Show simple item record

dc.contributor.authorKibiwot, Simon Kipkemboi
dc.date.accessioned2021-07-28T11:24:01Z
dc.date.available2021-07-28T11:24:01Z
dc.date.issued2020-09
dc.identifier.urihttp://hdl.handle.net/11071/12051
dc.descriptionThesis Submitted to the Faculty of Information in partial fulfillment of the requirements for the award of Master of Science in Information Technologyen_US
dc.description.abstractTask allocation is one among the key planning exercises that plays a major role in an organization’s quest to satisfy Service Level Agreements and to attain operational excellence. Task allocation is a difficult issue that addresses the inter-dynamics of tasks and employees, having in mind factors such as skills utilization, fairness and diversity. Conflicts arise when tasks take too long to be resolved due to in expertise and poor task allocation. The individuals assigned end up not performing the task well and incomplete tasks/project are presented. The end user is usually dissatisfied and will end up giving negative feedback. Previous studies have not addressed the issue of matching two or more employees to the task with qualities possessed by the employee, preference of the team or partner given and the qualifications for the task given. The nature of this problem has within this research been equated to the stable marriage problem solved by the Gale and Shapley’s Algorithm. However, a serious concern of the Gale-Shapley algorithm is its non-truthfulness also known as the man optimal result. Gale-Shapley’s algorithm was used to bring out the human aspect during task allocation. The aim of this research is to formulate an algorithm to support task allocation problem using machine learning. To ensure time efficiency, a predictive machine learning algorithm (artificial neural network) has been used to show improved time with each pair towards a task. A mixed method research methodology was used whereby it combined elements of quantitative and qualitative research approaches. The precision of the developed model was at 0.713421 bringing the estimate of the regression model to 71.34%. The findings showed that the neural network can be used as an effective algorithm for predicting the task allocation.en_US
dc.description.sponsorshipFaculty of Information Technologyen_US
dc.language.isoen_USen_US
dc.publisherStrathmore Universityen_US
dc.subjectTask allocationen_US
dc.subjectStable marriage problemen_US
dc.subjectMatching problemen_US
dc.subjectMachine learningen_US
dc.titleA Machine learning model for task allocationen_US
dc.typeThesisen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record