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An extended agent based model for service delivery optimization

Journal Article


Abstract


  • © Springer International Publishing Switzerland 2014. Service delivery optimization has an important impact on organizational profitability, where changes in allocation of resources (e.g. humans, equipment and materials) to services increases profit. Simulation and optimization techniques generally suffer from three main drawbacks; firstly, the limited knowledge and skill of researchers in modeling social complexities. Secondly, having assumed that a fairly realistic model of the problem is simulated, finding optimal solutions requires an exhaustive search that is almost impossible in problems with a large search space. Thirdly, mathematical optimization techniques often require the acquisition of knowledge in a central unit, which is problematic e.g. for privacy reasons. This article introduces a new technique, which combines Agent Based Modeling (ABM) and Distribution Constraint Optimization (DCOP) to overcome these difficulties. Our empirical results present a successful model for finding optimized resourced allocation settings in comparison with two different ABM simulated models on a sample of a real-life service delivery problem

UOW Authors


  •   Mohagheghian, Mohammadreza (external author)
  •   Sindhgatta Rajan, Renuka (external author)
  •   Ghose, Aditya

Publication Date


  • 2014

Citation


  • Mohagheghian, M., Sindhgatta Rajan, R. & Ghose, A. K. (2014). An extended agent based model for service delivery optimization. Lecture Notes in Computer Science, 8861 270-285.

Scopus Eid


  • 2-s2.0-84910121151

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4130

Number Of Pages


  • 15

Start Page


  • 270

End Page


  • 285

Volume


  • 8861

Abstract


  • © Springer International Publishing Switzerland 2014. Service delivery optimization has an important impact on organizational profitability, where changes in allocation of resources (e.g. humans, equipment and materials) to services increases profit. Simulation and optimization techniques generally suffer from three main drawbacks; firstly, the limited knowledge and skill of researchers in modeling social complexities. Secondly, having assumed that a fairly realistic model of the problem is simulated, finding optimal solutions requires an exhaustive search that is almost impossible in problems with a large search space. Thirdly, mathematical optimization techniques often require the acquisition of knowledge in a central unit, which is problematic e.g. for privacy reasons. This article introduces a new technique, which combines Agent Based Modeling (ABM) and Distribution Constraint Optimization (DCOP) to overcome these difficulties. Our empirical results present a successful model for finding optimized resourced allocation settings in comparison with two different ABM simulated models on a sample of a real-life service delivery problem

UOW Authors


  •   Mohagheghian, Mohammadreza (external author)
  •   Sindhgatta Rajan, Renuka (external author)
  •   Ghose, Aditya

Publication Date


  • 2014

Citation


  • Mohagheghian, M., Sindhgatta Rajan, R. & Ghose, A. K. (2014). An extended agent based model for service delivery optimization. Lecture Notes in Computer Science, 8861 270-285.

Scopus Eid


  • 2-s2.0-84910121151

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4130

Number Of Pages


  • 15

Start Page


  • 270

End Page


  • 285

Volume


  • 8861