Machine Learning and Text Mining based Real-Time Semi-Autonomous Staff Assignment System

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dc.contributor.authorArslan, Halil
dc.contributor.authorEmreis, Yunus
dc.contributor.authorGormez, Yasin
dc.contributor.authorTemiz, Mustafa
dc.date.accessioned2024-10-26T18:03:50Z
dc.date.available2024-10-26T18:03:50Z
dc.date.issued2024
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractThe growing demand for information systems has significantly increased the workload of consulting and software development firms, requiring them to manage multiple projects simultaneously. Usually, these firms rely on a shared pool of staff to carry out multiple projects that require different skills and expertise. However, since the number of employees is limited, the assignment of staff to projects should be carefully decided to increase the efficiency in job -sharing. Therefore, assigning tasks to the most appropriate personnel is one of the challenges of multiproject management. Assigning a staff to the project by team leaders or researchers is a very demanding process. For this reason, researchers are working on automatic assignment, but most of these studies are done using historical data. It is of great importance for companies that personnel assignment systems work with real-time data. However, a model designed with historical data has the risk of getting unsuccessful results in real-time data. In this study, unlike the literature, a machine learning -based decision support system that works with real-time data is proposed. The proposed system analyses the description of newly requested tasks using textmining and machine -learning approaches and then, predicts the optimal available staff that meets the needs of the project task. Moreover, personnel qualifications are iteratively updated after each completed task, ensuring up-to-date information on staff capabilities. In addition, because our system was developed as a microservice architecture, it can be easily integrated into companies' existing enterprise resource planning (ERP) or portal systems. In a real -world implementation at Detaysoft, the system demonstrated high assignment accuracy, achieving up to 80% accuracy in matching tasks with appropriate personnel.
dc.identifier.doi10.2298/CSIS220922065A
dc.identifier.endpage94
dc.identifier.issn1820-0214
dc.identifier.issn2406-1018
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85185679906
dc.identifier.scopusqualityQ3
dc.identifier.startpage75
dc.identifier.urihttps://doi.org/10.2298/CSIS220922065A
dc.identifier.urihttps://hdl.handle.net/20.500.12418/28586
dc.identifier.volume21
dc.identifier.wosWOS:001171828000006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherComsis Consortium
dc.relation.ispartofComputer Science and Information Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectmulti-project management
dc.subjecttask assignment
dc.subjecttext mining
dc.subjectstaff assign- ment system
dc.titleMachine Learning and Text Mining based Real-Time Semi-Autonomous Staff Assignment System
dc.typeArticle

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