Bo Sun, Lin Yang, Mohammad Hajiesmaili, Adam Wierman, John C.S. Lui, Don Towsley, Danny H.K. Tsang
{"title":"带出发的在线背包问题","authors":"Bo Sun, Lin Yang, Mohammad Hajiesmaili, Adam Wierman, John C.S. Lui, Don Towsley, Danny H.K. Tsang","doi":"10.1145/3606376.3593576","DOIUrl":null,"url":null,"abstract":"The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of different sizes and values into a capacity-limited knapsack. In this paper, we study a general version that includes item departures, while also considering multiple knapsacks and multi-dimensional item sizes. We design a threshold-based online algorithm and prove that the algorithm can achieve order-optimal competitive ratios. Beyond worst-case optimized algorithms, we also propose a data-driven online algorithm that can achieve near-optimal average performance under typical instances while guaranteeing the worst-case performance.","PeriodicalId":35745,"journal":{"name":"Performance Evaluation Review","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Online Knapsack Problem with Departures\",\"authors\":\"Bo Sun, Lin Yang, Mohammad Hajiesmaili, Adam Wierman, John C.S. Lui, Don Towsley, Danny H.K. Tsang\",\"doi\":\"10.1145/3606376.3593576\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of different sizes and values into a capacity-limited knapsack. In this paper, we study a general version that includes item departures, while also considering multiple knapsacks and multi-dimensional item sizes. We design a threshold-based online algorithm and prove that the algorithm can achieve order-optimal competitive ratios. Beyond worst-case optimized algorithms, we also propose a data-driven online algorithm that can achieve near-optimal average performance under typical instances while guaranteeing the worst-case performance.\",\"PeriodicalId\":35745,\"journal\":{\"name\":\"Performance Evaluation Review\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Performance Evaluation Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3606376.3593576\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Computer Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Performance Evaluation Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3606376.3593576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Computer Science","Score":null,"Total":0}
The online knapsack problem is a classic online resource allocation problem in networking and operations research. Its basic version studies how to pack online arriving items of different sizes and values into a capacity-limited knapsack. In this paper, we study a general version that includes item departures, while also considering multiple knapsacks and multi-dimensional item sizes. We design a threshold-based online algorithm and prove that the algorithm can achieve order-optimal competitive ratios. Beyond worst-case optimized algorithms, we also propose a data-driven online algorithm that can achieve near-optimal average performance under typical instances while guaranteeing the worst-case performance.
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