The MIP-Based Large Neighborhood Local Search Method for Large-Scale Optimization Problems with Many Constraints: Application to the Machining Scheduling
{"title":"The MIP-Based Large Neighborhood Local Search Method for Large-Scale Optimization Problems with Many Constraints: Application to the Machining Scheduling","authors":"Jin Matsuzaki, K. Sakakibara, Masaki Nakamura","doi":"10.1109/ICMLC56445.2022.9941310","DOIUrl":null,"url":null,"abstract":"This paper addresses the problem of scheduling machining operations in a highly automated manufacturing environment, taking into account the work styles of workers. In actual manufacturing, many issues must be taken into accounts, such as constraints related to the works to be machined in the machining schedule and the conditions of workers. To derive good solutions to such a large-scale problem with many constraints in a realistic amount of computing time, we develop an optimization technique based on the MIP-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments are conducted on a problem created concerning actual machining requirements to verify the validity of the proposed method.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC56445.2022.9941310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper addresses the problem of scheduling machining operations in a highly automated manufacturing environment, taking into account the work styles of workers. In actual manufacturing, many issues must be taken into accounts, such as constraints related to the works to be machined in the machining schedule and the conditions of workers. To derive good solutions to such a large-scale problem with many constraints in a realistic amount of computing time, we develop an optimization technique based on the MIP-based large neighborhood local search method for the machining scheduling problem. Then, computer experiments are conducted on a problem created concerning actual machining requirements to verify the validity of the proposed method.