{"title":"平衡旅行推销员问题的分支切割算法","authors":"Thi Quynh Trang Vo, Mourad Baiou, Viet Hung Nguyen","doi":"10.1007/s10878-023-01097-4","DOIUrl":null,"url":null,"abstract":"<p>The balanced traveling salesman problem (BTSP) is a variant of the traveling salesman problem, in which one seeks a tour that minimizes the difference between the largest and smallest edge costs in the tour. The BTSP, which is obviously NP-hard, was first investigated by Larusic and Punnen (Comput Oper Res 38(5):868–875, 2011). They proposed several heuristics based on the double-threshold framework, which converge to good-quality solutions though not always optimal. In this paper, we design a special-purpose branch-and-cut algorithm for exactly solving the BTSP. In contrast with the classical TSP, due to the BTSP’s objective function, the efficiency of algorithms for solving the BTSP depends heavily on determining correctly the largest and smallest edge costs in the tour. In the proposed branch-and-cut algorithm, we develop several mechanisms based on local cutting planes, edge elimination, and variable fixing to locate those edge costs more precisely. Other critical ingredients in our method are algorithms for initializing lower and upper bounds on the optimal value of the BTSP, which serve as warm starts for the branch-and-cut algorithm. Experiments on the same testbed of TSPLIB instances show that our algorithm can solve 63 out of 65 instances to proven optimality.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A branch-and-cut algorithm for the balanced traveling salesman problem\",\"authors\":\"Thi Quynh Trang Vo, Mourad Baiou, Viet Hung Nguyen\",\"doi\":\"10.1007/s10878-023-01097-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The balanced traveling salesman problem (BTSP) is a variant of the traveling salesman problem, in which one seeks a tour that minimizes the difference between the largest and smallest edge costs in the tour. The BTSP, which is obviously NP-hard, was first investigated by Larusic and Punnen (Comput Oper Res 38(5):868–875, 2011). They proposed several heuristics based on the double-threshold framework, which converge to good-quality solutions though not always optimal. In this paper, we design a special-purpose branch-and-cut algorithm for exactly solving the BTSP. In contrast with the classical TSP, due to the BTSP’s objective function, the efficiency of algorithms for solving the BTSP depends heavily on determining correctly the largest and smallest edge costs in the tour. In the proposed branch-and-cut algorithm, we develop several mechanisms based on local cutting planes, edge elimination, and variable fixing to locate those edge costs more precisely. Other critical ingredients in our method are algorithms for initializing lower and upper bounds on the optimal value of the BTSP, which serve as warm starts for the branch-and-cut algorithm. Experiments on the same testbed of TSPLIB instances show that our algorithm can solve 63 out of 65 instances to proven optimality.</p>\",\"PeriodicalId\":50231,\"journal\":{\"name\":\"Journal of Combinatorial Optimization\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-01-28\",\"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/s10878-023-01097-4\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Combinatorial Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10878-023-01097-4","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}
A branch-and-cut algorithm for the balanced traveling salesman problem
The balanced traveling salesman problem (BTSP) is a variant of the traveling salesman problem, in which one seeks a tour that minimizes the difference between the largest and smallest edge costs in the tour. The BTSP, which is obviously NP-hard, was first investigated by Larusic and Punnen (Comput Oper Res 38(5):868–875, 2011). They proposed several heuristics based on the double-threshold framework, which converge to good-quality solutions though not always optimal. In this paper, we design a special-purpose branch-and-cut algorithm for exactly solving the BTSP. In contrast with the classical TSP, due to the BTSP’s objective function, the efficiency of algorithms for solving the BTSP depends heavily on determining correctly the largest and smallest edge costs in the tour. In the proposed branch-and-cut algorithm, we develop several mechanisms based on local cutting planes, edge elimination, and variable fixing to locate those edge costs more precisely. Other critical ingredients in our method are algorithms for initializing lower and upper bounds on the optimal value of the BTSP, which serve as warm starts for the branch-and-cut algorithm. Experiments on the same testbed of TSPLIB instances show that our algorithm can solve 63 out of 65 instances to proven optimality.
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.