{"title":"带时间窗口的取货问题中最小化车队规模的并行算法","authors":"Miroslaw Blocho, J. Nalepa","doi":"10.1145/2802658.2802673","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a parallel guided ejection search algorithm to minimize the fleet size in the NP-hard pickup and delivery problem with time windows. The parallel processes co-operate periodically to enhance the quality of results and to accelerate the convergence of computations. The experimental study shows that the parallel algorithm retrieves very high-quality results. Finally, we report 13 (22% of all considered benchmark tests) new world's best solutions.","PeriodicalId":365272,"journal":{"name":"Proceedings of the 22nd European MPI Users' Group Meeting","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"A Parallel Algorithm for Minimizing the Fleet Size in the Pickup and Delivery Problem with Time Windows\",\"authors\":\"Miroslaw Blocho, J. Nalepa\",\"doi\":\"10.1145/2802658.2802673\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a parallel guided ejection search algorithm to minimize the fleet size in the NP-hard pickup and delivery problem with time windows. The parallel processes co-operate periodically to enhance the quality of results and to accelerate the convergence of computations. The experimental study shows that the parallel algorithm retrieves very high-quality results. Finally, we report 13 (22% of all considered benchmark tests) new world's best solutions.\",\"PeriodicalId\":365272,\"journal\":{\"name\":\"Proceedings of the 22nd European MPI Users' Group Meeting\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 22nd European MPI Users' Group Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2802658.2802673\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 22nd European MPI Users' Group Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2802658.2802673","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Parallel Algorithm for Minimizing the Fleet Size in the Pickup and Delivery Problem with Time Windows
In this paper, we propose a parallel guided ejection search algorithm to minimize the fleet size in the NP-hard pickup and delivery problem with time windows. The parallel processes co-operate periodically to enhance the quality of results and to accelerate the convergence of computations. The experimental study shows that the parallel algorithm retrieves very high-quality results. Finally, we report 13 (22% of all considered benchmark tests) new world's best solutions.
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