A systematic sampling evolutionary (SSE) method for stochastic bilevel programming problems
•Stochastic bilevel programming problems are studied.•A new meta-heuristic type algorithm is proposed to solve both deterministic and stochastic bilevel problems.•Convergence of the proposed method is established under some suitable conditions.•Preliminary numerical experiments indicate the validity...
Gespeichert in:
Veröffentlicht in: | Computers & operations research 2020-08, Vol.120, p.104942-14, Article 104942 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 14 |
---|---|
container_issue | |
container_start_page | 104942 |
container_title | Computers & operations research |
container_volume | 120 |
creator | Goshu, Natnael Nigussie Kassa, Semu Mitiku |
description | •Stochastic bilevel programming problems are studied.•A new meta-heuristic type algorithm is proposed to solve both deterministic and stochastic bilevel problems.•Convergence of the proposed method is established under some suitable conditions.•Preliminary numerical experiments indicate the validity of the proposed method.
Stochastic bilevel programming is a bilevel program having some form of randomness in the problem definition. The main objective is to optimize the leader’s (upper level) stochastic programming problem, where the follower’s problem is assumed to be satisfied as part of the constraints. Due to the involvement of randomness property and the hierarchical nature of the optimization procedure, the problem is computationally expensive and challenging. In this paper, a new meta-heuristic type algorithm is proposed that can effectively solve stochastic bilevel programs. The algorithm is based on realizing the random space, systematic sampling technique to choose a representative action from the leader’s decision space and on a hybrid particle swarm optimization procedure for searching its corresponding follower’s reaction for each leader’s action until Stackelberg equilibrium is achieved. The algorithm is shown to be convergent and its performance is checked using test problems from literature. The simulation result of the algorithm is very much promising and can be used to solve complex stochastic bilevel programming problems. |
doi_str_mv | 10.1016/j.cor.2020.104942 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2467814931</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0305054820300599</els_id><sourcerecordid>2467814931</sourcerecordid><originalsourceid>FETCH-LOGICAL-c357t-5aee8c5c78900bd6da3b8ebdf209ea586fcd1fdbb4c45130a5a8f8d81095279d3</originalsourceid><addsrcrecordid>eNp9kE1rwzAMhs3YYF23H7CbYZftkM5O4sRhp1K6Dyjs0A0GOxjHVlqHJO5st9B_P4fuPF0kgR690ovQLSUzSmjx2M6UdbOUpGOfV3l6hiaUl1lSFuzrHE1IRlhCWM4v0ZX3LYlRpnSCvufYH32AXgajsJf9rjPDBsPBdvtg7CDdEd-v18sH3EPYWo0b67APVm2lH4nadHCADu-c3TjZ9yMc67qD3l-ji0Z2Hm7-8hR9Pi8_Fq_J6v3lbTFfJSpjZUiYBOCKqZJXhNS60DKrOdS6SUkFkvGiUZo2uq5zlTOaEckkb7jmlFQsLSudTdHdaW8U_tmDD6K1ezdESZHmRclpXmU0TtHTlHLWeweN2DnTx_8EJWL0ULQieihGD8XJw8g8nRiI5x8MOOGVgUGBNg5UENqaf-hfn217PA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2467814931</pqid></control><display><type>article</type><title>A systematic sampling evolutionary (SSE) method for stochastic bilevel programming problems</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Goshu, Natnael Nigussie ; Kassa, Semu Mitiku</creator><creatorcontrib>Goshu, Natnael Nigussie ; Kassa, Semu Mitiku</creatorcontrib><description>•Stochastic bilevel programming problems are studied.•A new meta-heuristic type algorithm is proposed to solve both deterministic and stochastic bilevel problems.•Convergence of the proposed method is established under some suitable conditions.•Preliminary numerical experiments indicate the validity of the proposed method.
Stochastic bilevel programming is a bilevel program having some form of randomness in the problem definition. The main objective is to optimize the leader’s (upper level) stochastic programming problem, where the follower’s problem is assumed to be satisfied as part of the constraints. Due to the involvement of randomness property and the hierarchical nature of the optimization procedure, the problem is computationally expensive and challenging. In this paper, a new meta-heuristic type algorithm is proposed that can effectively solve stochastic bilevel programs. The algorithm is based on realizing the random space, systematic sampling technique to choose a representative action from the leader’s decision space and on a hybrid particle swarm optimization procedure for searching its corresponding follower’s reaction for each leader’s action until Stackelberg equilibrium is achieved. The algorithm is shown to be convergent and its performance is checked using test problems from literature. The simulation result of the algorithm is very much promising and can be used to solve complex stochastic bilevel programming problems.</description><identifier>ISSN: 0305-0548</identifier><identifier>EISSN: 1873-765X</identifier><identifier>EISSN: 0305-0548</identifier><identifier>DOI: 10.1016/j.cor.2020.104942</identifier><language>eng</language><publisher>New York: Elsevier Ltd</publisher><subject>Algorithms ; Bilevel programming ; Game theory ; Heuristic methods ; Operations research ; Particle swarm optimization ; Randomness ; Sample average approximation ; Sampling methods ; Stackelberg equilibrium ; Stochastic optimization ; Stochastic programming ; Systematic sampling</subject><ispartof>Computers & operations research, 2020-08, Vol.120, p.104942-14, Article 104942</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Pergamon Press Inc. Aug 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c357t-5aee8c5c78900bd6da3b8ebdf209ea586fcd1fdbb4c45130a5a8f8d81095279d3</citedby><cites>FETCH-LOGICAL-c357t-5aee8c5c78900bd6da3b8ebdf209ea586fcd1fdbb4c45130a5a8f8d81095279d3</cites><orcidid>0000-0001-5494-040X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cor.2020.104942$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Goshu, Natnael Nigussie</creatorcontrib><creatorcontrib>Kassa, Semu Mitiku</creatorcontrib><title>A systematic sampling evolutionary (SSE) method for stochastic bilevel programming problems</title><title>Computers & operations research</title><description>•Stochastic bilevel programming problems are studied.•A new meta-heuristic type algorithm is proposed to solve both deterministic and stochastic bilevel problems.•Convergence of the proposed method is established under some suitable conditions.•Preliminary numerical experiments indicate the validity of the proposed method.
Stochastic bilevel programming is a bilevel program having some form of randomness in the problem definition. The main objective is to optimize the leader’s (upper level) stochastic programming problem, where the follower’s problem is assumed to be satisfied as part of the constraints. Due to the involvement of randomness property and the hierarchical nature of the optimization procedure, the problem is computationally expensive and challenging. In this paper, a new meta-heuristic type algorithm is proposed that can effectively solve stochastic bilevel programs. The algorithm is based on realizing the random space, systematic sampling technique to choose a representative action from the leader’s decision space and on a hybrid particle swarm optimization procedure for searching its corresponding follower’s reaction for each leader’s action until Stackelberg equilibrium is achieved. The algorithm is shown to be convergent and its performance is checked using test problems from literature. The simulation result of the algorithm is very much promising and can be used to solve complex stochastic bilevel programming problems.</description><subject>Algorithms</subject><subject>Bilevel programming</subject><subject>Game theory</subject><subject>Heuristic methods</subject><subject>Operations research</subject><subject>Particle swarm optimization</subject><subject>Randomness</subject><subject>Sample average approximation</subject><subject>Sampling methods</subject><subject>Stackelberg equilibrium</subject><subject>Stochastic optimization</subject><subject>Stochastic programming</subject><subject>Systematic sampling</subject><issn>0305-0548</issn><issn>1873-765X</issn><issn>0305-0548</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kE1rwzAMhs3YYF23H7CbYZftkM5O4sRhp1K6Dyjs0A0GOxjHVlqHJO5st9B_P4fuPF0kgR690ovQLSUzSmjx2M6UdbOUpGOfV3l6hiaUl1lSFuzrHE1IRlhCWM4v0ZX3LYlRpnSCvufYH32AXgajsJf9rjPDBsPBdvtg7CDdEd-v18sH3EPYWo0b67APVm2lH4nadHCADu-c3TjZ9yMc67qD3l-ji0Z2Hm7-8hR9Pi8_Fq_J6v3lbTFfJSpjZUiYBOCKqZJXhNS60DKrOdS6SUkFkvGiUZo2uq5zlTOaEckkb7jmlFQsLSudTdHdaW8U_tmDD6K1ezdESZHmRclpXmU0TtHTlHLWeweN2DnTx_8EJWL0ULQieihGD8XJw8g8nRiI5x8MOOGVgUGBNg5UENqaf-hfn217PA</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Goshu, Natnael Nigussie</creator><creator>Kassa, Semu Mitiku</creator><general>Elsevier Ltd</general><general>Pergamon Press Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-5494-040X</orcidid></search><sort><creationdate>20200801</creationdate><title>A systematic sampling evolutionary (SSE) method for stochastic bilevel programming problems</title><author>Goshu, Natnael Nigussie ; Kassa, Semu Mitiku</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c357t-5aee8c5c78900bd6da3b8ebdf209ea586fcd1fdbb4c45130a5a8f8d81095279d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Bilevel programming</topic><topic>Game theory</topic><topic>Heuristic methods</topic><topic>Operations research</topic><topic>Particle swarm optimization</topic><topic>Randomness</topic><topic>Sample average approximation</topic><topic>Sampling methods</topic><topic>Stackelberg equilibrium</topic><topic>Stochastic optimization</topic><topic>Stochastic programming</topic><topic>Systematic sampling</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goshu, Natnael Nigussie</creatorcontrib><creatorcontrib>Kassa, Semu Mitiku</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Goshu, Natnael Nigussie</au><au>Kassa, Semu Mitiku</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A systematic sampling evolutionary (SSE) method for stochastic bilevel programming problems</atitle><jtitle>Computers & operations research</jtitle><date>2020-08-01</date><risdate>2020</risdate><volume>120</volume><spage>104942</spage><epage>14</epage><pages>104942-14</pages><artnum>104942</artnum><issn>0305-0548</issn><eissn>1873-765X</eissn><eissn>0305-0548</eissn><abstract>•Stochastic bilevel programming problems are studied.•A new meta-heuristic type algorithm is proposed to solve both deterministic and stochastic bilevel problems.•Convergence of the proposed method is established under some suitable conditions.•Preliminary numerical experiments indicate the validity of the proposed method.
Stochastic bilevel programming is a bilevel program having some form of randomness in the problem definition. The main objective is to optimize the leader’s (upper level) stochastic programming problem, where the follower’s problem is assumed to be satisfied as part of the constraints. Due to the involvement of randomness property and the hierarchical nature of the optimization procedure, the problem is computationally expensive and challenging. In this paper, a new meta-heuristic type algorithm is proposed that can effectively solve stochastic bilevel programs. The algorithm is based on realizing the random space, systematic sampling technique to choose a representative action from the leader’s decision space and on a hybrid particle swarm optimization procedure for searching its corresponding follower’s reaction for each leader’s action until Stackelberg equilibrium is achieved. The algorithm is shown to be convergent and its performance is checked using test problems from literature. The simulation result of the algorithm is very much promising and can be used to solve complex stochastic bilevel programming problems.</abstract><cop>New York</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.cor.2020.104942</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-5494-040X</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0305-0548 |
ispartof | Computers & operations research, 2020-08, Vol.120, p.104942-14, Article 104942 |
issn | 0305-0548 1873-765X 0305-0548 |
language | eng |
recordid | cdi_proquest_journals_2467814931 |
source | Elsevier ScienceDirect Journals Complete |
subjects | Algorithms Bilevel programming Game theory Heuristic methods Operations research Particle swarm optimization Randomness Sample average approximation Sampling methods Stackelberg equilibrium Stochastic optimization Stochastic programming Systematic sampling |
title | A systematic sampling evolutionary (SSE) method for stochastic bilevel programming problems |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T01%3A39%3A24IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20systematic%20sampling%20evolutionary%20(SSE)%20method%20for%20stochastic%20bilevel%20programming%20problems&rft.jtitle=Computers%20&%20operations%20research&rft.au=Goshu,%20Natnael%20Nigussie&rft.date=2020-08-01&rft.volume=120&rft.spage=104942&rft.epage=14&rft.pages=104942-14&rft.artnum=104942&rft.issn=0305-0548&rft.eissn=1873-765X&rft_id=info:doi/10.1016/j.cor.2020.104942&rft_dat=%3Cproquest_cross%3E2467814931%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2467814931&rft_id=info:pmid/&rft_els_id=S0305054820300599&rfr_iscdi=true |