{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:21:45Z","timestamp":1764174105693,"version":"3.41.2"},"reference-count":28,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2012,8,17]],"date-time":"2012-08-17T00:00:00Z","timestamp":1345161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,8,17]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>The purpose of this paper is to realize a multi\u2010objective location\u2010routing network optimization in reverse logistics using particle swarm optimization based on grey relational analysis with entropy weight.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>Real world network design problems are often characterized by multi\u2010objective in reverse logistics. This has recently been considered as an additional objective for facility location problem or vehicle routing problem in reverse logistics network design. Both of them are shown to be NP\u2010hard. Hence, location\u2010routing problem (LRP) with multi\u2010objective is more complicated integrated problem, and it is NP\u2010hard too. Due to the fact that NP\u2010hard model cannot be solved directly, grey relational analysis and entropy weight were added to particle swarm optimization to decision among the objectives. Then, a mathematics model about multi\u2010objective LRP of reverse logistics has been constructed, and a proposed hybrid particle swarm optimization with grey relational analysis and entropy weight has been developed to resolve it. An example is also computed in the last part of the paper.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>The results are convincing: not only that particle swarm optimization and grey relational analysis can be used to resolve multi\u2010objective location\u2010routing model, but also that entropy and grey relational analysis can be combined to decide weights of objectives.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Practical implications<\/jats:title><jats:p>The method exposed in the paper can be used to deal with multi\u2010objective LRP in reverse logistics, and multi\u2010objective network optimization result could be helpful for logistics efficiency and practicability.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper succeeds in realising both a constructed multi\u2010objective model about location\u2010routing of reverse logistics and a multi\u2010objective solution algorithm about particle swarm optimization and future stage by using one of the newest developed theories: grey relational analysis.<\/jats:p><\/jats:sec>","DOI":"10.1108\/20439371211260216","type":"journal-article","created":{"date-parts":[[2014,11,13]],"date-time":"2014-11-13T12:10:36Z","timestamp":1415880636000},"page":"249-258","source":"Crossref","is-referenced-by-count":21,"title":["Multi\u2010objective location\u2010routing problem of reverse logistics based on GRA with entropy weight"],"prefix":"10.1108","volume":"2","author":[{"given":"Hong","family":"Liu","sequence":"first","affiliation":[]},{"given":"Wenping","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Qishan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022020520264638900_b1","doi-asserted-by":"crossref","unstructured":"Caramia, M. and Guerriero, F. 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