{"@context":"http://schema.org","@type":"Dataset","@id":"https://doi.org/10.34820/FK2/0S67CT","identifier":"https://doi.org/10.34820/FK2/0S67CT","name":"Dataset for parallel flow-shop scheduling","creator":[{"name":"DARU KUSUMA, PURBA","affiliation":"fakultas teknik elektro - rekayasa komputer"}],"author":[{"name":"DARU KUSUMA, PURBA","affiliation":"fakultas teknik elektro - rekayasa komputer"}],"datePublished":"2022-09-29","dateModified":"2022-09-29","version":"1","description":["This data consists of 40 jobs. Each job will be processed in four stages. The average processing time of each stage is 5 unit time and follows normal distribution. Each data consists of five attributes: job id, stage 1 processing time, stage 2 processing time, stage 3 processing time, and stage 4 processing time. This data is simulation generated data. There are 30 trials. This data contains 1,200 rows."],"keywords":["Engineering","optimization, operations research, flow-shop"],"citation":[{"@type":"CreativeWork","text":"P. D. Kusuma and A. S. Albana, A Parallel Permutation Flow-Shop Scheduling Model by Using a Two-Step Evolutionary Algorithm to Minimize Intermediate Storage with Tolerable Maximum Completion Time, International Journal of Intelligent Engineering and Systems, 14(6), 2021."}],"license":{"@type":"Dataset","text":"CC0","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"includedInDataCatalog":{"@type":"DataCatalog","name":"Telkom University Dataverse","url":"https://dataverse.telkomuniversity.ac.id"},"publisher":{"@type":"Organization","name":"Telkom University Dataverse"},"provider":{"@type":"Organization","name":"Telkom University Dataverse"},"distribution":[{"@type":"DataDownload","name":"tabelflowshop.sql","fileFormat":"application/x-sql","contentSize":22504,"@id":"https://doi.org/10.34820/FK2/0S67CT/YQ3LHI","identifier":"https://doi.org/10.34820/FK2/0S67CT/YQ3LHI","contentUrl":"https://dataverse.telkomuniversity.ac.id/api/access/datafile/10178"}]}