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

We present an approximate stochastic dynamic programming methodology for a real-world hydropower management problem, in which water must be released from reservoirs to produce electricity to power aluminum smelters over a planning horizon of a year (three-day time step). In each period, decisions ar...

Full description

Saved in:

Bibliographic Details
Published in:	Annals of operations research Vol. 351; no. 1; pp. 333 - 364
Main Authors:	Zephyr, Luckny, Lamond, Bernard F., Demeester, Kenjy, Latraverse, Marco
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.08.2025 Springer Nature B.V
Subjects:	Algorithms Apexes Approximation Artificial intelligence Business and Management Combinatorics Decomposition Dynamic programming Electricity Errors Hydroelectric power Hydrology Lower bounds Monte Carlo simulation Operations Research/Decision Theory Original Research Reservoirs Smelters Theory of Computation Water Water shortages Canada Approximate stochastic dynamic programming Simplex Stochastic dynamic programming Hydropower Monte Carlo simulation Data envelopment analysis
ISSN:	0254-5330, 1572-9338
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	We present an approximate stochastic dynamic programming methodology for a real-world hydropower management problem, in which water must be released from reservoirs to produce electricity to power aluminum smelters over a planning horizon of a year (three-day time step). In each period, decisions are constrained by limits on the releases and the level of the four reservoirs, among others. The approach is a revisit of our previous work on simplicial approximate stochastic dynamic programming, in which the so-called cost-to-go or value functions are approximated over grid points chosen as vertices of simplices. The latter are constructed by first partitioning the reservoir level space into simplices and then iteratively subdividing existing simplices until a desired approximation error or a fixed number of grid points is reached. For each simplex, the approximation error is given by the difference between an upper and a lower bound. This scheme requires storing the list of created simplices in memory. In each iteration, the list is searched to find the existing simplex with the highest approximation error. This may be time-consuming as the number of existing simplices may be very large. In the new proposal, we avoid creating a long list of simplices by combining the original simplicial scheme with Monte Carlo simulation, similar to an exploration strategy in reinforcement learning. We benchmark the new method against its ancestor and an internal software package developed and used by an industrial partner, based on operational metrics and the concept of super-efficiency in data envelopment analysis. The Monte Carlo simplex-based scheme (the new method) outperforms the former method on all metrics considered. In addition, we compare the computational efficiency of both methods for different grid sizes. The average CPU time (over 15 replications) of the Monte Carlo simplicial approach varies between 78% and 98% of that of the simplicial method. As the grid sizes increase above 3,000 points, the simplicial method becomes intractable, in contrast to the Monte Carlo version, which confirms the advantage of the latter. Lastly, to further justify the Monte Carlo simplicial method, we create an artificial system by duplicating each component of the original system. In contrast to the new proposal, under the simplicial approach, the problem is tractable only for relatively modest size grids (up to 1,500 points), for which the average CPU time under the Monte Carlo approach varies between 2% and 5% of that of its simplicial counterpart.
AbstractList	We present an approximate stochastic dynamic programming methodology for a real-world hydropower management problem, in which water must be released from reservoirs to produce electricity to power aluminum smelters over a planning horizon of a year (three-day time step). In each period, decisions are constrained by limits on the releases and the level of the four reservoirs, among others. The approach is a revisit of our previous work on simplicial approximate stochastic dynamic programming, in which the so-called cost-to-go or value functions are approximated over grid points chosen as vertices of simplices. The latter are constructed by first partitioning the reservoir level space into simplices and then iteratively subdividing existing simplices until a desired approximation error or a fixed number of grid points is reached. For each simplex, the approximation error is given by the difference between an upper and a lower bound. This scheme requires storing the list of created simplices in memory. In each iteration, the list is searched to find the existing simplex with the highest approximation error. This may be time-consuming as the number of existing simplices may be very large. In the new proposal, we avoid creating a long list of simplices by combining the original simplicial scheme with Monte Carlo simulation, similar to an exploration strategy in reinforcement learning. We benchmark the new method against its ancestor and an internal software package developed and used by an industrial partner, based on operational metrics and the concept of super-efficiency in data envelopment analysis. The Monte Carlo simplex-based scheme (the new method) outperforms the former method on all metrics considered. In addition, we compare the computational efficiency of both methods for different grid sizes. The average CPU time (over 15 replications) of the Monte Carlo simplicial approach varies between 78% and 98% of that of the simplicial method. As the grid sizes increase above 3,000 points, the simplicial method becomes intractable, in contrast to the Monte Carlo version, which confirms the advantage of the latter. Lastly, to further justify the Monte Carlo simplicial method, we create an artificial system by duplicating each component of the original system. In contrast to the new proposal, under the simplicial approach, the problem is tractable only for relatively modest size grids (up to 1,500 points), for which the average CPU time under the Monte Carlo approach varies between 2% and 5% of that of its simplicial counterpart.
Author	Demeester, Kenjy Latraverse, Marco Zephyr, Luckny Lamond, Bernard F.
Author_xml	– sequence: 1 givenname: Luckny orcidid: 0000-0002-7611-5192 surname: Zephyr fullname: Zephyr, Luckny email: lzephyr@laurentian.ca organization: School of Business Administration/Barthi School of Engineering and Computer Science, Laurentian University – sequence: 2 givenname: Bernard F. surname: Lamond fullname: Lamond, Bernard F. organization: Département d’opérations et système de décisions, Faculté des sciences de l’administration, Université Laval – sequence: 3 givenname: Kenjy surname: Demeester fullname: Demeester, Kenjy organization: Technical Services, Rio Tinto – sequence: 4 givenname: Marco surname: Latraverse fullname: Latraverse, Marco organization: Technical Services, Rio Tinto
BookMark	eNp9UM1LwzAcDTLBTf0HPAU8R5OmSdqjDL9g4GWeQ5b-2nU0TU06tv33Zlbw5ukd3hfvLdCs9z0gdMfoA6NUPUZGc1USmglCpZCM5BdozoTKSMl5MUPzxOREcE6v0CLGHaWUsULMkVsfPI6tGzo4ko2JUGEzDMEfW2dGwHH0dmvi2FpcnXrjEiayCca5tm9wtFtwEHHtAzY4gOnw9lQFP_gDBOxMb5rE9-PZtOnA3aDL2nQRbn_xGn2-PK-Xb2T18fq-fFoRm6lsJLmAoiwp57VVAEYyU5a1kKYAClQqkRe1gETZqrAmkxuq0nhlQQoquc2BX6P7KTf1fu0hjnrn96FPlZpnXLBMKiaSKptUNvgYA9R6CGl2OGlG9flWPd2q03n651adJxOfTDGJ-wbCX_Q_rm9zzH7T
Cites_doi	10.1007/s11269-020-02738-7 10.1029/95WR02172 10.1029/90WR02032 10.1007/s11269-014-0610-6 10.1016/0142-0615(89)90025-2 10.1007/BF01582895 10.1287/opre.41.3.484 10.1007/s10287-021-00387-8 10.1016/j.ejor.2011.10.056 10.1016/j.ejor.2017.03.050 10.1007/s10287-024-00517-y 10.1007/s10479-011-0973-5 10.1016/j.cor.2020.105032 10.1007/s12667-011-0024-y 10.1287/opre.47.1.38 10.1016/j.advwatres.2017.08.015 10.1016/j.cageo.2010.03.022 10.1016/j.ejor.2016.11.049 10.1145/3544489 10.1023/A:1017992615625 10.1016/j.jhydrol.2018.08.050 10.1007/s10479-019-03446-1 10.1061/(ASCE)WR.1943-5452.0001050 10.1016/j.ejor.2016.11.028 10.1029/WR021i006p00779 10.1007/s11081-008-9045-3 10.1007/s10287-015-0242-1 10.3390/w12071898 10.1287/mnsc.39.10.1261 10.1029/WR024i008p01345 10.1016/j.cam.2015.04.048 10.1139/cjce-2013-0370 10.1007/s11269-017-1893-1 10.1061/(ASCE)0733-9496(2004)130:2(93) 10.1002/9781118029176 10.3390/en14030625 10.1061/(ASCE)0733-9496(2007)133:1(4) 10.1061/(ASCE)IR.1943-4774.0001063 10.1002/hyp.5674 10.1016/j.eap.2019.01.005 10.1007/978-3-319-20430-7_5 10.1016/j.ejor.2018.08.001 10.1016/j.ijepes.2022.108319 10.1007/s00186-012-0406-5 10.3390/w10030340 10.1007/s00170-022-09864-z 10.1016/S0377-2217(98)00130-1 10.1016/j.ijepes.2019.105469 10.1007/s11269-010-9612-1 10.1029/WR026i003p00447
ContentType	Journal Article
Copyright	The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Copyright_xml	– notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
DBID	AAYXX CITATION 7TA 7TB 8FD FR3 JG9 JQ2 KR7
DOI	10.1007/s10479-025-06561-4
DatabaseName	CrossRef Materials Business File Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database Materials Research Database ProQuest Computer Science Collection Civil Engineering Abstracts
DatabaseTitle	CrossRef Materials Research Database Civil Engineering Abstracts Technology Research Database Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Engineering Research Database Materials Business File
DatabaseTitleList	Materials Research Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science Business
EISSN	1572-9338
EndPage	364
ExternalDocumentID	10_1007_s10479_025_06561_4
GeographicLocations	Canada
GeographicLocations_xml	– name: Canada
GroupedDBID	-Y2 -~C -~X .4S .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 2.D 203 23M 28- 2J2 2JN 2JY 2KG 2KM 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 7WY 88I 8AO 8FE 8FG 8FL 8TC 8VB 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBRH ABBXA ABDBE ABDZT ABECU ABFSG ABFTV ABHLI ABHQN ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABRTQ ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACGOD ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSTC ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AEZWR AFBBN AFDZB AFEXP AFGCZ AFHIU AFKRA AFLOW AFOHR AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHQJS AHSBF AHWEU AHYZX AIAKS AIGIU AIIXL AILAN AITGF AIXLP AJBLW AJRNO AJZVZ AKVCP ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMVHM AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG ATHPR AVWKF AXYYD AYFIA AYQZM AZFZN AZQEC B-. BA0 BAPOH BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DWQXO EBLON EBO EBS EBU EDO EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ7 GQ8 GROUPED_ABI_INFORM_RESEARCH GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO ICD IEA IHE IJ- IKXTQ ITC ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K1G K60 K6V K6~ K7- KDC KOV KOW L6V LAK LLZTM M0C M2P M4Y M7S MA- N2Q N95 N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I OAM OVD P19 P2P P62 P9M PF0 PHGZM PHGZT PQBIZ PQBZA PQGLB PQQKQ PROAC PT4 PT5 PTHSS Q2X QOK QOS QWB R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZD RZK S16 S1Z S26 S27 S28 S3B SAP SBE SCF SCLPG SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TH9 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZL0 ZMTXR ZYFGU ~8M ~A9 ~EX AAYXX AFFHD CITATION 7TA 7TB 8FD FR3 JG9 JQ2 KR7
ID	FETCH-LOGICAL-c272t-45e899033fc7eea61a99f56a8e0e067548f5ec7ecd8ca26b074797ce65063c4e3
IEDL.DBID	RSV
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001462498100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0254-5330
IngestDate	Wed Nov 05 14:49:02 EST 2025 Sat Nov 29 07:33:36 EST 2025 Fri Aug 01 03:41:21 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Keywords	Approximate stochastic dynamic programming Simplex Stochastic dynamic programming Hydropower Monte Carlo simulation Data envelopment analysis
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c272t-45e899033fc7eea61a99f56a8e0e067548f5ec7ecd8ca26b074797ce65063c4e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-7611-5192
PQID	3235126715
PQPubID	25585
PageCount	32
ParticipantIDs	proquest_journals_3235126715 crossref_primary_10_1007_s10479_025_06561_4 springer_journals_10_1007_s10479_025_06561_4
PublicationCentury	2000
PublicationDate	20250800 2025-08-00 20250801
PublicationDateYYYYMMDD	2025-08-01
PublicationDate_xml	– month: 8 year: 2025 text: 20250800
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York
PublicationTitle	Annals of operations research
PublicationTitleAbbrev	Ann Oper Res
PublicationYear	2025
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	MV Pereira (6561_CR38) 1991; 52 6561_CR44 JL Morillo (6561_CR32) 2022; 142 A Al-Anazi (6561_CR1) 2010; 36 D Zhang (6561_CR53) 2018; 565 J Kelman (6561_CR25) 1990; 26 VC Chen (6561_CR9) 1999; 47 6561_CR49 F-J Chang (6561_CR7) 2005; 19 Q Desreumaux (6561_CR14) 2014; 41 A Magnani (6561_CR30) 2009; 10 X-B Li (6561_CR28) 1999; 115 T Cuvelier (6561_CR11) 2018; 32 R Munos (6561_CR33) 2002; 49 P Andersen (6561_CR2) 1993; 39 MV Pereira (6561_CR37) 1985; 21 G Halkos (6561_CR21) 2019; 62 C-M Ji (6561_CR23) 2014; 28 WB Powell (6561_CR40) 2011 6561_CR15 JA Tejada-Guibert (6561_CR45) 1995; 31 L Raso (6561_CR41) 2017; 143 G Uysal (6561_CR47) 2018; 10 P Côté (6561_CR10) 2019; 145 C Audet (6561_CR3) 2022; 48 L Zéphyr (6561_CR52) 2017; 262 MV Pereira (6561_CR36) 1989; 11 T Homem-de-Mello (6561_CR22) 2011; 2 M Bandarra (6561_CR4) 2021; 18 Y Cai (6561_CR6) 2013; 77 SA Johnson (6561_CR24) 1993; 41 6561_CR20 JF Bonnans (6561_CR5) 2012; 200 H-I Eum (6561_CR17) 2010; 24 JW Labadie (6561_CR27) 2004; 130 E Foufoula-Georgiou (6561_CR18) 1991; 27 X Tian (6561_CR46) 2017; 109 AF da Silva (6561_CR12) 2020; 287 E Foufoula-Georgiou (6561_CR19) 1988; 24 L Zéphyr (6561_CR51) 2015; 12 JL Morillo (6561_CR31) 2020; 115 6561_CR35 AB Philpott (6561_CR39) 2012; 218 Y-O Kim (6561_CR26) 2007; 133 S Séguin (6561_CR43) 2017; 259 AP dos Santos Rubem (6561_CR16) 2017; 260 KV SeethaRam (6561_CR42) 2021; 35 N Löhndorf (6561_CR29) 2019; 273 L Zephyr (6561_CR50) 2024; 21 Y Chen (6561_CR8) 2020; 124 X Wu (6561_CR48) 2021; 14 N Myo Lin (6561_CR34) 2020; 12 VL De Matos (6561_CR13) 2015; 290
References_xml	– volume: 35 start-page: 353 issue: 1 year: 2021 ident: 6561_CR42 publication-title: Water Resources Management doi: 10.1007/s11269-020-02738-7 – volume: 31 start-page: 2571 issue: 10 year: 1995 ident: 6561_CR45 publication-title: Water Resources Research doi: 10.1029/95WR02172 – volume: 27 start-page: 31 issue: 1 year: 1991 ident: 6561_CR18 publication-title: Water Resources Research doi: 10.1029/90WR02032 – volume: 28 start-page: 2435 year: 2014 ident: 6561_CR23 publication-title: Water Resources Management doi: 10.1007/s11269-014-0610-6 – volume: 11 start-page: 161 issue: 3 year: 1989 ident: 6561_CR36 publication-title: International Journal of Electrical Power & Energy Systems doi: 10.1016/0142-0615(89)90025-2 – volume: 52 start-page: 359 year: 1991 ident: 6561_CR38 publication-title: Mathematical Programming doi: 10.1007/BF01582895 – volume: 41 start-page: 484 issue: 3 year: 1993 ident: 6561_CR24 publication-title: Operations Research doi: 10.1287/opre.41.3.484 – volume: 18 start-page: 125 year: 2021 ident: 6561_CR4 publication-title: Computational Management Science doi: 10.1007/s10287-021-00387-8 – volume: 218 start-page: 470 issue: 2 year: 2012 ident: 6561_CR39 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2011.10.056 – volume: 262 start-page: 586 issue: 2 year: 2017 ident: 6561_CR52 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2017.03.050 – volume: 21 start-page: 1 issue: 1 year: 2024 ident: 6561_CR50 publication-title: Computational Management Science doi: 10.1007/s10287-024-00517-y – volume: 200 start-page: 199 year: 2012 ident: 6561_CR5 publication-title: Annals of Operations Research doi: 10.1007/s10479-011-0973-5 – volume: 124 year: 2020 ident: 6561_CR8 publication-title: Computers & Operations Research doi: 10.1016/j.cor.2020.105032 – volume: 2 start-page: 1 issue: 1 year: 2011 ident: 6561_CR22 publication-title: Energy Systems doi: 10.1007/s12667-011-0024-y – volume: 47 start-page: 38 issue: 1 year: 1999 ident: 6561_CR9 publication-title: Operations Research doi: 10.1287/opre.47.1.38 – volume: 109 start-page: 58 year: 2017 ident: 6561_CR46 publication-title: Advances in Water Resources doi: 10.1016/j.advwatres.2017.08.015 – volume: 36 start-page: 1494 issue: 12 year: 2010 ident: 6561_CR1 publication-title: Computers & Geosciences doi: 10.1016/j.cageo.2010.03.022 – volume: 260 start-page: 134 issue: 1 year: 2017 ident: 6561_CR16 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2016.11.049 – volume: 48 start-page: 1 issue: 3 year: 2022 ident: 6561_CR3 publication-title: ACM Transactions on Mathematical Software (TOMS) doi: 10.1145/3544489 – ident: 6561_CR35 – volume: 49 start-page: 291 year: 2002 ident: 6561_CR33 publication-title: Machine Learning doi: 10.1023/A:1017992615625 – volume: 565 start-page: 720 year: 2018 ident: 6561_CR53 publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2018.08.050 – volume: 287 start-page: 127 year: 2020 ident: 6561_CR12 publication-title: Annals of Operations Research doi: 10.1007/s10479-019-03446-1 – volume: 145 start-page: 05019005 issue: 4 year: 2019 ident: 6561_CR10 publication-title: Journal of Water Resources Planning and Management doi: 10.1061/(ASCE)WR.1943-5452.0001050 – volume: 259 start-page: 1156 issue: 3 year: 2017 ident: 6561_CR43 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2016.11.028 – volume: 21 start-page: 779 issue: 6 year: 1985 ident: 6561_CR37 publication-title: Water Resources Research doi: 10.1029/WR021i006p00779 – volume: 10 start-page: 1 year: 2009 ident: 6561_CR30 publication-title: Optimization and Engineering doi: 10.1007/s11081-008-9045-3 – volume: 12 start-page: 539 year: 2015 ident: 6561_CR51 publication-title: Computational Management Science doi: 10.1007/s10287-015-0242-1 – volume: 12 start-page: 1898 issue: 7 year: 2020 ident: 6561_CR34 publication-title: Water doi: 10.3390/w12071898 – volume: 39 start-page: 1261 issue: 10 year: 1993 ident: 6561_CR2 publication-title: Management Science doi: 10.1287/mnsc.39.10.1261 – volume: 24 start-page: 1345 issue: 8 year: 1988 ident: 6561_CR19 publication-title: Water Resources Research doi: 10.1029/WR024i008p01345 – volume: 290 start-page: 196 year: 2015 ident: 6561_CR13 publication-title: Journal of Computational and Applied Mathematics doi: 10.1016/j.cam.2015.04.048 – volume: 41 start-page: 839 issue: 9 year: 2014 ident: 6561_CR14 publication-title: Canadian Journal of Civil Engineering doi: 10.1139/cjce-2013-0370 – ident: 6561_CR44 – volume: 32 start-page: 1599 year: 2018 ident: 6561_CR11 publication-title: Water Resources Management doi: 10.1007/s11269-017-1893-1 – volume: 130 start-page: 93 issue: 2 year: 2004 ident: 6561_CR27 publication-title: Journal of water Resources Planning and Management doi: 10.1061/(ASCE)0733-9496(2004)130:2(93) – volume-title: Approximate Dynamic Programming: Solving the Curses of Dimensionality year: 2011 ident: 6561_CR40 doi: 10.1002/9781118029176 – volume: 14 start-page: 625 issue: 3 year: 2021 ident: 6561_CR48 publication-title: Energies doi: 10.3390/en14030625 – volume: 133 start-page: 4 issue: 1 year: 2007 ident: 6561_CR26 publication-title: Journal of Water Resources Planning and Management doi: 10.1061/(ASCE)0733-9496(2007)133:1(4) – volume: 143 start-page: 4016002 issue: 3 year: 2017 ident: 6561_CR41 publication-title: Journal of Irrigation and Drainage Engineering doi: 10.1061/(ASCE)IR.1943-4774.0001063 – volume: 19 start-page: 2277 issue: 11 year: 2005 ident: 6561_CR7 publication-title: Hydrological Processes: An International Journal doi: 10.1002/hyp.5674 – volume: 62 start-page: 97 year: 2019 ident: 6561_CR21 publication-title: Economic Analysis and Policy doi: 10.1016/j.eap.2019.01.005 – ident: 6561_CR49 doi: 10.1007/978-3-319-20430-7_5 – ident: 6561_CR20 – volume: 273 start-page: 650 issue: 2 year: 2019 ident: 6561_CR29 publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2018.08.001 – volume: 142 year: 2022 ident: 6561_CR32 publication-title: International Journal of Electrical Power & Energy Systems doi: 10.1016/j.ijepes.2022.108319 – volume: 77 start-page: 407 year: 2013 ident: 6561_CR6 publication-title: Mathematical Methods of Operations Research doi: 10.1007/s00186-012-0406-5 – volume: 10 start-page: 340 issue: 3 year: 2018 ident: 6561_CR47 publication-title: Water doi: 10.3390/w10030340 – ident: 6561_CR15 doi: 10.1007/s00170-022-09864-z – volume: 115 start-page: 507 issue: 3 year: 1999 ident: 6561_CR28 publication-title: European Journal of Operational Research doi: 10.1016/S0377-2217(98)00130-1 – volume: 115 year: 2020 ident: 6561_CR31 publication-title: International Journal of Electrical Power & Energy Systems doi: 10.1016/j.ijepes.2019.105469 – volume: 24 start-page: 3397 year: 2010 ident: 6561_CR17 publication-title: Water Resources Management doi: 10.1007/s11269-010-9612-1 – volume: 26 start-page: 447 issue: 3 year: 1990 ident: 6561_CR25 publication-title: Water Resources Research doi: 10.1029/WR026i003p00447
SSID	ssj0001185
Score	2.441136
Snippet	We present an approximate stochastic dynamic programming methodology for a real-world hydropower management problem, in which water must be released from...
SourceID	proquest crossref springer
SourceType	Aggregation Database Index Database Publisher
StartPage	333
SubjectTerms	Algorithms Apexes Approximation Artificial intelligence Business and Management Combinatorics Decomposition Dynamic programming Electricity Errors Hydroelectric power Hydrology Lower bounds Monte Carlo simulation Operations Research/Decision Theory Original Research Reservoirs Smelters Theory of Computation Water Water shortages
Title	Two simplex-based approximate stochastic dynamic programming schemes for a real hydropower management problem
URI	https://link.springer.com/article/10.1007/s10479-025-06561-4 https://www.proquest.com/docview/3235126715
Volume	351
WOSCitedRecordID	wos001462498100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLink Online Journals customDbUrl: eissn: 1572-9338 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001185 issn: 0254-5330 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3PT8IwFH5RNEYPoqgRRdODN20CbbduR2MknohRNNyW0r0FEgHDUPC_93VsgEYPeu6vpa_t-976-n0AF5ZAsm5YwVFITQGKQh52leBGycAEKGws40xsQrdaQacT3uePwtIi2724ksxO6pXHbkqH3Mmvktv0KfJZhw1yd4ETbHh4fF6cvwSZs8RFCn24y53Mn8r83MdXd7TEmN-uRTNv0yz_7zv3YDdHl-x6vhz2YQ2HFdgqktsrUC5EHFi-pyuws8JIeACD9nTE0r6jDJ5x5-FilrGOz_qEbJERUrQ946idWTyXsmd5gteAmjOKlHGAKSMgzAwjOPrCeh-x02GY0pCDRaYNy2VsDuGpedu-ueO5IgO3QosJVx5SfFaXMrEa0fgNE4aJ55NR6-hCDxUkHlKRIxwwwu86dv5QWyQY6EurUB5BaTga4jEwL4kTT5vESyjApGYm0KEfonE_tqimrsJlYZjodU68ES0plt0URzTFUTbFkapCrbBdlG_CNJJCEpyhpehV4aqw1bL4995O_lb9FLZFZm6XFliD0mT8hmewad8n_XR8ni3OTwvp3-s
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3JTsMwEB1BQSwHCgVEWX3gBpaK7cTJESFQEVAhKIhb5DoTUYm2qCkU_p5xmrSA4ABnb5HH9ryJn98A7FsCyfrICo5CagpQFPKwpQQ3SgYmQGFjGWfJJnSjETw8hNf5o7C0YLsXV5LZSf3psZvSIXfpV8lt-hT5TMOMIo_lFPNvbu_H5y9B5oy4SKEPd9zJ_KnMz318dUcTjPntWjTzNmfl_33nMizl6JIdj5bDCkxhtwJzBbm9AuUiiQPL93QFFj8pEq5CpznssbTtJIPfuPNwMctUx9_ahGyREVK0j8ZJO7N4lMqe5QSvDjVnFCljB1NGQJgZRnD0iT2-xy4Pw5CG7IyZNixPY7MGd2enzZM6zzMycCu0GHDlIcVnNSkTqxGNf2TCMPF8MmoNXeihgsRDKnKCA0b4LafOH2qLBAN9aRXKdSh1e13cAOYlceJpk3gJBZjUzAQ69EM07scW1dRVOCgMEz2PhDeiicSym-KIpjjKpjhSVdgubBflmzCNpJAEZ2gpelU4LGw1Kf69t82_Vd-D-Xrz6jK6PG9cbMGCyEzvKILbUBr0X3AHZu3roJ32d7OF-gGTcuLP
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3JTsMwEB2xCcGBpYAoFPCBG1gF24mTIwIqEKhCYhG3yHUmaiXSVm2h8PeM06QtCA6Is5dEHjvzXjzzBuDQEkjWp1ZwFFITQVHIw4YS3CgZmACFjWWcFZvQ9Xrw_BzeTWXxZ9HuxZXkKKfBqTS1B9VunFSnEt-UDrkrxUou1CcWNAvzygXSO75-_zT-FhN8zoIYiQZxF0eZp838PMdX1zTBm9-uSDPPU1v9_zuvwUqOOtnZaJuswwy2S7BYBL2XYLUo7sDys16C5Smlwg1IH4Yd1m85KeF37jxfzDI18vcWIV5khCBt0zjJZxaPStyzPPArpeGMGDSm2GcEkJlhBFNfWPMjdvUZhvTIdByBw_LyNpvwWLt8OL_ieaUGboUWA648JN52ImViNaLxT00YJp5Pxj5BR0lUkHhITU6IwAi_4VT7Q22R4KEvrUK5BXPtThu3gXlJnHjaJF5CxJOGmUCHfojG_fCinroMR4WRou5IkCOaSC-7JY5oiaNsiSNVhkphxyg_nP1ICkkwh7aoV4bjwm6T5t9n2_lb9wNYvLuoRbfX9ZtdWBKZ5V3kYAXmBr1X3IMF-zZo9Xv72Z79BAg767M
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Two+simplex-based+approximate+stochastic+dynamic+programming+schemes+for+a+real+hydropower+management+problem&rft.jtitle=Annals+of+operations+research&rft.au=Zephyr%2C+Luckny&rft.au=Lamond%2C+Bernard+F.&rft.au=Demeester%2C+Kenjy&rft.au=Latraverse%2C+Marco&rft.date=2025-08-01&rft.pub=Springer+US&rft.issn=0254-5330&rft.eissn=1572-9338&rft.volume=351&rft.issue=1&rft.spage=333&rft.epage=364&rft_id=info:doi/10.1007%2Fs10479-025-06561-4&rft.externalDocID=10_1007_s10479_025_06561_4
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0254-5330&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0254-5330&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0254-5330&client=summon