{"title":"混合装配线平衡问题的改进果蝇优化算法","authors":"XiaoYu Niu, Xiwang Guo, Jiacun Wang, Shujin Qin, Liang Qi, ChenYang Fan","doi":"10.1109/WOCC58016.2023.10139674","DOIUrl":null,"url":null,"abstract":"The study of the disassembly line balancing problem (DLBP) aims to schedule optimally disassembly tasks across all workstations. Since existing research mainly focuses on specific layouts of disassembly lines, which may not be suitable for multi-product scenarios, this work proposes a hybrid DLBP and a linear programming model based on AND/OR graph establishes to maximize disassembly profit in this paper. The proposed model is solved by using CPLEX and an improved fruit fly optimization algorithm in various cases, and the correctness of the model is verified. Furthermore, the convergence of the improved fruit fly optimization algorithm is compared with a genetic algorithm to validate its effectiveness.","PeriodicalId":226792,"journal":{"name":"2023 32nd Wireless and Optical Communications Conference (WOCC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Improved Fruit Fly Optimization Algorithm for Hybrid Disassembly Line Balancing Problem\",\"authors\":\"XiaoYu Niu, Xiwang Guo, Jiacun Wang, Shujin Qin, Liang Qi, ChenYang Fan\",\"doi\":\"10.1109/WOCC58016.2023.10139674\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The study of the disassembly line balancing problem (DLBP) aims to schedule optimally disassembly tasks across all workstations. Since existing research mainly focuses on specific layouts of disassembly lines, which may not be suitable for multi-product scenarios, this work proposes a hybrid DLBP and a linear programming model based on AND/OR graph establishes to maximize disassembly profit in this paper. The proposed model is solved by using CPLEX and an improved fruit fly optimization algorithm in various cases, and the correctness of the model is verified. Furthermore, the convergence of the improved fruit fly optimization algorithm is compared with a genetic algorithm to validate its effectiveness.\",\"PeriodicalId\":226792,\"journal\":{\"name\":\"2023 32nd Wireless and Optical Communications Conference (WOCC)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 32nd Wireless and Optical Communications Conference (WOCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WOCC58016.2023.10139674\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 32nd Wireless and Optical Communications Conference (WOCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WOCC58016.2023.10139674","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Improved Fruit Fly Optimization Algorithm for Hybrid Disassembly Line Balancing Problem
The study of the disassembly line balancing problem (DLBP) aims to schedule optimally disassembly tasks across all workstations. Since existing research mainly focuses on specific layouts of disassembly lines, which may not be suitable for multi-product scenarios, this work proposes a hybrid DLBP and a linear programming model based on AND/OR graph establishes to maximize disassembly profit in this paper. The proposed model is solved by using CPLEX and an improved fruit fly optimization algorithm in various cases, and the correctness of the model is verified. Furthermore, the convergence of the improved fruit fly optimization algorithm is compared with a genetic algorithm to validate its effectiveness.