System Dynamics and Agent-Based Simulations for Workforce Climate

Zhu,; Meade, Conor; Sargent,; Warren, Christopher N.

doi:10.1109/wsc.2006.323144

Cited by 8 publications

(4 citation statements)

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“…Copyright [1998] Springer-Verlag. Confirming the general findings of Parunak, Savit and Riolo, examples from different research domains are summarised below: Wakeland et al [56], Marin et al[30], Demirel[11] and Alvarez et al[3]…”

mentioning

confidence: 67%

“…Marin et al [30] report on their SD model for workforce planning within NASA's Kennedy Space Centre. Echoing the observations of Shanthikumar and Sargent [48], regarding the preference of analytic models to simulation models on the basis of cost and computational efficiency, they chose to develop their model using SD as they perceived that the complexity associated with other methodologies would be prohibitive.…”

Section: Decomposing a System In Order To Establish The Most Approprimentioning

confidence: 99%

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Design classes for hybrid simulations involving agent-based and system dynamics models

Swinerd

McNaught

2012

Simulation Modelling Practice and Theory

122

107

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Hybrid simulation involves the use of multiple simulation paradigms, and is becoming an increasingly common approach to modelling modern, complex systems. Despite growing interest in its use, little guidance exists for modellers regarding the nature and variety of hybrid simulation models. Here, we concentrate on one particular hybrid -that involving agent-based and system dynamics models. Based on an up-to-date review of the literature, we propose three basic types of hybrid agent-based system dynamics simulations, referred to here as interfaced, integrated and sequential hybrid designs. We speculate that the classification presented may also be useful for other classes of hybrid simulations. on the part of the modeller. In such cases, it may be that an alternative simulation approach, either using another modelling paradigm or a hybrid approach, could provide a simpler, more natural or more efficient solution.This paper is not intended to provide a general review of hybrid simulation. Nor does it consider all of the hybrid combinations in equal measure. Instead, it focuses primarily on the class of hybrid simulation involving agentbased simulation and system dynamics, referred to here as AB-SD simulation. Our reasons for focusing on this particular combination are firstly that it is less well researched and understood than the SD-DES combination and secondly that we believe it offers a potentially useful approach to the modelling of complex adaptive systems (CAS). Such systems are defined by the US Argonne National Laboratory [5], a leading exponent of agent-based simulation, as 'fluidly changing collections of distributed interacting components that react to both their environments and to one another'. For example, McCarthy et al. view the development of new products as a complex adaptive system [32]. They state that 'Complex adaptive systems consist of a nested and scaleable system of agents; that is, the level of system abstraction could be an individual, a group, or an organization' [32, p 442]. They go on to say that '…nonlinearity and feedback can occur at multiple levels between individual agents and between groups of agents' [32, p443].Since nonlinearity and feedback are essential parts of the SD worldview and are explicitly represented within SD models, it is little wonder that SD has often been applied to model CAS. A fine example is provided by Sterman and Wittenberg [51] who developed an SD model to illustrate Kuhn's arguments concerning the rise and fall of academic paradigms. An implicit notion within this model was that each paradigm could be regarded as an agent.This notion of agency can also be observed in several other SD models of CAS. In fact, it is not always clear whether a particular model should be regarded as purely an SD model or as a hybrid AB-SD model. Perhaps what Duggan [13] refers to as agent-oriented SD models is a more accurate description of such models. It is our belief, however, that some CAS are best represented by truly hybrid AB-SD models. In this paper, we review several ...

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mentioning

confidence: 67%

Section: Decomposing a System In Order To Establish The Most Approprimentioning

confidence: 99%

Design classes for hybrid simulations involving agent-based and system dynamics models

Swinerd

McNaught

2012

Simulation Modelling Practice and Theory

122

107

View full text Add to dashboard Cite

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“…However, most SD models do not have state definitions that are cleanly decomposable into state representations at the individual entity level. The relationship between SD and ABS is an area of interest and research (Scholl 2001;Parunack 1998;Marin et al 2006;Norlin 2007). …”

Section: Agent Based Simulation and System Dynamicsmentioning

confidence: 99%

Cross-paradigm simulation modeling: Challenges and successes

Heath

Brailsford

Buss

et al. 2011

Proceedings of the 2011 Winter Simulation Conference (WSC)

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This paper addresses the broad topic area of cross-paradigm simulation modeling with a focus on the discrete-event, system dynamics and agent-based paradigms. It incorporates contributions from four panel members with diverse perspectives and areas of expertise. First, each paradigm is described and definitions are presented. The difference between the process-oriented worldview and the event-oriented worldview within discrete-event simulation modeling, and the importance of this difference for crossparadigm modeling, are discussed. Following the definitions, discussion of cross-paradigm modeling is given for each pair of these paradigms, highlighting current challenges and early successes in these areas. The basic time-advance mechanisms used in simulation modeling are also discussed, and the implications of these mechanisms for each paradigm is explored.

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“…The US Military has many specialized simulation tools (Murty et al 1995;McGinnis and Fernandez-Gaucherand 1994;Thom-Scales, Okazawa, and Ormrod as et al 1997;Miller K. 2009;Davenport et al 2007;and Hutchins et al 2005). Manpower planning in a business setting has also been explored by the University in Manchester in the UK (Gregoriades 2001), IBM (Lee 2007), and the Kennedy Space Center (Marin et al 2006) among others.…”

Section: Introductionmentioning

confidence: 99%

The Managed Readiness Simulator: A force readiness model

Scales¹,

Okazawa²,

Ormrod³

2011

Proceedings of the 2011 Winter Simulation Conference (WSC)

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This paper presents an overview of a force readiness simulation tool that has been developed for the Canadian Forces (CF). The Managed Readiness Simulator (MARS) is a versatile program that allows the user to quickly simulate a wide range of scenarios to forecast the extent to which the resources of an establishment are available to fulfill the requirements of a set of planned tasks over time. MARS can also account for the dynamics of the establishment, including recruitment, promotion, and attrition of personnel, and acquisition, maintenance and disposal of equipment. Two examples of how MARS has been applied to current CF problems are also included.

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System Dynamics and Agent-Based Simulations for Workforce Climate

Cited by 8 publications

References 0 publications

Design classes for hybrid simulations involving agent-based and system dynamics models

Design classes for hybrid simulations involving agent-based and system dynamics models

Cross-paradigm simulation modeling: Challenges and successes

The Managed Readiness Simulator: A force readiness model

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