Formulation and solution of a two-stage capacitated facility location problem with multilevel capacities
In this paper, the multi-product facility location problem in a two-stage supply chain is investigated. In this problem, the locations of depots (distribution centres) need to be determined along with their corresponding capacities. Moreover, the product flows from the plants to depots and onto cust...
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Veröffentlicht in: | Annals of operations research 2019, Vol.272 (1-2), p.41-67 |
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description | In this paper, the multi-product facility location problem in a two-stage supply chain is investigated. In this problem, the locations of depots (distribution centres) need to be determined along with their corresponding capacities. Moreover, the product flows from the plants to depots and onto customers must also be optimised. Here, plants have a production limit whereas potential depots have several possible capacity levels to choose from, which are defined as multilevel capacities. Plants must serve customer demands via depots. Two integer linear programming (ILP) models are introduced to solve the problem in order to minimise the fixed costs of opening depots and transportation costs. In the first model, the depot capacity is based on the maximum number of each product that can be stored whereas in the second one, the capacity is determined by the size (volume) of the depot. For large problems, the models are very difficult to solve using an exact method. Therefore, a matheuristic approach based on an aggregation approach and an exact method (ILP) is proposed in order to solve such problems. The methods are assessed using randomly generated data sets and existing data sets taken from the literature. The solutions obtained from the computational study confirm the effectiveness of the proposed matheuristic approach which outperforms the exact method. In addition, a case study arising from the wind energy sector in the UK is presented. |
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In this problem, the locations of depots (distribution centres) need to be determined along with their corresponding capacities. Moreover, the product flows from the plants to depots and onto customers must also be optimised. Here, plants have a production limit whereas potential depots have several possible capacity levels to choose from, which are defined as multilevel capacities. Plants must serve customer demands via depots. Two integer linear programming (ILP) models are introduced to solve the problem in order to minimise the fixed costs of opening depots and transportation costs. In the first model, the depot capacity is based on the maximum number of each product that can be stored whereas in the second one, the capacity is determined by the size (volume) of the depot. For large problems, the models are very difficult to solve using an exact method. Therefore, a matheuristic approach based on an aggregation approach and an exact method (ILP) is proposed in order to solve such problems. The methods are assessed using randomly generated data sets and existing data sets taken from the literature. The solutions obtained from the computational study confirm the effectiveness of the proposed matheuristic approach which outperforms the exact method. In addition, a case study arising from the wind energy sector in the UK is presented.</description><identifier>ISSN: 0254-5330</identifier><identifier>EISSN: 1572-9338</identifier><identifier>DOI: 10.1007/s10479-017-2741-7</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Business and Management ; Business logistics ; Combinatorics ; Datasets ; Distribution centers ; Integer programming ; Linear programming ; Methods ; Operations research ; Operations Research/Decision Theory ; Optimization theory ; S.I.: Advances in Theoretical and Applied Combinatorial Optimization ; Site selection ; Supply chains ; Theory of Computation ; Wind power</subject><ispartof>Annals of operations research, 2019, Vol.272 (1-2), p.41-67</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>COPYRIGHT 2019 Springer</rights><rights>Annals of Operations Research is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c463t-86a2ce15265ed91679426ccf936d6cf7c2d41e367c8f4b2d24753f6ee3e4913d3</citedby><cites>FETCH-LOGICAL-c463t-86a2ce15265ed91679426ccf936d6cf7c2d41e367c8f4b2d24753f6ee3e4913d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10479-017-2741-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10479-017-2741-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Irawan, Chandra Ade</creatorcontrib><creatorcontrib>Jones, Dylan</creatorcontrib><title>Formulation and solution of a two-stage capacitated facility location problem with multilevel capacities</title><title>Annals of operations research</title><addtitle>Ann Oper Res</addtitle><description>In this paper, the multi-product facility location problem in a two-stage supply chain is investigated. In this problem, the locations of depots (distribution centres) need to be determined along with their corresponding capacities. Moreover, the product flows from the plants to depots and onto customers must also be optimised. Here, plants have a production limit whereas potential depots have several possible capacity levels to choose from, which are defined as multilevel capacities. Plants must serve customer demands via depots. Two integer linear programming (ILP) models are introduced to solve the problem in order to minimise the fixed costs of opening depots and transportation costs. In the first model, the depot capacity is based on the maximum number of each product that can be stored whereas in the second one, the capacity is determined by the size (volume) of the depot. For large problems, the models are very difficult to solve using an exact method. Therefore, a matheuristic approach based on an aggregation approach and an exact method (ILP) is proposed in order to solve such problems. The methods are assessed using randomly generated data sets and existing data sets taken from the literature. The solutions obtained from the computational study confirm the effectiveness of the proposed matheuristic approach which outperforms the exact method. In addition, a case study arising from the wind energy sector in the UK is presented.</description><subject>Business and Management</subject><subject>Business logistics</subject><subject>Combinatorics</subject><subject>Datasets</subject><subject>Distribution centers</subject><subject>Integer programming</subject><subject>Linear programming</subject><subject>Methods</subject><subject>Operations research</subject><subject>Operations Research/Decision Theory</subject><subject>Optimization theory</subject><subject>S.I.: Advances in Theoretical and Applied Combinatorial Optimization</subject><subject>Site selection</subject><subject>Supply chains</subject><subject>Theory of Computation</subject><subject>Wind power</subject><issn>0254-5330</issn><issn>1572-9338</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kU1rFTEUhgdR8Fr9Ae4Cbk2b70yWpdgqFLrRdcjNnMxNyUyuk4yl_95cR7EFJZAPeJ6Tc3i77j0l55QQfVEoEdpgQjVmWlCsX3Q7KjXDhvP-ZbcjTAosOSevuzel3BNCKO3lrjtc52Vak6sxz8jNAyo5rb8eOSCH6kPGpboRkHdH52N1FQYU2i3F-ohS9pt5XPI-wYQeYj2gVq_GBD8g_bEilLfdq-BSgXe_z7Pu2_Wnr1ef8e3dzZery1vsheIV98oxD1QyJWEwVGkjmPI-GK4G5YP2bBAUuNK-D2LPBia05EEBcBCG8oGfdR-2uq2l7yuUau_zusztS0uN4bSnjOu_1OgS2DiHXBfnp1i8vZSqJ6I3Sjbq_B9UWwNM0ecZQhvzufDxibBfS5yhtK3E8VDL6NZSnuN0w_2SS1kg2OMSJ7c8WkrsKVa7xWpbrPYUqz11zjanNHYeYXky33-lny8dpOg</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Irawan, Chandra Ade</creator><creator>Jones, Dylan</creator><general>Springer US</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>3V.</scope><scope>7TA</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JG9</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>KR7</scope><scope>L.-</scope><scope>L6V</scope><scope>M0C</scope><scope>M0N</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>2019</creationdate><title>Formulation and solution of a two-stage capacitated facility location problem with multilevel capacities</title><author>Irawan, Chandra Ade ; 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In this problem, the locations of depots (distribution centres) need to be determined along with their corresponding capacities. Moreover, the product flows from the plants to depots and onto customers must also be optimised. Here, plants have a production limit whereas potential depots have several possible capacity levels to choose from, which are defined as multilevel capacities. Plants must serve customer demands via depots. Two integer linear programming (ILP) models are introduced to solve the problem in order to minimise the fixed costs of opening depots and transportation costs. In the first model, the depot capacity is based on the maximum number of each product that can be stored whereas in the second one, the capacity is determined by the size (volume) of the depot. For large problems, the models are very difficult to solve using an exact method. Therefore, a matheuristic approach based on an aggregation approach and an exact method (ILP) is proposed in order to solve such problems. The methods are assessed using randomly generated data sets and existing data sets taken from the literature. The solutions obtained from the computational study confirm the effectiveness of the proposed matheuristic approach which outperforms the exact method. In addition, a case study arising from the wind energy sector in the UK is presented.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10479-017-2741-7</doi><tpages>27</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Business and Management Business logistics Combinatorics Datasets Distribution centers Integer programming Linear programming Methods Operations research Operations Research/Decision Theory Optimization theory S.I.: Advances in Theoretical and Applied Combinatorial Optimization Site selection Supply chains Theory of Computation Wind power |
title | Formulation and solution of a two-stage capacitated facility location problem with multilevel capacities |
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