Understanding Team Dynamics with Agent-Based Simulation

Thierry Mamer, John McCall (), Siddhartha Shakya, Gilbert Owusu and Olivier Regnier-Coudert
Additional contact information
Thierry Mamer: Robert Gordon University
John McCall: Robert Gordon University
Siddhartha Shakya: Research and Innovation, BT Technology, Services and Operations
Gilbert Owusu: Research and Innovation, BT Technology, Services and Operations
Olivier Regnier-Coudert: Robert Gordon University

Chapter Chapter 12 in Transforming Field and Service Operations, 2013, pp 183-198 from Springer

Abstract: Abstract Agent-based simulation is increasingly used in industry to model systems of interest allowing the evaluation of alternative scenarios. By this means, business managers can estimate the consequences of policy changes at low cost before implementing them in the business. However, in order to apply such models with confidence, it is necessary to validate them continuously against changing business patterns. Typically, models contain key parameters which significantly affect the overall behaviour of the system. The process of selecting such parameters is an inverse problem known as ‘tuning’ In this chapter, we describe the application of computational intelligence to tune the parameters of a workforce dynamics simulator. We show that the best algorithm achieves reduced tuning times as well as more accurate field workforce simulations. Since implementation, this algorithm has facilitated the use of simulation to assess the effect of changes in different business scenarios and transformation initiatives.

Keywords: Simulated Annealing; Mean Absolute Percentage Error; Metaheuristic Algorithm; Parameter Sweep; British Telecom (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-44970-3_12

Ordering information: This item can be ordered from
http://www.springer.com/9783642449703

DOI: 10.1007/978-3-642-44970-3_12

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().