D. Ramin, E. Leo, L. Nicolosi, S. Spinelli, A. Brusaferri
{"title":"基于pso的先进电厂多提升机统一调度方法","authors":"D. Ramin, E. Leo, L. Nicolosi, S. Spinelli, A. Brusaferri","doi":"10.1109/CoDIT49905.2020.9263919","DOIUrl":null,"url":null,"abstract":"In this work, we propose a method based on the unified particle swarm optimization (UPSO) for no-wait multi-hoist scheduling, including a collision avoidance heuristic. Conflicts due to track sharing between hoists and no-wait constraints represent major issues to be addressed. Consequently, a complex optimization problem has to be solved dynamically, to identify the best operating strategy to be executed depending on the characteristics of the current job list. A decomposition procedure has been developed to speed up the solution of the large-scale optimization problem at hand. The proposed approach is demonstrated on a real galvanic process layout, showing the improved performances achieved by the proposed heuristic compared to the monolithic approach.","PeriodicalId":355781,"journal":{"name":"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Unified PSO-based method for multi-hoist scheduling in advanced Galvanic plants\",\"authors\":\"D. Ramin, E. Leo, L. Nicolosi, S. Spinelli, A. Brusaferri\",\"doi\":\"10.1109/CoDIT49905.2020.9263919\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we propose a method based on the unified particle swarm optimization (UPSO) for no-wait multi-hoist scheduling, including a collision avoidance heuristic. Conflicts due to track sharing between hoists and no-wait constraints represent major issues to be addressed. Consequently, a complex optimization problem has to be solved dynamically, to identify the best operating strategy to be executed depending on the characteristics of the current job list. A decomposition procedure has been developed to speed up the solution of the large-scale optimization problem at hand. The proposed approach is demonstrated on a real galvanic process layout, showing the improved performances achieved by the proposed heuristic compared to the monolithic approach.\",\"PeriodicalId\":355781,\"journal\":{\"name\":\"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CoDIT49905.2020.9263919\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CoDIT49905.2020.9263919","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Unified PSO-based method for multi-hoist scheduling in advanced Galvanic plants
In this work, we propose a method based on the unified particle swarm optimization (UPSO) for no-wait multi-hoist scheduling, including a collision avoidance heuristic. Conflicts due to track sharing between hoists and no-wait constraints represent major issues to be addressed. Consequently, a complex optimization problem has to be solved dynamically, to identify the best operating strategy to be executed depending on the characteristics of the current job list. A decomposition procedure has been developed to speed up the solution of the large-scale optimization problem at hand. The proposed approach is demonstrated on a real galvanic process layout, showing the improved performances achieved by the proposed heuristic compared to the monolithic approach.