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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PurposeThe purpose of this paper is to raise awareness among manufacturing researchers and practitioners of the potential of Bayesian networks (BNs) to enhance decision making in those parts of the manufacturing domain where uncertainty is a key characteristic. In doing so, the paper describes the development of an intelligent decision support system (DSS) to help operators in Motorola to diagnose and correct faults during the process of product system testing.Design/methodology/approachThe intelligent (DSS) combines BNs and an intelligent user interface to produce multi‐media advice for operators.FindingsSurveys show that the system is effective in considerably reducing fault correction times for most operators and most fault types and in helping inexperienced operators to approach the performance levels of experienced operators.Originality/valueSuch efficiency improvements are of obvious value in manufacturing. In this particular case, additional benefit was derived when the product testing facility was moved from the UK to China as the system was able to help the new operators to get close to the historical performance level of experienced operators.