Decision models and artificial intelligence in supporting workforce forecasting and planning

dc.contributor.advisorWan, Hung-Da
dc.contributor.authorShukla, Sanjay Kumar
dc.contributor.committeeMemberChen, F. Frank
dc.contributor.committeeMemberSaygin, C.
dc.date.accessioned2024-03-08T15:44:31Z
dc.date.available2024-03-08T15:44:31Z
dc.date.issued2009
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractFor any organization, the effective workforce planning is essential to stay competitive and continue to subsist. Workforce planning is an organized process for identifying the number of employees, their mix and the types of skill sets required to accomplish an organization's strategic goals and objectives. This thesis focuses on demand analysis (i.e. forecasting the future workforce demand) in workforce planning. Workforce demand forecasting techniques can be classified into two broad categories viz. qualitative and quantitative. Generally, quantitative techniques are used to forecast workforce size and mix, whereas, qualitative techniques forecast competency requirements. This research explores demand analysis in many folds. First, state-of-the-art of workforce analysis techniques are presented and synthesized into a scenario specific forecasting technique(s) selection tree. Afterwards, the Clonal C-fuzzy Decision Tree (C2FDT), a decision support model, is proposed to forecast future workforce demand. C2FDT inherits its properties from fuzzy c-mean clustering and clonal algorithm. From the literature of workforce planning eight key parameters are selected as the major determinants of workforce analysis outcomes. In order to collect time-series and cross-sectional data corresponding to these parameters, set of questions are made. These questions are given to experts and according to their responses questions are integrated with the aid of Fuzzy Logic Controller. In this way large amount of data set is collected to train and test the C2FDT model.
dc.description.departmentMechanical Engineering
dc.format.extent80 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9781109540956
dc.identifier.urihttps://hdl.handle.net/20.500.12588/5718
dc.languageen
dc.subjectClonal Algorithm
dc.subjectFuzzy C-mean Clustering
dc.subjectFuzzy Logic Controller
dc.subjectScenario Specific Forecasting Technique (S) Selection Tree
dc.subjectWorkforce Forecasting
dc.subject.classificationMechanical engineering
dc.subject.classificationIndustrial engineering
dc.subject.lcshManpower planning -- Mathematical models
dc.subject.lcshPersonnel management -- Decision making
dc.titleDecision models and artificial intelligence in supporting workforce forecasting and planning
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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