A dynamic simulation/optimization model for scheduling restoration of degraded military training lands
Intensive use of military vehicles on Department of Defense training installations causes deterioration in ground surface quality. Degraded lands restrict the scheduled training activities and jeopardize personnel and equipment safety. We present a simulation-optimization approach and develop a disc...
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Veröffentlicht in: | Journal of environmental management 2016-04, Vol.171, p.144-157 |
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container_title | Journal of environmental management |
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creator | Önal, Hayri Woodford, Philip Tweddale, Scott A. Westervelt, James D. Chen, Mengye Dissanayake, Sahan T.M. Pitois, Gauthier |
description | Intensive use of military vehicles on Department of Defense training installations causes deterioration in ground surface quality. Degraded lands restrict the scheduled training activities and jeopardize personnel and equipment safety. We present a simulation-optimization approach and develop a discrete dynamic optimization model to determine an optimum land restoration for a given training schedule and availability of financial resources to minimize the adverse effects of training on military lands. The model considers weather forecasts, scheduled maneuver exercises, and unique qualities and importance of the maneuver areas. An application of this approach to Fort Riley, Kansas, shows that: i) starting with natural conditions, the total amount of training damages would increase almost linearly and exceed a quarter of the training area and 228 gullies would be formed (mostly in the intensive training areas) if no restoration is carried out over 10 years; ii) assuming an initial state that resembles the present conditions, sustaining the landscape requires an annual restoration budget of $957 thousand; iii) targeting a uniform distribution of maneuver damages would increase the total damages and adversely affect the overall landscape quality, therefore a selective restoration strategy may be preferred; and iv) a proactive restoration strategy would be optimal where land degradations are repaired before they turn into more severe damages that are more expensive to repair and may pose a higher training risk. The last finding can be used as a rule-of-thumb for land restoration efforts in other installations with similar characteristics.
•We use a dynamic MIP to optimize rehabilitation of degraded military training lands.•The optimum solution suggests repairing damaged lands, rather than fixing gullies.•Minimization of the restoration cost leads to hot spots with excessive damage.•Imposing a uniform damage distribution across space increases the total damage.•Maintaining the status quo requires approximately $1 million annually. |
doi_str_mv | 10.1016/j.jenvman.2016.02.005 |
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•We use a dynamic MIP to optimize rehabilitation of degraded military training lands.•The optimum solution suggests repairing damaged lands, rather than fixing gullies.•Minimization of the restoration cost leads to hot spots with excessive damage.•Imposing a uniform damage distribution across space increases the total damage.•Maintaining the status quo requires approximately $1 million annually.</description><identifier>ISSN: 0301-4797</identifier><identifier>EISSN: 1095-8630</identifier><identifier>DOI: 10.1016/j.jenvman.2016.02.005</identifier><identifier>PMID: 26895721</identifier><identifier>CODEN: JEVMAW</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>adverse effects ; Conservation of Natural Resources ; Damage ; Degradation ; Environmental restoration ; human resources ; Humans ; Kansas ; Land ; Land damages ; Land degradation ; land restoration ; landscapes ; Maneuvers ; Mathematical models ; Military Facilities ; military lands ; Military training ; Models, Theoretical ; Optimization ; ravines ; Rehabilitation ; Restoration ; risk ; safety equipment ; Simulation ; surface quality ; Training ; United States ; weather</subject><ispartof>Journal of environmental management, 2016-04, Vol.171, p.144-157</ispartof><rights>2016 Elsevier Ltd</rights><rights>Copyright © 2016 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Academic Press Ltd. Apr 15, 2016</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c539t-1ef5c9b9e0d69ba35b846cab90376e11ca19d113a125177fd779ec74d2f8e2b13</citedby><cites>FETCH-LOGICAL-c539t-1ef5c9b9e0d69ba35b846cab90376e11ca19d113a125177fd779ec74d2f8e2b13</cites><orcidid>0000-0002-0307-1731</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0301479716300469$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26895721$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Önal, Hayri</creatorcontrib><creatorcontrib>Woodford, Philip</creatorcontrib><creatorcontrib>Tweddale, Scott A.</creatorcontrib><creatorcontrib>Westervelt, James D.</creatorcontrib><creatorcontrib>Chen, Mengye</creatorcontrib><creatorcontrib>Dissanayake, Sahan T.M.</creatorcontrib><creatorcontrib>Pitois, Gauthier</creatorcontrib><title>A dynamic simulation/optimization model for scheduling restoration of degraded military training lands</title><title>Journal of environmental management</title><addtitle>J Environ Manage</addtitle><description>Intensive use of military vehicles on Department of Defense training installations causes deterioration in ground surface quality. Degraded lands restrict the scheduled training activities and jeopardize personnel and equipment safety. We present a simulation-optimization approach and develop a discrete dynamic optimization model to determine an optimum land restoration for a given training schedule and availability of financial resources to minimize the adverse effects of training on military lands. The model considers weather forecasts, scheduled maneuver exercises, and unique qualities and importance of the maneuver areas. An application of this approach to Fort Riley, Kansas, shows that: i) starting with natural conditions, the total amount of training damages would increase almost linearly and exceed a quarter of the training area and 228 gullies would be formed (mostly in the intensive training areas) if no restoration is carried out over 10 years; ii) assuming an initial state that resembles the present conditions, sustaining the landscape requires an annual restoration budget of $957 thousand; iii) targeting a uniform distribution of maneuver damages would increase the total damages and adversely affect the overall landscape quality, therefore a selective restoration strategy may be preferred; and iv) a proactive restoration strategy would be optimal where land degradations are repaired before they turn into more severe damages that are more expensive to repair and may pose a higher training risk. The last finding can be used as a rule-of-thumb for land restoration efforts in other installations with similar characteristics.
•We use a dynamic MIP to optimize rehabilitation of degraded military training lands.•The optimum solution suggests repairing damaged lands, rather than fixing gullies.•Minimization of the restoration cost leads to hot spots with excessive damage.•Imposing a uniform damage distribution across space increases the total damage.•Maintaining the status quo requires approximately $1 million annually.</description><subject>adverse effects</subject><subject>Conservation of Natural Resources</subject><subject>Damage</subject><subject>Degradation</subject><subject>Environmental restoration</subject><subject>human resources</subject><subject>Humans</subject><subject>Kansas</subject><subject>Land</subject><subject>Land damages</subject><subject>Land degradation</subject><subject>land restoration</subject><subject>landscapes</subject><subject>Maneuvers</subject><subject>Mathematical models</subject><subject>Military Facilities</subject><subject>military lands</subject><subject>Military training</subject><subject>Models, Theoretical</subject><subject>Optimization</subject><subject>ravines</subject><subject>Rehabilitation</subject><subject>Restoration</subject><subject>risk</subject><subject>safety equipment</subject><subject>Simulation</subject><subject>surface quality</subject><subject>Training</subject><subject>United States</subject><subject>weather</subject><issn>0301-4797</issn><issn>1095-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkktr3TAQhUVpaG5v-hNaDN10Y0cjWZa1KiH0BYFsmrWQpXEqY1u3kh1If33l3tsuuklWw8A3D845hLwFWgGF5nKoBpwfJjNXLLcVZRWl4gXZAVWibBtOX5Id5RTKWip5Tl6nNFBKOQP5ipyzplVCMtiR_qpwj7OZvC2Sn9bRLD7Ml-Gw-Mn_-tMUU3A4Fn2IRbI_0K2jn--LiGkJ8QiEvnB4H41DV0x-9IuJj8USjZ83cjSzSxfkrDdjwjenuid3nz99v_5a3tx--XZ9dVNawdVSAvbCqk4hdY3qDBddWzfWdIpy2SCANaAcADfABEjZOykVWlk71rfIOuB78uG49xDDzzX_qCefLI75CQxr0tDypskaZIGeRKWiqs4H2megrWC1olQ-A5UgWcPlhr7_Dx3CGucsz0ZxUYPIhu2JOFI2hpQi9voQ_ZQV1kD1lgQ96FMS9JYETZnOSchz707b125C92_qr_UZ-HgEMPvx4DHqZD3OFp2PaBftgn_ixG_IsccG</recordid><startdate>20160415</startdate><enddate>20160415</enddate><creator>Önal, Hayri</creator><creator>Woodford, Philip</creator><creator>Tweddale, Scott A.</creator><creator>Westervelt, James D.</creator><creator>Chen, Mengye</creator><creator>Dissanayake, Sahan T.M.</creator><creator>Pitois, Gauthier</creator><general>Elsevier Ltd</general><general>Academic Press Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7SN</scope><scope>7ST</scope><scope>7UA</scope><scope>8BJ</scope><scope>C1K</scope><scope>F1W</scope><scope>FQK</scope><scope>H97</scope><scope>JBE</scope><scope>L.G</scope><scope>SOI</scope><scope>7X8</scope><scope>7SU</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-0307-1731</orcidid></search><sort><creationdate>20160415</creationdate><title>A dynamic simulation/optimization model for scheduling restoration of degraded military training lands</title><author>Önal, Hayri ; Woodford, Philip ; Tweddale, Scott A. ; Westervelt, James D. ; Chen, Mengye ; Dissanayake, Sahan T.M. ; Pitois, Gauthier</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c539t-1ef5c9b9e0d69ba35b846cab90376e11ca19d113a125177fd779ec74d2f8e2b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>adverse effects</topic><topic>Conservation of Natural Resources</topic><topic>Damage</topic><topic>Degradation</topic><topic>Environmental restoration</topic><topic>human resources</topic><topic>Humans</topic><topic>Kansas</topic><topic>Land</topic><topic>Land damages</topic><topic>Land degradation</topic><topic>land restoration</topic><topic>landscapes</topic><topic>Maneuvers</topic><topic>Mathematical models</topic><topic>Military Facilities</topic><topic>military lands</topic><topic>Military training</topic><topic>Models, Theoretical</topic><topic>Optimization</topic><topic>ravines</topic><topic>Rehabilitation</topic><topic>Restoration</topic><topic>risk</topic><topic>safety equipment</topic><topic>Simulation</topic><topic>surface quality</topic><topic>Training</topic><topic>United States</topic><topic>weather</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Önal, Hayri</creatorcontrib><creatorcontrib>Woodford, Philip</creatorcontrib><creatorcontrib>Tweddale, Scott A.</creatorcontrib><creatorcontrib>Westervelt, James D.</creatorcontrib><creatorcontrib>Chen, Mengye</creatorcontrib><creatorcontrib>Dissanayake, Sahan T.M.</creatorcontrib><creatorcontrib>Pitois, Gauthier</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>International Bibliography of the Social Sciences</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>International Bibliography of the Social Sciences</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Journal of environmental management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Önal, Hayri</au><au>Woodford, Philip</au><au>Tweddale, Scott A.</au><au>Westervelt, James D.</au><au>Chen, Mengye</au><au>Dissanayake, Sahan T.M.</au><au>Pitois, Gauthier</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A dynamic simulation/optimization model for scheduling restoration of degraded military training lands</atitle><jtitle>Journal of environmental management</jtitle><addtitle>J Environ Manage</addtitle><date>2016-04-15</date><risdate>2016</risdate><volume>171</volume><spage>144</spage><epage>157</epage><pages>144-157</pages><issn>0301-4797</issn><eissn>1095-8630</eissn><coden>JEVMAW</coden><abstract>Intensive use of military vehicles on Department of Defense training installations causes deterioration in ground surface quality. Degraded lands restrict the scheduled training activities and jeopardize personnel and equipment safety. We present a simulation-optimization approach and develop a discrete dynamic optimization model to determine an optimum land restoration for a given training schedule and availability of financial resources to minimize the adverse effects of training on military lands. The model considers weather forecasts, scheduled maneuver exercises, and unique qualities and importance of the maneuver areas. An application of this approach to Fort Riley, Kansas, shows that: i) starting with natural conditions, the total amount of training damages would increase almost linearly and exceed a quarter of the training area and 228 gullies would be formed (mostly in the intensive training areas) if no restoration is carried out over 10 years; ii) assuming an initial state that resembles the present conditions, sustaining the landscape requires an annual restoration budget of $957 thousand; iii) targeting a uniform distribution of maneuver damages would increase the total damages and adversely affect the overall landscape quality, therefore a selective restoration strategy may be preferred; and iv) a proactive restoration strategy would be optimal where land degradations are repaired before they turn into more severe damages that are more expensive to repair and may pose a higher training risk. The last finding can be used as a rule-of-thumb for land restoration efforts in other installations with similar characteristics.
•We use a dynamic MIP to optimize rehabilitation of degraded military training lands.•The optimum solution suggests repairing damaged lands, rather than fixing gullies.•Minimization of the restoration cost leads to hot spots with excessive damage.•Imposing a uniform damage distribution across space increases the total damage.•Maintaining the status quo requires approximately $1 million annually.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>26895721</pmid><doi>10.1016/j.jenvman.2016.02.005</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-0307-1731</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | adverse effects Conservation of Natural Resources Damage Degradation Environmental restoration human resources Humans Kansas Land Land damages Land degradation land restoration landscapes Maneuvers Mathematical models Military Facilities military lands Military training Models, Theoretical Optimization ravines Rehabilitation Restoration risk safety equipment Simulation surface quality Training United States weather |
title | A dynamic simulation/optimization model for scheduling restoration of degraded military training lands |
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