Maria Krisnawati, Fadhila Rifda Azka Syailendri, A. A. Sibarani
{"title":"Ready Mix Concrete Production Scheduling and Truck Mixer Allocation Using Genetic Algorithm: A Case Study","authors":"Maria Krisnawati, Fadhila Rifda Azka Syailendri, A. A. Sibarani","doi":"10.4028/p-4kfzgm","DOIUrl":null,"url":null,"abstract":"The XYZ company is engaged in the production of construction materials such as Ready-Mix Concrete (RMC). XYZ company must increase productivity in order to compete with the industry’s rapid expansion in Banyumas, Indonesia. Scheduling production is one of the methods for boosting productivity. Utilizing a mathematical model and a single-machine approach, scheduling is arranged. Using a product delivery system that is integrated with the production process, a production scheduling model and allocation of product delivery vehicles, the Truck Mixer, are developed. The Genetic Algorithm is used to find a more effective scheduling solution. The results of this study indicate that the schedule generated by the genetic algorithm has a total reduction of ten job sequence completion times of 134363 seconds or 24% more efficient than the existing schedule on average.","PeriodicalId":512976,"journal":{"name":"Engineering Headway","volume":"66 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Headway","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-4kfzgm","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
The XYZ company is engaged in the production of construction materials such as Ready-Mix Concrete (RMC). XYZ company must increase productivity in order to compete with the industry’s rapid expansion in Banyumas, Indonesia. Scheduling production is one of the methods for boosting productivity. Utilizing a mathematical model and a single-machine approach, scheduling is arranged. Using a product delivery system that is integrated with the production process, a production scheduling model and allocation of product delivery vehicles, the Truck Mixer, are developed. The Genetic Algorithm is used to find a more effective scheduling solution. The results of this study indicate that the schedule generated by the genetic algorithm has a total reduction of ten job sequence completion times of 134363 seconds or 24% more efficient than the existing schedule on average.