A simulation-based approximate dynamic programming approach to dynamic and stochastic resource-constrained multi-project scheduling problem
We consider the dynamic and stochastic resource-constrained multi-project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of r...
Saved in:
| Published in: | European journal of operational research Vol. 315; no. 2; pp. 454 - 469 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.06.2024
|
| Subjects: | |
| ISSN: | 0377-2217, 1872-6860 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | We consider the dynamic and stochastic resource-constrained multi-project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite-horizon discrete-time Markov decision process and seeks to maximise the expected discounted long-run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision-maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule-based algorithm and a reactive genetic algorithm.
•Dynamic programming is optimal in small problems but intractable for large problems.•Rule based algorithms are straightforward to apply but can perform poorly.•Reactive baseline algorithms have a restricted view of future profits.•Approximate dynamic programming learns about actions by simulating the future.•Approximate dynamic programming performs competitively compared to alternatives. |
|---|---|
| AbstractList | We consider the dynamic and stochastic resource-constrained multi-project scheduling problem which allows for the random arrival of projects and stochastic task durations. Completing projects generates rewards, which are reduced by a tardiness cost in the case of late completion. Multiple types of resource are available, and projects consume different amounts of these resources when under processing. The problem is modelled as an infinite-horizon discrete-time Markov decision process and seeks to maximise the expected discounted long-run profit. We use an approximate dynamic programming algorithm (ADP) with a linear approximation model which can be used for online decision making. Our approximation model uses project elements that are easily accessible by a decision-maker, with the model coefficients obtained offline via a combination of Monte Carlo simulation and least squares estimation. Our numerical study shows that ADP often statistically significantly outperforms the optimal reactive baseline algorithm (ORBA). In experiments on smaller problems however, both typically perform suboptimally compared to the optimal scheduler obtained by stochastic dynamic programming. ADP has an advantage over ORBA and dynamic programming in that ADP can be applied to larger problems. We also show that ADP generally produces statistically significantly higher profits than common algorithms used in practice, such as a rule-based algorithm and a reactive genetic algorithm.
•Dynamic programming is optimal in small problems but intractable for large problems.•Rule based algorithms are straightforward to apply but can perform poorly.•Reactive baseline algorithms have a restricted view of future profits.•Approximate dynamic programming learns about actions by simulating the future.•Approximate dynamic programming performs competitively compared to alternatives. |
| Author | Satic, U. Jacko, P. Kirkbride, C. |
| Author_xml | – sequence: 1 givenname: U. orcidid: 0000-0002-9160-0006 surname: Satic fullname: Satic, U. email: ugur.satic@agu.edu.tr organization: Lancaster University Management School, Bailrigg, Lancaster, LA1 4YX, United Kingdom – sequence: 2 givenname: P. orcidid: 0000-0003-3376-0260 surname: Jacko fullname: Jacko, P. organization: Lancaster University Management School, Bailrigg, Lancaster, LA1 4YX, United Kingdom – sequence: 3 givenname: C. orcidid: 0000-0002-3667-3413 surname: Kirkbride fullname: Kirkbride, C. organization: Lancaster University Management School, Bailrigg, Lancaster, LA1 4YX, United Kingdom |
| BookMark | eNp9kMtKAzEUhoNUsK2-gKt5galJ5pIpuCnFGxTc6HrInJxpM8wkJUnFPoMvbcaKCxfdJOQ_fCf834xMjDVIyC2jC0ZZedctsLNuwSnPYrCgeXlBpqwSPC2rkk7IlGZCpJwzcUVm3neUUlawYkq-VonXw6GXQVuTNtKjSuR-7-ynHmTARB2NHDQkMdk6OQzabE9zCbsk2L-5NCrxwcJO-hCfDr09OMAUrPHBSW3i3vhN0GlkO4SQeNihOvTjwhg1PQ7X5LKVvceb33tO3h8f3tbP6eb16WW92qSQZ3lIZb5ccqkEVU1btkCbQihEqZRAKKpcZWXRYJ43DeMCM9EgLKGqIJ5KAJNZNif8tBec9d5hW-9dbOuONaP1qLPu6lFnPeocs6gzQtU_CHT4sTbW68-j9ycUY6kPja72oNEAKu2iiVpZfQ7_BsyTma0 |
| CitedBy_id | crossref_primary_10_3390_math13091395 crossref_primary_10_1016_j_ejor_2025_01_011 crossref_primary_10_1049_gtd2_70145 crossref_primary_10_1016_j_eswa_2024_124947 crossref_primary_10_1080_01605682_2025_2457655 crossref_primary_10_1016_j_jhydrol_2024_132515 crossref_primary_10_1080_00207543_2024_2430457 crossref_primary_10_1016_j_mex_2025_103496 crossref_primary_10_1016_j_cie_2025_111489 crossref_primary_10_1080_23302674_2025_2532727 crossref_primary_10_3390_systems13030191 crossref_primary_10_1016_j_eswa_2025_127488 crossref_primary_10_1016_j_eswa_2025_128881 crossref_primary_10_1080_21681015_2024_2377180 crossref_primary_10_2478_emj_2025_0014 |
| Cites_doi | 10.1002/nav.20347 10.1080/00207543.2020.1857450 10.1007/s10951-015-0421-5 10.1080/00207543.2020.1788737 10.1016/j.ejor.2011.09.007 10.1016/S0377-2217(96)00170-1 10.1287/mnsc.41.3.458 10.1016/j.ejor.2015.04.015 10.1111/j.1475-3995.2007.00614.x 10.1007/s10479-005-5730-1 10.1016/j.trc.2018.09.010 10.1287/mnsc.41.10.1693 10.1016/j.ejor.2016.11.023 10.1080/00207543.2018.1431417 10.1007/s10951-009-0160-6 10.1016/j.cie.2019.106060 10.1016/j.orl.2017.06.002 10.1002/rnc.1164 |
| ContentType | Journal Article |
| Copyright | 2023 The Author(s) |
| Copyright_xml | – notice: 2023 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.ejor.2023.10.046 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Business |
| EISSN | 1872-6860 |
| EndPage | 469 |
| ExternalDocumentID | 10_1016_j_ejor_2023_10_046 S0377221723008214 |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 6I. 6OB 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAFTH AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABAOU ABBOA ABFNM ABFRF ABJNI ABMAC ABUCO ABYKQ ACAZW ACDAQ ACGFO ACGFS ACIWK ACNCT ACRLP ACZNC ADBBV ADEZE ADGUI AEBSH AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR AXJTR BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W KOM MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ RXW SCC SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSV SSW SSZ T5K TAE TN5 U5U XPP ZMT ~02 ~G- 1OL 29G 41~ 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADIYS ADJOM ADMUD ADNMO ADXHL AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HVGLF HZ~ LY1 M41 R2- VH1 WUQ ~HD |
| ID | FETCH-LOGICAL-c434t-a4992ad70dbf6fc0b57deeadd7ec584d365be44bb127e37bec9c88cc9cd7c1a33 |
| ISICitedReferencesCount | 17 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001185197200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0377-2217 |
| IngestDate | Sat Nov 29 07:24:44 EST 2025 Tue Nov 18 22:39:05 EST 2025 Sat Feb 17 16:07:38 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | Project scheduling Approximate dynamic programming Dynamic resource allocation Dynamic programming Markov decision processes |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c434t-a4992ad70dbf6fc0b57deeadd7ec584d365be44bb127e37bec9c88cc9cd7c1a33 |
| ORCID | 0000-0002-3667-3413 0000-0003-3376-0260 0000-0002-9160-0006 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.ejor.2023.10.046 |
| PageCount | 16 |
| ParticipantIDs | crossref_primary_10_1016_j_ejor_2023_10_046 crossref_citationtrail_10_1016_j_ejor_2023_10_046 elsevier_sciencedirect_doi_10_1016_j_ejor_2023_10_046 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-06-01 |
| PublicationDateYYYYMMDD | 2024-06-01 |
| PublicationDate_xml | – month: 06 year: 2024 text: 2024-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | European journal of operational research |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Choi, Realff, Lee (b5) 2007; 17 Melchiors, Leus, Creemers, Kolisch (b19) 2018; 56 Capa, Ulusoy (b3) 2015 Creemers (b7) 2015; 18 Kolisch, Sprecher, Drexl (b14) 1995; 41 Fliedner, T., Gutjahr, W., Kolisch, R., & Melchiors, P. (2012). Solving the Dynamic Stochastic Resource-Constrained Multi-Project Scheduling Problem with SRCPSP-methods. In Schütz, Kolisch (b27) 2012; 218 Kolisch, Sprecher (b13) 1996; 96 Schwindt (b28) 1998 Adler, Mandelbaum, Nguyen, Schwerer (b1) 1995; 41 Parizi, Gocgun, Ghate (b21) 2017; 45 Pamay, Bülbül, Ulusoy (b20) 2014; vol. 200 Wellingtone PPM (b30) 2018 Davis, Robbins, Lunday (b8) 2017; 259 Garey, Johnson (b10) 1979 Homberger (b12) 2007; 14 (pp. 148–151). Melchiors, Kolisch (b18) 2009 Melchiors (b17) 2015 He, Yang, Li (b11) 2018; 96 Ahuja, Birge (b2) 2020; 32 Li, Womer (b15) 2015; 246 Powell (b22) 2009; 56 Li, Zhang, Sun, Dong (b16) 2023; 61 Cohen, Golany, Shtub (b6) 2005; 134 Sutton, Barto (b29) 2018 Chen, Ding, Zhang, Qin (b4) 2019; 137 Powell (b23) 2011 Satic, Jacko, Kirkbride (b25) 2020 Ronconi, Powell (b24) 2010; 13 Satic, Jacko, Kirkbride (b26) 2022; 60 Wellingtone PPM (10.1016/j.ejor.2023.10.046_b30) 2018 Kolisch (10.1016/j.ejor.2023.10.046_b14) 1995; 41 Choi (10.1016/j.ejor.2023.10.046_b5) 2007; 17 Powell (10.1016/j.ejor.2023.10.046_b22) 2009; 56 Adler (10.1016/j.ejor.2023.10.046_b1) 1995; 41 Ahuja (10.1016/j.ejor.2023.10.046_b2) 2020; 32 Creemers (10.1016/j.ejor.2023.10.046_b7) 2015; 18 Davis (10.1016/j.ejor.2023.10.046_b8) 2017; 259 Parizi (10.1016/j.ejor.2023.10.046_b21) 2017; 45 Sutton (10.1016/j.ejor.2023.10.046_b29) 2018 Schütz (10.1016/j.ejor.2023.10.046_b27) 2012; 218 Powell (10.1016/j.ejor.2023.10.046_b23) 2011 Schwindt (10.1016/j.ejor.2023.10.046_b28) 1998 Cohen (10.1016/j.ejor.2023.10.046_b6) 2005; 134 Kolisch (10.1016/j.ejor.2023.10.046_b13) 1996; 96 Ronconi (10.1016/j.ejor.2023.10.046_b24) 2010; 13 Chen (10.1016/j.ejor.2023.10.046_b4) 2019; 137 Garey (10.1016/j.ejor.2023.10.046_b10) 1979 Melchiors (10.1016/j.ejor.2023.10.046_b17) 2015 Melchiors (10.1016/j.ejor.2023.10.046_b18) 2009 Satic (10.1016/j.ejor.2023.10.046_b26) 2022; 60 Melchiors (10.1016/j.ejor.2023.10.046_b19) 2018; 56 He (10.1016/j.ejor.2023.10.046_b11) 2018; 96 Li (10.1016/j.ejor.2023.10.046_b16) 2023; 61 Capa (10.1016/j.ejor.2023.10.046_b3) 2015 10.1016/j.ejor.2023.10.046_b9 Homberger (10.1016/j.ejor.2023.10.046_b12) 2007; 14 Li (10.1016/j.ejor.2023.10.046_b15) 2015; 246 Satic (10.1016/j.ejor.2023.10.046_b25) 2020 Pamay (10.1016/j.ejor.2023.10.046_b20) 2014; vol. 200 |
| References_xml | – year: 2018 ident: b30 article-title: The state of project management annual survey 2018 – volume: 246 start-page: 20 year: 2015 end-page: 33 ident: b15 article-title: Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming publication-title: European Journal of Operational Research – volume: 61 start-page: 198 year: 2023 end-page: 218 ident: b16 article-title: Dynamic resource levelling in projects under uncertainty publication-title: International Journal of Production Research – volume: 13 start-page: 597 year: 2010 end-page: 607 ident: b24 article-title: Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming publication-title: Journal of Scheduling – volume: 137 year: 2019 ident: b4 article-title: Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival publication-title: Computers & Industrial Engineering – volume: 218 start-page: 239 year: 2012 end-page: 250 ident: b27 article-title: Approximate dynamic programming for capacity allocation in the service industry publication-title: European Journal of Operational Research – reference: Fliedner, T., Gutjahr, W., Kolisch, R., & Melchiors, P. (2012). Solving the Dynamic Stochastic Resource-Constrained Multi-Project Scheduling Problem with SRCPSP-methods. In – year: 2015 ident: b17 publication-title: Dynamic and stochastic multi-project planning – volume: 41 start-page: 458 year: 1995 end-page: 484 ident: b1 article-title: From project to process management: An empirically-based framework for analyzing product development time publication-title: Management Science – volume: 60 start-page: 1411 year: 2022 end-page: 1423 ident: b26 article-title: Performance evaluation of scheduling policies for the dynamic and stochastic resource-constrained multi-project scheduling problem publication-title: International Journal of Production Research – volume: 56 start-page: 459 year: 2018 end-page: 475 ident: b19 article-title: Dynamic order acceptance and capacity planning in a stochastic multi-project environment with a bottleneck resource publication-title: International Journal of Production Research – volume: 259 start-page: 873 year: 2017 end-page: 886 ident: b8 article-title: Approximate dynamic programming for missile defense interceptor fire control publication-title: European Journal of Operational Research – volume: 41 start-page: 1693 year: 1995 end-page: 1703 ident: b14 article-title: Characterization and generation of a general class of resource-constrained project scheduling problems publication-title: Management Science – volume: 56 start-page: 239 year: 2009 end-page: 249 ident: b22 article-title: What you should know about approximate dynamic programming publication-title: Naval Research Logistics – year: 2011 ident: b23 article-title: Approximate dynamic programming: Solving the curses of dimensionality – volume: 96 start-page: 144 year: 2018 end-page: 159 ident: b11 article-title: Vehicle scheduling under stochastic trip times: An approximate dynamic programming approach publication-title: Transportation Research Part C (Emerging Technologies) – start-page: 135 year: 2009 end-page: 140 ident: b18 article-title: Scheduling of multiple R&D projects in a dynamic and stochastic environment publication-title: Operations research proceedings 2008 – year: 1979 ident: b10 article-title: Computers and intractability: A guide to the theory of NP-completeness – volume: 45 start-page: 442 year: 2017 end-page: 447 ident: b21 article-title: Approximate policy iteration for dynamic resource-constrained project scheduling publication-title: Operations Research Letters – volume: 17 start-page: 1214 year: 2007 end-page: 1231 ident: b5 article-title: A Q-learning-based method applied to stochastic resource constrained project scheduling with new project arrivals publication-title: International Journal of Robust and Nonlinear Control – start-page: 1 year: 2015 end-page: 6 ident: b3 article-title: Proactive project scheduling in an R&D department a bi-objective genetic algorithm publication-title: 2015 International conference on industrial engineering and operations management (IEOM), Vol. 1 – volume: 134 start-page: 183 year: 2005 end-page: 199 ident: b6 article-title: Managing stochastic, finite capacity, multi-project systems through the cross-entropy methodology publication-title: Annals of Operations Research – reference: (pp. 148–151). – volume: 96 start-page: 205 year: 1996 end-page: 216 ident: b13 article-title: PSPLIB: A project scheduling problem library publication-title: European Journal of Operational Research – start-page: 100 year: 2020 end-page: 114 ident: b25 article-title: Performance evaluation of scheduling policies for the DRCMPSP publication-title: Analytical and stochastic modelling techniques and applications, Vol. 12023 – volume: 14 start-page: 565 year: 2007 end-page: 589 ident: b12 article-title: A multi-agent system for the decentralized resource-constrained multi-project scheduling problem publication-title: International Transactions in Operational Research – volume: vol. 200 start-page: 219 year: 2014 end-page: 247 ident: b20 article-title: Dynamic resource constrained multi-project scheduling problem with weighted earliness/tardiness costs publication-title: Essays in production, project planning and scheduling – volume: 18 start-page: 263 year: 2015 end-page: 273 ident: b7 article-title: Minimizing the expected makespan of a project with stochastic activity durations under resource constraints publication-title: Journal of Scheduling – volume: 32 start-page: 877 year: 2020 end-page: 894 ident: b2 article-title: An approximation approach for response adaptive clinical trial design publication-title: INFORMS Journal on Computing – year: 1998 ident: b28 article-title: Generation of resource constrained project scheduling problems subject to temporal constraints – year: 2018 ident: b29 publication-title: Reinforcement learning: An introduction – volume: 56 start-page: 239 issue: 3 year: 2009 ident: 10.1016/j.ejor.2023.10.046_b22 article-title: What you should know about approximate dynamic programming publication-title: Naval Research Logistics doi: 10.1002/nav.20347 – volume: 60 start-page: 1411 issue: 4 year: 2022 ident: 10.1016/j.ejor.2023.10.046_b26 article-title: Performance evaluation of scheduling policies for the dynamic and stochastic resource-constrained multi-project scheduling problem publication-title: International Journal of Production Research doi: 10.1080/00207543.2020.1857450 – volume: 18 start-page: 263 issue: 3 year: 2015 ident: 10.1016/j.ejor.2023.10.046_b7 article-title: Minimizing the expected makespan of a project with stochastic activity durations under resource constraints publication-title: Journal of Scheduling doi: 10.1007/s10951-015-0421-5 – volume: 61 start-page: 198 issue: 1 year: 2023 ident: 10.1016/j.ejor.2023.10.046_b16 article-title: Dynamic resource levelling in projects under uncertainty publication-title: International Journal of Production Research doi: 10.1080/00207543.2020.1788737 – volume: 218 start-page: 239 issue: 1 year: 2012 ident: 10.1016/j.ejor.2023.10.046_b27 article-title: Approximate dynamic programming for capacity allocation in the service industry publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2011.09.007 – volume: 96 start-page: 205 issue: 1 year: 1996 ident: 10.1016/j.ejor.2023.10.046_b13 article-title: PSPLIB: A project scheduling problem library publication-title: European Journal of Operational Research doi: 10.1016/S0377-2217(96)00170-1 – volume: 32 start-page: 877 issue: 4 year: 2020 ident: 10.1016/j.ejor.2023.10.046_b2 article-title: An approximation approach for response adaptive clinical trial design publication-title: INFORMS Journal on Computing – year: 2011 ident: 10.1016/j.ejor.2023.10.046_b23 – volume: vol. 200 start-page: 219 year: 2014 ident: 10.1016/j.ejor.2023.10.046_b20 article-title: Dynamic resource constrained multi-project scheduling problem with weighted earliness/tardiness costs – volume: 41 start-page: 458 issue: 3 year: 1995 ident: 10.1016/j.ejor.2023.10.046_b1 article-title: From project to process management: An empirically-based framework for analyzing product development time publication-title: Management Science doi: 10.1287/mnsc.41.3.458 – volume: 246 start-page: 20 issue: 1 year: 2015 ident: 10.1016/j.ejor.2023.10.046_b15 article-title: Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2015.04.015 – year: 2018 ident: 10.1016/j.ejor.2023.10.046_b29 – volume: 14 start-page: 565 issue: 6 year: 2007 ident: 10.1016/j.ejor.2023.10.046_b12 article-title: A multi-agent system for the decentralized resource-constrained multi-project scheduling problem publication-title: International Transactions in Operational Research doi: 10.1111/j.1475-3995.2007.00614.x – start-page: 135 year: 2009 ident: 10.1016/j.ejor.2023.10.046_b18 article-title: Scheduling of multiple R&D projects in a dynamic and stochastic environment – year: 2018 ident: 10.1016/j.ejor.2023.10.046_b30 – volume: 134 start-page: 183 issue: 1 year: 2005 ident: 10.1016/j.ejor.2023.10.046_b6 article-title: Managing stochastic, finite capacity, multi-project systems through the cross-entropy methodology publication-title: Annals of Operations Research doi: 10.1007/s10479-005-5730-1 – volume: 96 start-page: 144 year: 2018 ident: 10.1016/j.ejor.2023.10.046_b11 article-title: Vehicle scheduling under stochastic trip times: An approximate dynamic programming approach publication-title: Transportation Research Part C (Emerging Technologies) doi: 10.1016/j.trc.2018.09.010 – ident: 10.1016/j.ejor.2023.10.046_b9 – volume: 41 start-page: 1693 issue: 10 year: 1995 ident: 10.1016/j.ejor.2023.10.046_b14 article-title: Characterization and generation of a general class of resource-constrained project scheduling problems publication-title: Management Science doi: 10.1287/mnsc.41.10.1693 – year: 2015 ident: 10.1016/j.ejor.2023.10.046_b17 – volume: 259 start-page: 873 issue: 3 year: 2017 ident: 10.1016/j.ejor.2023.10.046_b8 article-title: Approximate dynamic programming for missile defense interceptor fire control publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2016.11.023 – start-page: 100 year: 2020 ident: 10.1016/j.ejor.2023.10.046_b25 article-title: Performance evaluation of scheduling policies for the DRCMPSP – start-page: 1 year: 2015 ident: 10.1016/j.ejor.2023.10.046_b3 article-title: Proactive project scheduling in an R&D department a bi-objective genetic algorithm – volume: 56 start-page: 459 issue: 1–2 year: 2018 ident: 10.1016/j.ejor.2023.10.046_b19 article-title: Dynamic order acceptance and capacity planning in a stochastic multi-project environment with a bottleneck resource publication-title: International Journal of Production Research doi: 10.1080/00207543.2018.1431417 – volume: 13 start-page: 597 year: 2010 ident: 10.1016/j.ejor.2023.10.046_b24 article-title: Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming publication-title: Journal of Scheduling doi: 10.1007/s10951-009-0160-6 – year: 1998 ident: 10.1016/j.ejor.2023.10.046_b28 – volume: 137 year: 2019 ident: 10.1016/j.ejor.2023.10.046_b4 article-title: Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2019.106060 – volume: 45 start-page: 442 issue: 5 year: 2017 ident: 10.1016/j.ejor.2023.10.046_b21 article-title: Approximate policy iteration for dynamic resource-constrained project scheduling publication-title: Operations Research Letters doi: 10.1016/j.orl.2017.06.002 – volume: 17 start-page: 1214 issue: 13 year: 2007 ident: 10.1016/j.ejor.2023.10.046_b5 article-title: A Q-learning-based method applied to stochastic resource constrained project scheduling with new project arrivals publication-title: International Journal of Robust and Nonlinear Control doi: 10.1002/rnc.1164 – year: 1979 ident: 10.1016/j.ejor.2023.10.046_b10 |
| SSID | ssj0001515 |
| Score | 2.539201 |
| Snippet | We consider the dynamic and stochastic resource-constrained multi-project scheduling problem which allows for the random arrival of projects and stochastic... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 454 |
| SubjectTerms | Approximate dynamic programming Dynamic programming Dynamic resource allocation Markov decision processes Project scheduling |
| Title | A simulation-based approximate dynamic programming approach to dynamic and stochastic resource-constrained multi-project scheduling problem |
| URI | https://dx.doi.org/10.1016/j.ejor.2023.10.046 |
| Volume | 315 |
| WOSCitedRecordID | wos001185197200001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6860 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001515 issn: 0377-2217 databaseCode: AIEXJ dateStart: 19950105 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9owFD5idJq2h13Ypra7yA97Q0YQO3XyiKZWu6mqtFbiLUpsR4O2AUFW8R_6Y_oXexzbScZotT7sxUIOdgLnw-fC-c4B-JSqUa7yUNBAiZzyEGGcCR5StF1RX8pYZaqqM_tDHB9Hk0l80unceC7M1YUoimi9jhf_VdQ4h8I21NkHiLveFCfwNQodRxQ7jv8k-HF_Nb10TbmoUVLKFg5fT9E41X1lW9D7xKzLiqS4aIhV_noVUC_n8le6smWebZifSmNQmr4SuG-VjUhdMKePfjLqLUdvr9rU3Bn2dyYwTix9MNJVHaqj0z9NKVkDgLNBneWTyvMqsntST32fLs8N58zmMw7aMYyAN7lWnrslEC2BZXH6c5lZnqcDYNA6ZbmtO-0UNre9Xv7SBTYsMRvo2dwUfg3YwKTx8S2FtzcUYp2m6DPgZonZIzF74ESCezyCnUCEcdSFnfHXw8m3Wvkb-7D648p9IMfTsimFm0-y3RZq2TenL-G5c0zI2ALqFXR00YMnnhfRgxe-_wdx6qAHz1rFLF_D9ZhsAo-0gEccsEgLeMQDj5Tz-joCjzTAI9uAR_4AHmmARxzw3sDZ0eHp5y_UtfqgkjNe0hQd7yBVYqiy_CCXwywUSuMhp4SWaCIrdhBmmvMsGwVCM4EHTyyjSOKohByljL2FbjEv9C4QKdFLySJcp4dcK5aiT6MZH6Zo-eLCaA9G_ktPpKuDb57-Irlb3HvQr9csbBWYe98delkmzo619mmC0Lxn3f6D7vIOnjY_pffQLZe_9Qd4LK_K6Wr50eHyFjoFyY4 |
| linkProvider | Elsevier |
| 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=A+simulation-based+approximate+dynamic+programming+approach+to+dynamic+and+stochastic+resource-constrained+multi-project+scheduling+problem&rft.jtitle=European+journal+of+operational+research&rft.au=Satic%2C+U.&rft.au=Jacko%2C+P.&rft.au=Kirkbride%2C+C.&rft.date=2024-06-01&rft.issn=0377-2217&rft.volume=315&rft.issue=2&rft.spage=454&rft.epage=469&rft_id=info:doi/10.1016%2Fj.ejor.2023.10.046&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_ejor_2023_10_046 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0377-2217&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0377-2217&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0377-2217&client=summon |