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Learning 'good quality' resource allocation from historical data

Journal Article


Abstract


  • Effective and efficient delivery of services requires tasks to

    be allocated to appropriate and available set of resources. Much of the

    research in task allocation, model a system of tasks and resources and

    determine which tasks should be executed by which resources. These

    techniques when applied to service systems with human resources, model

    parameters that can be explicitly identified, such as worker efficiency,

    worker capability based on skills and expertise, authority derived from

    organizational positions and so on. However, in real-life workers have

    complex behaviors with varying efficiencies that are either unknown or

    are increasingly complex to model. Hence, resource allocation models

    that equate human performance to device or machine performance could

    provide inaccurate results. In this paper we use data from process execution

    logs to identify resource allocations that have resulted in an expected

    service quality, to guide future resource allocations. We evaluate data for

    a service system with 40 human workers for a period of 8 months. We

    build a learning model using Support Vector Machine (SVM), that predicts

    the quality of service for specific allocation of tasks to workers. The

    SVM based classifier is able to predict service quality with 80% accuracy.

    Further, a latent discriminant classifier, uses the number of tasks

    pending in a worker’s queue as a key predictor, to predict the likelihood

    of allocating a new incoming request to the worker. A simulation model

    that incorporates the dispatching policy based on worker’s pending tasks

    shows an improved service quality and utilization of service workers.

UOW Authors


  •   Sindhgatta Rajan, Renuka (external author)
  •   Ghose, Aditya
  •   Dasgupta, Gaargi Banerjee. (external author)

Publication Date


  • 2015

Citation


  • Sindhgatta Rajan, R., Ghose, A. K. & Dasgupta, G. Banerjee. (2015). Learning 'good quality' resource allocation from historical data. Lecture Notes in Computer Science, 8954 84-95.

Scopus Eid


  • 2-s2.0-84983643632

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5044

Number Of Pages


  • 11

Start Page


  • 84

End Page


  • 95

Volume


  • 8954

Abstract


  • Effective and efficient delivery of services requires tasks to

    be allocated to appropriate and available set of resources. Much of the

    research in task allocation, model a system of tasks and resources and

    determine which tasks should be executed by which resources. These

    techniques when applied to service systems with human resources, model

    parameters that can be explicitly identified, such as worker efficiency,

    worker capability based on skills and expertise, authority derived from

    organizational positions and so on. However, in real-life workers have

    complex behaviors with varying efficiencies that are either unknown or

    are increasingly complex to model. Hence, resource allocation models

    that equate human performance to device or machine performance could

    provide inaccurate results. In this paper we use data from process execution

    logs to identify resource allocations that have resulted in an expected

    service quality, to guide future resource allocations. We evaluate data for

    a service system with 40 human workers for a period of 8 months. We

    build a learning model using Support Vector Machine (SVM), that predicts

    the quality of service for specific allocation of tasks to workers. The

    SVM based classifier is able to predict service quality with 80% accuracy.

    Further, a latent discriminant classifier, uses the number of tasks

    pending in a worker’s queue as a key predictor, to predict the likelihood

    of allocating a new incoming request to the worker. A simulation model

    that incorporates the dispatching policy based on worker’s pending tasks

    shows an improved service quality and utilization of service workers.

UOW Authors


  •   Sindhgatta Rajan, Renuka (external author)
  •   Ghose, Aditya
  •   Dasgupta, Gaargi Banerjee. (external author)

Publication Date


  • 2015

Citation


  • Sindhgatta Rajan, R., Ghose, A. K. & Dasgupta, G. Banerjee. (2015). Learning 'good quality' resource allocation from historical data. Lecture Notes in Computer Science, 8954 84-95.

Scopus Eid


  • 2-s2.0-84983643632

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5044

Number Of Pages


  • 11

Start Page


  • 84

End Page


  • 95

Volume


  • 8954