A globalized robust preemptive goal programming method for marine reserve problem with eco-economy tradeoffs and multiple uncertainties
“Blue growth” emphasizes the sustainable use of marine resources for economic process whereas conserving the health of marine ecosystems. Motivated by this, we focus on the marine reserve design problem under multiple uncertainties, incorporating conservation, economy, reserve area and reserve shape...
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Veröffentlicht in: | Journal of ambient intelligence and humanized computing 2023-10, Vol.14 (10), p.13375-13385 |
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creator | Jia, Ruru Gao, Jinwu Zou, Zezhou |
description | “Blue growth” emphasizes the sustainable use of marine resources for economic process whereas conserving the health of marine ecosystems. Motivated by this, we focus on the marine reserve design problem under multiple uncertainties, incorporating conservation, economy, reserve area and reserve shape compactness. We develop a novel globalized robust preemptive goal programming model to investigate the problem, where inner-outer uncertainty sets are defined to model the uncertain conservation value and opportunity cost. We thereby derive the tractable globalized robust counterpart of the proposed model and perform the model analysis on the globalized sensitivity parameters. Our proposed optimization framework can be demonstrated for the marine reserve design of West Coast National Marine Park of Qingdao in China. The computational experiments justify several important insights: (i) the resulting reserve area design is more robust than the delimitation of 2014 on the West Coast New Area; (ii) with the change of parameters related to uncertainty sets and global sensitivity, the conservation value can always be realized while the opportunity cost is sensitive; (iii) the comparison results with classic robust optimization method show that the proposed method can reduce the conservatism of decisions and enable planners to formulate more effective policy. |
doi_str_mv | 10.1007/s12652-022-03792-2 |
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The computational experiments justify several important insights: (i) the resulting reserve area design is more robust than the delimitation of 2014 on the West Coast New Area; (ii) with the change of parameters related to uncertainty sets and global sensitivity, the conservation value can always be realized while the opportunity cost is sensitive; (iii) the comparison results with classic robust optimization method show that the proposed method can reduce the conservatism of decisions and enable planners to formulate more effective policy.</description><identifier>ISSN: 1868-5137</identifier><identifier>EISSN: 1868-5145</identifier><identifier>DOI: 10.1007/s12652-022-03792-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Artificial Intelligence ; Biodiversity ; Case studies ; Commercial fishing ; Computational Intelligence ; Conservation ; Decision making ; Engineering ; Fisheries ; Fishing ; Goal programming ; Marine resources ; Mathematical models ; Opportunity costs ; Optimization ; Original Research ; Parameter sensitivity ; Parameter uncertainty ; Planning ; Preempting ; Robotics and Automation ; Robustness ; User Interfaces and Human Computer Interaction</subject><ispartof>Journal of ambient intelligence and humanized computing, 2023-10, Vol.14 (10), p.13375-13385</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-d6daf3ccf4f4abd9d247de537253d999136dd5119975629b1e7f44d8419eec113</cites><orcidid>0000-0003-4806-927X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12652-022-03792-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2920010711?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21387,27923,27924,33743,41487,42556,43804,51318,64384,64388,72240</link.rule.ids></links><search><creatorcontrib>Jia, Ruru</creatorcontrib><creatorcontrib>Gao, Jinwu</creatorcontrib><creatorcontrib>Zou, Zezhou</creatorcontrib><title>A globalized robust preemptive goal programming method for marine reserve problem with eco-economy tradeoffs and multiple uncertainties</title><title>Journal of ambient intelligence and humanized computing</title><addtitle>J Ambient Intell Human Comput</addtitle><description>“Blue growth” emphasizes the sustainable use of marine resources for economic process whereas conserving the health of marine ecosystems. Motivated by this, we focus on the marine reserve design problem under multiple uncertainties, incorporating conservation, economy, reserve area and reserve shape compactness. We develop a novel globalized robust preemptive goal programming model to investigate the problem, where inner-outer uncertainty sets are defined to model the uncertain conservation value and opportunity cost. We thereby derive the tractable globalized robust counterpart of the proposed model and perform the model analysis on the globalized sensitivity parameters. Our proposed optimization framework can be demonstrated for the marine reserve design of West Coast National Marine Park of Qingdao in China. The computational experiments justify several important insights: (i) the resulting reserve area design is more robust than the delimitation of 2014 on the West Coast New Area; (ii) with the change of parameters related to uncertainty sets and global sensitivity, the conservation value can always be realized while the opportunity cost is sensitive; (iii) the comparison results with classic robust optimization method show that the proposed method can reduce the conservatism of decisions and enable planners to formulate more effective policy.</description><subject>Artificial Intelligence</subject><subject>Biodiversity</subject><subject>Case studies</subject><subject>Commercial fishing</subject><subject>Computational Intelligence</subject><subject>Conservation</subject><subject>Decision making</subject><subject>Engineering</subject><subject>Fisheries</subject><subject>Fishing</subject><subject>Goal programming</subject><subject>Marine resources</subject><subject>Mathematical models</subject><subject>Opportunity costs</subject><subject>Optimization</subject><subject>Original Research</subject><subject>Parameter sensitivity</subject><subject>Parameter uncertainty</subject><subject>Planning</subject><subject>Preempting</subject><subject>Robotics and Automation</subject><subject>Robustness</subject><subject>User Interfaces and Human Computer Interaction</subject><issn>1868-5137</issn><issn>1868-5145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kM9KxDAQxosouKy-gKeA52omaZvNUcR_IHjRc0ibSY20zZqkyvoCvrZxV_TmwDAJ_L5vkq8oToCeAaXiPAJralZSlpsLyUq2Vyxg1azKGqp6__fMxWFxHOMLzcUlB4BF8XlB-sG3enAfaEjw7RwTWQfEcZ3cG5Le6yHffR_0OLqpJyOmZ2-I9YGMOrgJScCIIaOZagccybtLzwQ7X-ae_LghKWiD3tpI9GTIOA_JrQck89RhSNpNyWE8Kg6sHiIe_8xl8XR99Xh5W94_3NxdXtyXHRM0laYx2vKus5WtdGukYZUwWHPBam6klMAbY2oAKUXdMNkCCltVZlWBROwA-LI43fnm177OGJN68XOY8krFJKMUqNhSbEd1wccY0Kp1cPm7GwVUfWeudpmrnLnaZq5YFvGdKGZ46jH8Wf-j-gIxjIdw</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Jia, Ruru</creator><creator>Gao, Jinwu</creator><creator>Zou, Zezhou</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><orcidid>https://orcid.org/0000-0003-4806-927X</orcidid></search><sort><creationdate>20231001</creationdate><title>A globalized robust preemptive goal programming method for marine reserve problem with eco-economy tradeoffs and multiple uncertainties</title><author>Jia, Ruru ; Gao, Jinwu ; Zou, Zezhou</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-d6daf3ccf4f4abd9d247de537253d999136dd5119975629b1e7f44d8419eec113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial Intelligence</topic><topic>Biodiversity</topic><topic>Case studies</topic><topic>Commercial fishing</topic><topic>Computational Intelligence</topic><topic>Conservation</topic><topic>Decision making</topic><topic>Engineering</topic><topic>Fisheries</topic><topic>Fishing</topic><topic>Goal programming</topic><topic>Marine resources</topic><topic>Mathematical models</topic><topic>Opportunity costs</topic><topic>Optimization</topic><topic>Original Research</topic><topic>Parameter sensitivity</topic><topic>Parameter uncertainty</topic><topic>Planning</topic><topic>Preempting</topic><topic>Robotics and Automation</topic><topic>Robustness</topic><topic>User Interfaces and Human Computer Interaction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jia, Ruru</creatorcontrib><creatorcontrib>Gao, Jinwu</creatorcontrib><creatorcontrib>Zou, Zezhou</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Journal of ambient intelligence and humanized computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jia, Ruru</au><au>Gao, Jinwu</au><au>Zou, Zezhou</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A globalized robust preemptive goal programming method for marine reserve problem with eco-economy tradeoffs and multiple uncertainties</atitle><jtitle>Journal of ambient intelligence and humanized computing</jtitle><stitle>J Ambient Intell Human Comput</stitle><date>2023-10-01</date><risdate>2023</risdate><volume>14</volume><issue>10</issue><spage>13375</spage><epage>13385</epage><pages>13375-13385</pages><issn>1868-5137</issn><eissn>1868-5145</eissn><abstract>“Blue growth” emphasizes the sustainable use of marine resources for economic process whereas conserving the health of marine ecosystems. Motivated by this, we focus on the marine reserve design problem under multiple uncertainties, incorporating conservation, economy, reserve area and reserve shape compactness. We develop a novel globalized robust preemptive goal programming model to investigate the problem, where inner-outer uncertainty sets are defined to model the uncertain conservation value and opportunity cost. We thereby derive the tractable globalized robust counterpart of the proposed model and perform the model analysis on the globalized sensitivity parameters. Our proposed optimization framework can be demonstrated for the marine reserve design of West Coast National Marine Park of Qingdao in China. The computational experiments justify several important insights: (i) the resulting reserve area design is more robust than the delimitation of 2014 on the West Coast New Area; (ii) with the change of parameters related to uncertainty sets and global sensitivity, the conservation value can always be realized while the opportunity cost is sensitive; (iii) the comparison results with classic robust optimization method show that the proposed method can reduce the conservatism of decisions and enable planners to formulate more effective policy.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12652-022-03792-2</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-4806-927X</orcidid></addata></record> |
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subjects | Artificial Intelligence Biodiversity Case studies Commercial fishing Computational Intelligence Conservation Decision making Engineering Fisheries Fishing Goal programming Marine resources Mathematical models Opportunity costs Optimization Original Research Parameter sensitivity Parameter uncertainty Planning Preempting Robotics and Automation Robustness User Interfaces and Human Computer Interaction |
title | A globalized robust preemptive goal programming method for marine reserve problem with eco-economy tradeoffs and multiple uncertainties |
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