distributions and a vertex analysis approach is proposed for solving multiple fuzzy sets in the MFSP model.
Abstract: In this study, a multistage fuzzy-stochastic programming (MFSP) model is developed for tackling uncertainties presented as fuzzy sets and probability distributions. A vertex analysis approach is proposed for solving multiple fuzzy sets in the MFSP model. Solutions under a set of @a-cut levels can be generated by solving a series of deterministic submodels. The developed method is applied to the planning of a case study for water-resources management. Dynamics and uncertainties of water availability (and thus water allocation and shortage) could be taken into account through generation of a set of representative scenarios within a multistage context. Moreover, 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. The modeling results can help to generate a range of alternatives under various system conditions, and thus help decision makers to identify desired water-resources management policies under uncertainty.
174 citations
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TL;DR: The CVRPSD can be formulated as a set partitioning problem and it is shown that the associated column generation subproblem can be solved using a dynamic programming scheme.
174 citations
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TL;DR: The simulated annealing (SA) approach, which is one of the leading stochastic search methods, is employed for specifying a large-scale linear regression model and the results are compared to the results of the more common stepwise regression (SWR) approach for model specification.
173 citations
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25 Jan 2017
TL;DR: In this article, the authors proposed to leverage day-ahead power market and time-of-use electricity, and uses stochastic programming to address the uncertainties in EV charging demand.
Abstract: Workplace electric vehicle (EV) charging is now supported by more and more companies to encourage EV adoption. In the meantime, renewable energies are becoming an important power source. To participate in the day-ahead power market, decisions have to be made before knowing the actual power demand. This paper addresses the challenges of energy scheduling in office buildings integrated with photovoltaic systems and workplace EV charging. It proposes to leverage day-ahead power market and time-of-use electricity, and uses stochastic programming to address the uncertainties in EV charging demand. Two computationally efficient control algorithms, stochastic programming and load forecasting for energy management with two stages (SPLET) and sample average approximation-based SPLET, are proposed. Both algorithms contain two stages: day-ahead scheduling and real-time operation. First, they try to find the amount of power to purchase from the day-ahead power market while leveraging the flexibility of the load. Then, the real-time demand is satisfied while incorporating the uncertainties realization. Case study based on real-world data shows the proposed two algorithms could provide 7.2% and 6.9% average cost reduction, respectively. Vehicle-to-building and stand-alone battery system can serve as countermeasures for the mismatch between the day-ahead scheduling and real-time demand to further reduce the operation cost.
172 citations
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TL;DR: A probabilistic bi-level linear multi-objective programming problem and its application in enterprise-wide supply chain planning problem where (1) market demand, (2) production capacity of each plant and (3) resource available to all plants for each product are random variables and the constraints may consist of joint probability distributions or not.
172 citations