Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem
<p dir="ltr">Selecting facility locations requires significant investment to anticipate and prepare for disruptive events like earthquakes, floods, or labor strikes. In practice, location choices account for facility capacities, which often cannot change during disruptions. When a fa...
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
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| _version_ | 1864513509817057280 |
|---|---|
| author | Nezir Aydin (8355378) |
| author2 | Alper Murat (19256374) Boris S. Mordukhovich (19256377) |
| author2_role | author author |
| author_facet | Nezir Aydin (8355378) Alper Murat (19256374) Boris S. Mordukhovich (19256377) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nezir Aydin (8355378) Alper Murat (19256374) Boris S. Mordukhovich (19256377) |
| dc.date.none.fl_str_mv | 2024-05-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s10462-024-10755-w |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Sample_intelligence-based_progressive_hedging_algorithms_for_the_stochastic_capacitated_reliable_facility_location_problem/26403946 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Commerce, management, tourism and services Transportation, logistics and supply chains Information and computing sciences Theory of computation Stochastic programming Capacitated reliable facility location Hybrid algorithm Progressive hedging Sample average approximation |
| dc.title.none.fl_str_mv | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Selecting facility locations requires significant investment to anticipate and prepare for disruptive events like earthquakes, floods, or labor strikes. In practice, location choices account for facility capacities, which often cannot change during disruptions. When a facility fails, demand transfers to others only if spare capacity exists. Thus, capacitated reliable facility location problems (CRFLP) under uncertainty are more complex than uncapacitated versions. To manage uncertainty and decide effectively, stochastic programming (SP) methods are often employed. Two commonly used SP methods are approximation methods, i.e., Sample Average Approximation (SAA), and decomposition methods, i.e., Progressive Hedging Algorithm (PHA). SAA needs large sample sizes for performance guarantee and turn into computationally intractable. On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed methods are innovative in that they blend the complementary aspects of PHA and SAA in terms of exactness and computational efficiency, respectively. Further, the developed methods are practical in that they allow the specialist to adjust the tradeoff between the exactness and speed of attaining a solution. We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. SAA). The validation of the methods is demonstrated through two-stage stochastic CRFLP. Promising results are attained for CRFLP, and the method has great potential to be generalized for SP problems.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-024-10755-w" target="_blank">https://dx.doi.org/10.1007/s10462-024-10755-w</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_52df33c76eadac895970de199fcb35ed |
| identifier_str_mv | 10.1007/s10462-024-10755-w |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26403946 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problemNezir Aydin (8355378)Alper Murat (19256374)Boris S. Mordukhovich (19256377)Commerce, management, tourism and servicesTransportation, logistics and supply chainsInformation and computing sciencesTheory of computationStochastic programmingCapacitated reliable facility locationHybrid algorithmProgressive hedgingSample average approximation<p dir="ltr">Selecting facility locations requires significant investment to anticipate and prepare for disruptive events like earthquakes, floods, or labor strikes. In practice, location choices account for facility capacities, which often cannot change during disruptions. When a facility fails, demand transfers to others only if spare capacity exists. Thus, capacitated reliable facility location problems (CRFLP) under uncertainty are more complex than uncapacitated versions. To manage uncertainty and decide effectively, stochastic programming (SP) methods are often employed. Two commonly used SP methods are approximation methods, i.e., Sample Average Approximation (SAA), and decomposition methods, i.e., Progressive Hedging Algorithm (PHA). SAA needs large sample sizes for performance guarantee and turn into computationally intractable. On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed methods are innovative in that they blend the complementary aspects of PHA and SAA in terms of exactness and computational efficiency, respectively. Further, the developed methods are practical in that they allow the specialist to adjust the tradeoff between the exactness and speed of attaining a solution. We present the effectiveness of the developed integrated approaches, Sampling Based Progressive Hedging Algorithm (SBPHA) and Discarding SBPHA (d-SBPHA), over the pure strategies (i.e. SAA). The validation of the methods is demonstrated through two-stage stochastic CRFLP. Promising results are attained for CRFLP, and the method has great potential to be generalized for SP problems.</p><h2>Other Information</h2><p dir="ltr">Published in: Artificial Intelligence Review<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s10462-024-10755-w" target="_blank">https://dx.doi.org/10.1007/s10462-024-10755-w</a></p>2024-05-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s10462-024-10755-whttps://figshare.com/articles/journal_contribution/Sample_intelligence-based_progressive_hedging_algorithms_for_the_stochastic_capacitated_reliable_facility_location_problem/26403946CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264039462024-05-07T03:00:00Z |
| spellingShingle | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem Nezir Aydin (8355378) Commerce, management, tourism and services Transportation, logistics and supply chains Information and computing sciences Theory of computation Stochastic programming Capacitated reliable facility location Hybrid algorithm Progressive hedging Sample average approximation |
| status_str | publishedVersion |
| title | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem |
| title_full | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem |
| title_fullStr | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem |
| title_full_unstemmed | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem |
| title_short | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem |
| title_sort | Sample intelligence-based progressive hedging algorithms for the stochastic capacitated reliable facility location problem |
| topic | Commerce, management, tourism and services Transportation, logistics and supply chains Information and computing sciences Theory of computation Stochastic programming Capacitated reliable facility location Hybrid algorithm Progressive hedging Sample average approximation |