{"title":"A model generating framework for regional waste management taking local peculiarities explicitly into account","authors":"Avraam Karagiannidis, Nicolas Moussiopoulos","doi":"10.1016/S0966-8349(98)00052-7","DOIUrl":null,"url":null,"abstract":"<div><p>Presented here is the axiomatic foundation of a model-generating framework for formulating location–allocation models in the field of integrated regional solid waste management. Data requirements are standardized, a generalized network objective function is developed and a set of potential constraint menu is compiled. Along the framework development, many local peculiarities are considered; resulting mixed-integer, linear models are solvable by exact or heuristic algorithms. These models are adaptable to each criterion of a customized set, thus supporting multicriterial analysis.</p></div>","PeriodicalId":100880,"journal":{"name":"Location Science","volume":"6 1","pages":"Pages 281-305"},"PeriodicalIF":0.0000,"publicationDate":"1998-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/S0966-8349(98)00052-7","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Location Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0966834998000527","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38
Abstract
Presented here is the axiomatic foundation of a model-generating framework for formulating location–allocation models in the field of integrated regional solid waste management. Data requirements are standardized, a generalized network objective function is developed and a set of potential constraint menu is compiled. Along the framework development, many local peculiarities are considered; resulting mixed-integer, linear models are solvable by exact or heuristic algorithms. These models are adaptable to each criterion of a customized set, thus supporting multicriterial analysis.
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