T. A. Castillo, C. E. Díaz B., J. D. Gomez, E. Orduz, M. Niño
{"title":"Optimización del makespan en el problema de Job Shop Flexible con restricciones de transporte usando Algoritmos Genéticos","authors":"T. A. Castillo, C. E. Díaz B., J. D. Gomez, E. Orduz, M. Niño","doi":"10.31908/19098367.3820","DOIUrl":null,"url":null,"abstract":"We solved the Flexible Job Shop Scheduling Problem (FJSSP) with transportation constraints in order to minimize the makespan by sequencing and assigning machines. A bibliographic review was made in order to guide the methodology to be used. From there, we approached the problem with a genetic algorithm. We validated its eff ectiveness by comparing the results obtained with different instances proposed in the literature. The results obtained show that the proposed genetic algorithm is efficient in the different classic Job Shop configurations tested. The algorithm is able to find very approximate solutions to the best found to date for the Flexible Job Shop Scheduling Problem with transportation constraints.","PeriodicalId":41325,"journal":{"name":"ENTRE CIENCIA E INGENIERIA","volume":" ","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2018-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ENTRE CIENCIA E INGENIERIA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31908/19098367.3820","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
We solved the Flexible Job Shop Scheduling Problem (FJSSP) with transportation constraints in order to minimize the makespan by sequencing and assigning machines. A bibliographic review was made in order to guide the methodology to be used. From there, we approached the problem with a genetic algorithm. We validated its eff ectiveness by comparing the results obtained with different instances proposed in the literature. The results obtained show that the proposed genetic algorithm is efficient in the different classic Job Shop configurations tested. The algorithm is able to find very approximate solutions to the best found to date for the Flexible Job Shop Scheduling Problem with transportation constraints.