Optimal Scheduling of Logistical Support for Medical Resource with Demand Information Updating
This paper presents a discrete time-space network model for a dynamic resource allocation problem following an epidemic outbreak in a region. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal...
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Veröffentlicht in: | Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-12 |
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description | This paper presents a discrete time-space network model for a dynamic resource allocation problem following an epidemic outbreak in a region. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. A custom genetic algorithm is adopted to solve the proposed model, and a numerical example is presented for sensitivity analysis of the parameters. We compare the proposed medical resource allocation mode with two traditional operation modes in practice and find that our model is superior to any of them in less waste of resource and less logistic cost. The results may provide some practical guidelines for a decision-maker who is in charge of medical resource allocation in an epidemics control effort. |
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It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. A custom genetic algorithm is adopted to solve the proposed model, and a numerical example is presented for sensitivity analysis of the parameters. We compare the proposed medical resource allocation mode with two traditional operation modes in practice and find that our model is superior to any of them in less waste of resource and less logistic cost. The results may provide some practical guidelines for a decision-maker who is in charge of medical resource allocation in an epidemics control effort.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2015/765098</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Computer simulation ; Cost analysis ; Decision making ; Demand ; Demand analysis ; Disease control ; Dynamics ; Efficiency ; Epidemics ; Genetic algorithms ; Immunization ; Impact analysis ; Linear programming ; Literature reviews ; Logistics ; Management science ; Mathematical models ; Mathematical problems ; Medical ; Medical wastes ; Monte Carlo simulation ; Optimization ; Ordinary differential equations ; Parameter sensitivity ; Recursion ; Resource allocation ; Resource scheduling ; Sensitivity analysis ; Spreads ; Swine flu ; Traveling salesman problem</subject><ispartof>Mathematical problems in engineering, 2015-01, Vol.2015 (2015), p.1-12</ispartof><rights>Copyright © 2015 Ming Liu and Yihong Xiao.</rights><rights>Copyright © 2015 Ming Liu and Yihong Xiao. Ming Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-5275210d44478579873669dd4defd0f74e566861d2a25c9ce2f0a28a985f4f8e3</citedby><cites>FETCH-LOGICAL-c389t-5275210d44478579873669dd4defd0f74e566861d2a25c9ce2f0a28a985f4f8e3</cites><orcidid>0000-0002-0945-3888 ; 0000-0001-7750-8919</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Lütjen, Michael</contributor><creatorcontrib>Liu, Ming</creatorcontrib><creatorcontrib>Xiao, Yihong</creatorcontrib><title>Optimal Scheduling of Logistical Support for Medical Resource with Demand Information Updating</title><title>Mathematical problems in engineering</title><description>This paper presents a discrete time-space network model for a dynamic resource allocation problem following an epidemic outbreak in a region. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. A custom genetic algorithm is adopted to solve the proposed model, and a numerical example is presented for sensitivity analysis of the parameters. We compare the proposed medical resource allocation mode with two traditional operation modes in practice and find that our model is superior to any of them in less waste of resource and less logistic cost. The results may provide some practical guidelines for a decision-maker who is in charge of medical resource allocation in an epidemics control effort.</description><subject>Computer simulation</subject><subject>Cost analysis</subject><subject>Decision making</subject><subject>Demand</subject><subject>Demand analysis</subject><subject>Disease control</subject><subject>Dynamics</subject><subject>Efficiency</subject><subject>Epidemics</subject><subject>Genetic algorithms</subject><subject>Immunization</subject><subject>Impact analysis</subject><subject>Linear programming</subject><subject>Literature reviews</subject><subject>Logistics</subject><subject>Management science</subject><subject>Mathematical models</subject><subject>Mathematical problems</subject><subject>Medical</subject><subject>Medical wastes</subject><subject>Monte Carlo simulation</subject><subject>Optimization</subject><subject>Ordinary differential equations</subject><subject>Parameter sensitivity</subject><subject>Recursion</subject><subject>Resource allocation</subject><subject>Resource scheduling</subject><subject>Sensitivity analysis</subject><subject>Spreads</subject><subject>Swine flu</subject><subject>Traveling salesman problem</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0M1LwzAYBvAgCs7pybsEvIhSl6T56lHm12AyUAeeLKFJtoy2qU3L8L83sx7Ei6e8JD8e8j4AnGJ0jTFjE4IwmwjOUCb3wAgzniYMU7EfZ0Rogkn6dgiOQtggRDDDcgTeF03nKlXCl2JtdF-6egW9hXO_cqFzxe6hbxrfdtD6Fj4Z_X33bILv28LArevW8NZUqtZwVkdSqc75Gi4bHYd6dQwOrCqDOfk5x2B5f_c6fUzmi4fZ9GaeFKnMuoQRwQhGmlIqJBOZFCnnmdZUG6uRFdQwziXHmijCiqwwxCJFpMoks9RKk47BxZDbtP6jN6HLKxcKU5aqNr4POeYxNiZQGen5H7qJu9Txd1FxkWKBKYrqalBF60Nojc2bNvbUfuYY5buu813X-dB11JeDXrtaq637B58NONYWg9UvLCgXOP0CqZ-G6Q</recordid><startdate>20150101</startdate><enddate>20150101</enddate><creator>Liu, Ming</creator><creator>Xiao, Yihong</creator><general>Hindawi Publishing Corporation</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-0945-3888</orcidid><orcidid>https://orcid.org/0000-0001-7750-8919</orcidid></search><sort><creationdate>20150101</creationdate><title>Optimal Scheduling of Logistical Support for Medical Resource with Demand Information Updating</title><author>Liu, Ming ; Xiao, Yihong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-5275210d44478579873669dd4defd0f74e566861d2a25c9ce2f0a28a985f4f8e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Computer simulation</topic><topic>Cost analysis</topic><topic>Decision making</topic><topic>Demand</topic><topic>Demand analysis</topic><topic>Disease control</topic><topic>Dynamics</topic><topic>Efficiency</topic><topic>Epidemics</topic><topic>Genetic algorithms</topic><topic>Immunization</topic><topic>Impact analysis</topic><topic>Linear programming</topic><topic>Literature reviews</topic><topic>Logistics</topic><topic>Management science</topic><topic>Mathematical models</topic><topic>Mathematical problems</topic><topic>Medical</topic><topic>Medical wastes</topic><topic>Monte Carlo simulation</topic><topic>Optimization</topic><topic>Ordinary differential equations</topic><topic>Parameter sensitivity</topic><topic>Recursion</topic><topic>Resource allocation</topic><topic>Resource scheduling</topic><topic>Sensitivity analysis</topic><topic>Spreads</topic><topic>Swine flu</topic><topic>Traveling salesman problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Ming</creatorcontrib><creatorcontrib>Xiao, Yihong</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Ming</au><au>Xiao, Yihong</au><au>Lütjen, Michael</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Scheduling of Logistical Support for Medical Resource with Demand Information Updating</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2015-01-01</date><risdate>2015</risdate><volume>2015</volume><issue>2015</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>This paper presents a discrete time-space network model for a dynamic resource allocation problem following an epidemic outbreak in a region. It couples a forecasting mechanism for dynamic demand of medical resource based on an epidemic diffusion model and a multistage programming model for optimal allocation and transport of such resource. At each stage, the linear programming solves for a cost minimizing resource allocation solution subject to a time-varying demand that is forecasted by a recursion model. The rationale that the medical resource allocated in early periods will take effect in subduing the spread of epidemic and thus impact the demand in later periods has been incorporated in such recursion model. A custom genetic algorithm is adopted to solve the proposed model, and a numerical example is presented for sensitivity analysis of the parameters. We compare the proposed medical resource allocation mode with two traditional operation modes in practice and find that our model is superior to any of them in less waste of resource and less logistic cost. 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subjects | Computer simulation Cost analysis Decision making Demand Demand analysis Disease control Dynamics Efficiency Epidemics Genetic algorithms Immunization Impact analysis Linear programming Literature reviews Logistics Management science Mathematical models Mathematical problems Medical Medical wastes Monte Carlo simulation Optimization Ordinary differential equations Parameter sensitivity Recursion Resource allocation Resource scheduling Sensitivity analysis Spreads Swine flu Traveling salesman problem |
title | Optimal Scheduling of Logistical Support for Medical Resource with Demand Information Updating |
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