Search Results - "Approximate Stochastic Dynamic Programming"

Two simplex-based approximate stochastic dynamic programming schemes for a real hydropower management problem

Authors: Luckny Zephyr Bernard F. Lamond Kenjy Demeester et al.

Source: Annals of Operations Research. 351:333-364

Randomized Simplicial Approximate Stochastic Dynamic Programming for Mid-term Reservoir Optimization

Authors: Luckny Zephyr Bernard F. Lamond

Source: 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT). :468-474

Subject Terms: 0211 other engineering and technologies, 02 engineering and technology

Quantitative Analysis on Resilience of Distributed Energy Systems with Approximate Stochastic Dynamic Programming Models Considering Disaster Predictability / 災害の予測可能性を考慮した近似確率動的計画モデルによる分散型エネルギーシステムのレジリエンスに関する定量的分析

Authors: Kanta Nishikura Ryoichi Komiyama Yasumasa Fujii et al.

Source: エネルギー・資源学会論文誌 / Journal of Japan Society of Energy and Resources. 2023, 44(2):74

Hybrid Simplicial-Randomized Approximate Stochastic Dynamic Programming for Multireservoir Optimization

Authors: Luckny Zephyr Bernard F. Lamond Pascal Lang

Source: SSRN Electronic Journal.

Approximate stochastic dynamic programming for hydroelectric production planning

Authors: Bernard F. Lamond Pascal Côté Luckny Zéphyr et al.

Source: European Journal of Operational Research. 262:586-601

Subject Terms: 0211 other engineering and technologies, 0207 environmental engineering, 02 engineering and technology, 7. Clean energy

Access URL: https://corpus.ulaval.ca/jspui/bitstream/20.500.11794/13667/1/Zephyr_Lang_Lamond_cote_ASDP_2015.pdf

Approximate Stochastic Dynamic Programming for Sensor Scheduling to Track Multiple Targets.

Contributors: Krakow, L.

Source: Conference: Proposed for presentation at the 5th Workshop on Defence Applications of Signal Processing held December 10-14, 2006 in Fraser Island, Queensland, Australia.

File Description: Medium: ED; Size: 5 p.

Access URL: http://www.osti.gov/scitech/servlets/purl/1264003

Two simplex-based approximate stochastic dynamic programming schemes for a real hydropower management problem.

Authors: Zephyr, Luckny Lamond, Bernard F. Demeester, Kenjy et al.

Source: Annals of Operations Research; Aug2025, Vol. 351 Issue 1, p333-364, 32p

Subject Terms: WATER power, STOCHASTIC programming, RESOURCE allocation, SIMPLEX algorithm, REINFORCEMENT learning, MONTE Carlo method, ELECTRIC power production, WATER management

Approximate stochastic dynamic programming for sensor scheduling to track multiple targets

Authors: Lucas W. Krakow Y. Li Edwin K. P. Chong et al.

Source: Digital Signal Processing. 19:978-989

Subject Terms: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://www.osti.gov/biblio/1264003
https://dlnext.acm.org/doi/10.1016/j.dsp.2007.05.004
http://www.sciencedirect.com/science/article/pii/S1051200407000905
https://www.sciencedirect.com/science/article/pii/S1051200407000905
https://dblp.uni-trier.de/db/journals/dsp/dsp19.html#LiKCG09
https://dl.acm.org/doi/10.1016/j.dsp.2007.05.004

Approximate stochastic dynamic programming for hydroelectric production planning

Authors: Zéphyr, Luckny Lang, Pascal Lamond, Bernard et al.

Index Terms: Dynamic programming, Curse of dimensionality, Dynamic decision process, Value function, Simplicial state space partitioning, Programmation dynamique, Programmation stochastique, Hydroélectricité, Réservoirs (Lacs), Mathématiques, info:eu-repo/semantics/article, info:eu-repo/semantics/acceptedVersion

URL: http://hdl.handle.net/20.500.11794/13667
https://doi.org/10.1016/j.ejor.2017.03.050
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ejor.2017.03.050
info:eu-repo/semantics/altIdentifier/issn/0377-2217
https://doi.org/10.1016/j.ejor.2017.03.050
European journal of operational research, (2017)

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization.

Authors: Zephyr, Luckny Lamond, Bernard F. Lang, Pascal

Source: Computational Management Science; 5/10/2024, Vol. 21 Issue 1, p1-44, 44p

Subject Terms: STOCHASTIC programming, DYNAMIC programming, PIECEWISE linear approximation, VALUATION of real property

Approximate stochastic dynamic programming for opportunistic fair scheduling in wireless networks

Authors: Zhi Zhang Edwin K. P. Chong S. Moola

Source: 2008 47th IEEE Conference on Decision and Control.

Subject Terms: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://dblp.uni-trier.de/db/conf/cdc/cdc2008.html#ZhangMC08
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000004739159

An approximate dynamic programming approach for multi-stage stochastic lot-sizing under a Decision-Hazard-Decision information structure

Authors: Spitzer, Victor Gicquel, Céline Gurevsky, Evgeny et al.

Contributors: Spitzer, Victor Gicquel, Céline Gurevsky, Evgeny et al.

Source: https://hal.science/hal-04987947 ; 2025.

Subject Terms: Lot-sizing, Multi-stage stochastic integer programming, Decision-Hazard-Decision information structure, Approximate stochastic dynamic programming, Two-stage stochastic integer programming, Benders decomposition, [INFO.INFO-RO]Computer Science [cs]/Operations Research [math.OC]

Availability: https://hal.science/hal-04987947
https://hal.science/hal-04987947v3/document
https://hal.science/hal-04987947v3/file/Spitzer_DAM_2025_v2_clean.pdf

An approximate dynamic programming approach for multi-stage stochastic lot-sizing under a Decision–Hazard–Decision information structure.

Authors: Spitzer, Victor Gicquel, Céline Gurevsky, Evgeny et al.

Source: Discrete Applied Mathematics. Jan2026, Vol. 379, p355-378. 24p.

Approximate stochastic dynamic programming for hydroelectric production planning

Authors: Zéphyr, Luckny Lang, Pascal Lamond, Bernard F. et al.

Relation: http://www.sciencedirect.com/science/article/pii/S0377221717302722

Availability: http://www.sciencedirect.com/science/article/pii/S0377221717302722

Novel Artificial Intelligence Applications in Energy: A Systematic Review.

Authors: Zhang, Tai Strbac, Goran

Source: Energies (19961073); Jul2025, Vol. 18 Issue 14, p3747, 51p

Subject Terms: ARTIFICIAL intelligence, REINFORCEMENT learning, ENERGY consumption, MULTIAGENT systems, ENERGY infrastructure

Interior Point Algorithm in Multi-objective Portfolio Optimization: GlueVaR Approach. (English)

Authors: Goharnia, Elaheh Mansourfar, Gholamreza Biglari, Fahimeh

Source: Financial Research Journal (FRJ); Fall2023, Vol. 25 Issue 3, p453-484, 34p

Subject Terms: OPTIMIZATION algorithms, PORTFOLIO management (Investments), INVESTORS, VALUE at risk, QUADRATIC programming

The use of simple calibrations of the individual locations in making transhipment decisions in a multi-location inventory network

Authors: Archibald, T W Black, D Glazebrook, K D

Index Terms: Journal Article, PeerReviewed

URL: https://eprints.lancs.ac.uk/id/eprint/45361/
https://eprints.lancs.ac.uk/id/eprint/45361

Computationally efficient evolutionary optimisation for joint reservoir design-operations.

Authors: Motevali Bashi Naeini, Elaheh Soltaninia, Shahrokh

Source: Proceedings of ICE: Water Management; Jun2023, Vol. 176 Issue 3, p132-147, 16p

Subject Terms: PARTICLE swarm optimization, LINEAR programming, RESERVOIRS, WATER supply

The use of simple calibrations of individual locations in making transshipment decisions in a multi-location inventory network.

Authors: Archibald, T. W. Black, D. P. Glazebrook, K. D.

Source: Journal of the Operational Research Society; Feb2010, Vol. 61 Issue 2, p294-305, 12p

Subject Terms: INVENTORY control, SUPPLY & demand, DYNAMIC programming, STOCHASTIC processes, MATHEMATICAL models of decision making, DECISION making in industrial management, HEURISTIC, MANAGEMENT

Planning and Scheduling: Dynamic Assignment and Scheduling with Contingencies

Authors: ALPHATECH INC BURLINGTON MA Castanon, David A. Logan, David A. et al.

Source: DTIC AND NTIS

Index Terms: Operations Research, DYNAMIC PROGRAMMING, AIR FORCE PLANNING, AIR FORCE OPERATIONS, ALGORITHMS, STOCHASTIC PROCESSES, DECISION MAKING, REAL TIME, SCHEDULING, HEURISTIC METHODS., Text

URL: https://apps.dtic.mil/docs/citations/ADA342494