Resource Pooling and Cost Allocation Among Independent Service Providers
We study a situation where several independent service providers collaborate by complete pooling of their resources and customer streams into a joint service system. These service providers may represent such diverse organizations as hospitals that pool beds or maintenance firms that pool repairmen....
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Veröffentlicht in: | Operations research 2015-03, Vol.63 (2), p.476-488 |
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creator | Karsten, Frank Slikker, Marco van Houtum, Geert-Jan |
description | We study a situation where several independent service providers collaborate by complete pooling of their resources and customer streams into a joint service system. These service providers may represent such diverse organizations as hospitals that pool beds or maintenance firms that pool repairmen. We model the service systems as Erlang delay systems (
M/M/s
queues) that face a fixed cost rate per server and homogeneous delay costs for waiting customers. We examine rules to fairly allocate the collective costs of the pooled system amongst the participants by applying concepts from cooperative game theory. We consider both the case where players’ numbers of servers are exogenously given and the scenario where any coalition picks an optimal number of servers. By exploiting new analytical properties of the continuous extension of the classic Erlang delay function, we provide sufficient conditions for the games under consideration to possess a core allocation (i.e., an allocation that gives no group of players an incentive to split off and form a separate pool) and to admit a population monotonic allocation scheme (whereby adding extra players does not make anyone worse off). This is not guaranteed in general, as illustrated via examples. |
doi_str_mv | 10.1287/opre.2015.1360 |
format | Article |
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M/M/s
queues) that face a fixed cost rate per server and homogeneous delay costs for waiting customers. We examine rules to fairly allocate the collective costs of the pooled system amongst the participants by applying concepts from cooperative game theory. We consider both the case where players’ numbers of servers are exogenously given and the scenario where any coalition picks an optimal number of servers. By exploiting new analytical properties of the continuous extension of the classic Erlang delay function, we provide sufficient conditions for the games under consideration to possess a core allocation (i.e., an allocation that gives no group of players an incentive to split off and form a separate pool) and to admit a population monotonic allocation scheme (whereby adding extra players does not make anyone worse off). This is not guaranteed in general, as illustrated via examples.</description><identifier>ISSN: 0030-364X</identifier><identifier>EISSN: 1526-5463</identifier><identifier>DOI: 10.1287/opre.2015.1360</identifier><language>eng</language><publisher>Linthicum: INFORMS</publisher><subject>Analysis ; Cost allocation ; Customer services ; Customers ; Delay ; Game theory ; METHODS ; Operations research ; Players ; queueing theory ; Queuing theory ; Repair & maintenance services ; Resource allocation ; service operations ; Stochastic systems</subject><ispartof>Operations research, 2015-03, Vol.63 (2), p.476-488</ispartof><rights>2015 INFORMS</rights><rights>COPYRIGHT 2015 Institute for Operations Research and the Management Sciences</rights><rights>Copyright Institute for Operations Research and the Management Sciences Mar/Apr 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c573t-1da09af94e14176225c47593e7d4b63d432eead3543bc78c74e45c5441ce13da3</citedby><cites>FETCH-LOGICAL-c573t-1da09af94e14176225c47593e7d4b63d432eead3543bc78c74e45c5441ce13da3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/24540385$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://pubsonline.informs.org/doi/full/10.1287/opre.2015.1360$$EHTML$$P50$$Ginforms$$H</linktohtml><link.rule.ids>314,776,780,799,3679,27901,27902,57992,58225,62589</link.rule.ids></links><search><creatorcontrib>Karsten, Frank</creatorcontrib><creatorcontrib>Slikker, Marco</creatorcontrib><creatorcontrib>van Houtum, Geert-Jan</creatorcontrib><title>Resource Pooling and Cost Allocation Among Independent Service Providers</title><title>Operations research</title><description>We study a situation where several independent service providers collaborate by complete pooling of their resources and customer streams into a joint service system. These service providers may represent such diverse organizations as hospitals that pool beds or maintenance firms that pool repairmen. We model the service systems as Erlang delay systems (
M/M/s
queues) that face a fixed cost rate per server and homogeneous delay costs for waiting customers. We examine rules to fairly allocate the collective costs of the pooled system amongst the participants by applying concepts from cooperative game theory. We consider both the case where players’ numbers of servers are exogenously given and the scenario where any coalition picks an optimal number of servers. By exploiting new analytical properties of the continuous extension of the classic Erlang delay function, we provide sufficient conditions for the games under consideration to possess a core allocation (i.e., an allocation that gives no group of players an incentive to split off and form a separate pool) and to admit a population monotonic allocation scheme (whereby adding extra players does not make anyone worse off). This is not guaranteed in general, as illustrated via examples.</description><subject>Analysis</subject><subject>Cost allocation</subject><subject>Customer services</subject><subject>Customers</subject><subject>Delay</subject><subject>Game theory</subject><subject>METHODS</subject><subject>Operations research</subject><subject>Players</subject><subject>queueing theory</subject><subject>Queuing theory</subject><subject>Repair & maintenance services</subject><subject>Resource allocation</subject><subject>service operations</subject><subject>Stochastic systems</subject><issn>0030-364X</issn><issn>1526-5463</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><recordid>eNqFkd1rFDEUxYMouLa--iYM-Ops853dx2WxtlCo1BZ8C9nkzphlJllzZwv-92aoWIUFCdxA7u-c3Msh5B2jS8ZX5iIfCiw5ZWrJhKYvyIIprlsltXhJFpQK2gotv70mbxD3lNK10mpBru4A87F4aL7kPMTUNy6FZptxajbDkL2bYk7NZsy1c50CHKCWNDVfoTzGWVXyYwxQ8Jy86tyA8Pb3fUYeLj_db6_am9vP19vNTeuVEVPLgqNr160lMMmM5lx5adRagAlyp0WQggO4IJQUO29W3kiQyispmQcmghNn5MOT76HkH0fAye7r_Kl-abk2hiqm9eqZ6t0ANqYuT8X5MaK3G8k445QbWan2BNVDguKGnKCL9fkffnmCryfAGP1Jwce_BLsjxgRYC8b--4S9OyKe9PclIxbo7KHE0ZWfllE7Z2znjO2csZ0zroL3T4I9Trn8oblUkoqVel5wnrWM-D-_X8iTr5A</recordid><startdate>20150301</startdate><enddate>20150301</enddate><creator>Karsten, Frank</creator><creator>Slikker, Marco</creator><creator>van Houtum, Geert-Jan</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>XI7</scope><scope>JQ2</scope><scope>K9.</scope></search><sort><creationdate>20150301</creationdate><title>Resource Pooling and Cost Allocation Among Independent Service Providers</title><author>Karsten, Frank ; Slikker, Marco ; van Houtum, Geert-Jan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c573t-1da09af94e14176225c47593e7d4b63d432eead3543bc78c74e45c5441ce13da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Analysis</topic><topic>Cost allocation</topic><topic>Customer services</topic><topic>Customers</topic><topic>Delay</topic><topic>Game theory</topic><topic>METHODS</topic><topic>Operations research</topic><topic>Players</topic><topic>queueing theory</topic><topic>Queuing theory</topic><topic>Repair & maintenance services</topic><topic>Resource allocation</topic><topic>service operations</topic><topic>Stochastic systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karsten, Frank</creatorcontrib><creatorcontrib>Slikker, Marco</creatorcontrib><creatorcontrib>van Houtum, Geert-Jan</creatorcontrib><collection>CrossRef</collection><collection>Gale Business: Insights</collection><collection>Business Insights: Essentials</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><jtitle>Operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karsten, Frank</au><au>Slikker, Marco</au><au>van Houtum, Geert-Jan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Resource Pooling and Cost Allocation Among Independent Service Providers</atitle><jtitle>Operations research</jtitle><date>2015-03-01</date><risdate>2015</risdate><volume>63</volume><issue>2</issue><spage>476</spage><epage>488</epage><pages>476-488</pages><issn>0030-364X</issn><eissn>1526-5463</eissn><abstract>We study a situation where several independent service providers collaborate by complete pooling of their resources and customer streams into a joint service system. These service providers may represent such diverse organizations as hospitals that pool beds or maintenance firms that pool repairmen. We model the service systems as Erlang delay systems (
M/M/s
queues) that face a fixed cost rate per server and homogeneous delay costs for waiting customers. We examine rules to fairly allocate the collective costs of the pooled system amongst the participants by applying concepts from cooperative game theory. We consider both the case where players’ numbers of servers are exogenously given and the scenario where any coalition picks an optimal number of servers. By exploiting new analytical properties of the continuous extension of the classic Erlang delay function, we provide sufficient conditions for the games under consideration to possess a core allocation (i.e., an allocation that gives no group of players an incentive to split off and form a separate pool) and to admit a population monotonic allocation scheme (whereby adding extra players does not make anyone worse off). This is not guaranteed in general, as illustrated via examples.</abstract><cop>Linthicum</cop><pub>INFORMS</pub><doi>10.1287/opre.2015.1360</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Analysis Cost allocation Customer services Customers Delay Game theory METHODS Operations research Players queueing theory Queuing theory Repair & maintenance services Resource allocation service operations Stochastic systems |
title | Resource Pooling and Cost Allocation Among Independent Service Providers |
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