Log in

Relevant bibliographies by topics / Scenario-Based Stochastic Programming

Contents

Journal articles

Academic literature on the topic 'Scenario-Based Stochastic Programming'

Author: Grafiati

Published: 12 September 2021

Last updated: 2 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Scenario-Based Stochastic Programming.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Scenario-Based Stochastic Programming"

1

Tarim, S. Armagan, Suresh Manandhar, and Toby Walsh. "Stochastic Constraint Programming: A Scenario-Based Approach." Constraints 11, no. 1 (2006): 53–80. http://dx.doi.org/10.1007/s10601-006-6849-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Golpîra, Hêriş. "A Scenario Based Stochastic Time-Cost-Quality Trade-Off model for Project Scheduling Problem." International Journal of Management Science and Business Administration 2, no. 5 (2014): 7–12. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.25.1001.

Full text

Abstract:

This paper formulates a new time–cost trade-off problem under some uncertainties for a multi-phase project. To do this, a new approach is proposed based on goal programming in compliance with scenario-based stochastic optimization formulation. To the best of our knowledge, this problem has not been extensively treated in the literature. Computational results show the applicability and usefulness of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

3

Li, He Peng, Chuan Zhi Zang, Peng Zeng, Hai Bin Yu, and Zhong Wen Li. "Scenario-Based Stochastic Programming Strategy for Microgrid Energy Scheduling Considering Uncertainties." Applied Mechanics and Materials 672-674 (October 2014): 1322–28. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1322.

Full text

Abstract:

The inherent random and intermittence of the renewable energy resources pose a huge challenge to the Microgrid (MG) energy management systems (EMS). In order to mitigate the effects of uncertainties, we propose a novel two-stage stochastic programming model for the energy scheduling optimization by considering the uncertainties in solar and wind generation, and the plug-in electric vehicles (EV). The random nature of uncertainty is characterized by a scenarios generation approach based on autoregressive moving average (ARMA) model according to probability density function of each random variable. By use of the strategy of scenarios simulation, the stochastic problem is decomposed into the deterministic equivalent problem. The firefly algorithm (FA) is used to solve the equivalent model. The effectiveness and robust of proposed stochastic energy scheduling optimization strategy for MG is valid by comparison with the simulation results of deterministic method.

APA, Harvard, Vancouver, ISO, and other styles

4

JI, XIAODONG, XIUJUAN ZHAO, and XIULI CHAO. "A NOVEL METHOD FOR MULTISTAGE SCENARIO GENERATION BASED ON CLUSTER ANALYSIS." International Journal of Information Technology & Decision Making 05, no. 03 (2006): 513–30. http://dx.doi.org/10.1142/s0219622006002106.

Full text

Abstract:

Based on cluster analysis, a novel method is introduced in this paper to generate multistage scenarios. A linear programming model is proposed to exclude the arbitrage opportunity by appending a scenario to the generated scenario set. By means of a cited stochastic linear goal programming portfolio model, a case is given to exhibit the virtues of this scenario generation approach.

APA, Harvard, Vancouver, ISO, and other styles

5

Chen, Zhi-Long, Shanling Li, and Devanath Tirupati. "A scenario-based stochastic programming approach for technology and capacity planning." Computers & Operations Research 29, no. 7 (2002): 781–806. http://dx.doi.org/10.1016/s0305-0548(00)00076-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Mitra, Sovan, and Tong Ji. "Optimisation of stochastic programming by hidden Markov modelling based scenario generation." International Journal of Mathematics in Operational Research 2, no. 4 (2010): 436. http://dx.doi.org/10.1504/ijmor.2010.033439.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Jayaraman, Raja, Cinzia Colapinto, Danilo Liuzzi, and Davide La Torre. "Planning sustainable development through a scenario-based stochastic goal programming model." Operational Research 17, no. 3 (2016): 789–805. http://dx.doi.org/10.1007/s12351-016-0239-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Li, Y. P., G. H. Huang, and X. Chen. "Multistage scenario-based interval-stochastic programming for planning water resources allocation." Stochastic Environmental Research and Risk Assessment 23, no. 6 (2008): 781–92. http://dx.doi.org/10.1007/s00477-008-0258-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Mohd Noh, Norshela, Arifah Bahar, and Zaitul Marlizawati Zainuddin. "Scenario Based Two-Stage Stochastic Programming Approach for the Midterm Production Planning of Oil Refinery." MATEMATIKA 34, no. 3 (2018): 45–55. http://dx.doi.org/10.11113/matematika.v34.n3.1138.

Full text

Abstract:

Recently, oil refining industry is facing with lower profit margin due to uncertainty. This causes oil refinery to include stochastic optimization in making a decision to maximize the profit. In the past, deterministic linear programming approach is widely used in oil refinery optimization problems. However, due to volatility and unpredictability of oil prices in the past ten years, deterministic model might not be able to predict the reality of the situation as it does not take into account the uncertainties thus, leads to non-optimal solution. Therefore, this study will develop two-stage stochastic linear programming for the midterm production planning of oil refinery to handle oil price volatility. Geometric Brownian motion (GBM) is used to describe uncertainties in crude oil price, petroleum product prices, and demand for petroleum products. This model generates the future realization of the price and demands with scenario tree based on the statistical specification of GBM using method of moment as input to the stochastic programming. The model developed in this paper was tested for Malaysia oil refinery data. The result of stochastic approach indicates that the model gives better prediction of profit margin.

APA, Harvard, Vancouver, ISO, and other styles

10

Papavasiliou, A., S. S. Oren, and R. P. O'Neill. "Reserve Requirements for Wind Power Integration: A Scenario-Based Stochastic Programming Framework." IEEE Transactions on Power Systems 26, no. 4 (2011): 2197–206. http://dx.doi.org/10.1109/tpwrs.2011.2121095.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!