{"title":"Approximation algorithm for dynamic facility location problem","authors":"Li Zhang, Qiaoliang Li","doi":"10.1007/s10878-025-01282-7","DOIUrl":null,"url":null,"abstract":"<p>In this paper, we consider dynamic facility location problem with unit demand (DFLPUD). We propose a 1.52-approximation algorithm that skillfully integrates dual-fitting and greedy augmentation schemes. Our algorithmic framework begins by formulating DFLPUD as a set covering linear integer programming problem. Then we scale the opening cost of all facilities and use the solution of dual-fitting algorithm to seed a local search to yield an improved performance guarantee 1.52. To the best of our knowledge, this is the best known approximation ratio for DFLPUD.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"117 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Combinatorial Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10878-025-01282-7","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we consider dynamic facility location problem with unit demand (DFLPUD). We propose a 1.52-approximation algorithm that skillfully integrates dual-fitting and greedy augmentation schemes. Our algorithmic framework begins by formulating DFLPUD as a set covering linear integer programming problem. Then we scale the opening cost of all facilities and use the solution of dual-fitting algorithm to seed a local search to yield an improved performance guarantee 1.52. To the best of our knowledge, this is the best known approximation ratio for DFLPUD.
期刊介绍：
The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering.
The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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