Engaging Stakeholders To Extend The Lifecycle Of Hybrid Simulation Models

Developing a simulation model of a complex system requires a significant investment of time, expertise and expense. In order to realize the greatest return on such an investment, it is desirable to extend the lifecycle of the simulation model as much as possible. Existing studies typically end after the `first loop' of the lifecycle, with the computer model suitable for addressing the initial requirements of the stakeholders. We explore extending the modeling lifecycle to a `second loop' by introducing an existing hybrid simulation model to a new group of stakeholders and further developing it to capture new requirements. With the aid of an example application, we explain how the hybrid model facilitated stakeholder engagement by closely reflecting the real world and how the model lifecycle has been successfully extended to maximize the benefit to Eurostar International Limited

A Review Of Hybrid Simulation In Healthcare

Hybrid Simulation (HS) has been applied to healthcare systems, but there is still limited literature and an opportunity to develop research. This review explores applications of HS in healthcare, to outline research gaps and foster new research in HS to solve complex real healthcare problems. The twelve application papers found through a systematic literature search covered nearly all hybrid combinations. Discrete Event (DES) and System Dynamics (SD) were found to be the most popular combination, and AnyLogic, the most used HS tool. We found that none of the papers we reviewed used the SD-ABS approach, which raises questions about the need and challenges associated with certain combinations. HS in healthcare applications, for the most part, are published in conference proceedings. We discuss opportunities for research and, in particular, the potential for HS application in problems related to communicable disease and healthcare services planning

Minimum Viable Model (MVM) Methodology for Integration of Agile Methods into Operational Simulation of Logistics

Background: Logistics problems involve a large number of complexities, which makes the development of models challenging. While computer simulation models are developed for addressing complexities, it is essential to ensure that the necessary operational behaviours are captured, and that the architecture of the model is suitable to represent them. The early stage of simulation modelling, known as conceptual modelling (CM), is thus dependent on successfully extracting tacit operational knowledge and avoiding misunderstanding between the client (customer of the model) and simulation analyst. Objective: This paper developed a methodology for managing the knowledge-acquisition process needed to create a sufficient simulation model at the early or the CM stage to ensure the correctness of operation representation. Methods: A minimum viable model (MVM) methodology was proposed with five principles relevant to CM: iterative development, embedded communication, soliciting tacit knowledge, interactive face validity, and a sufficient model. The method was validated by a case study of freight operations, and the results were encouraging. Conclusions: The MVM method improved the architecture of the simulation model through eliciting tacit knowledge and clearing up communication misunderstandings. It also helped shape the architecture of the model towards the features most appreciated by the client, and features not needed in the model. Originality: The novel contribution of this work is the presentation of a method for eliciting tacit information from industrial clients, and building a minimally sufficient simulation model at the early modelling stage. The framework is demonstrated for logistics operations, though the principles may benefit simulation practitioners more generally.</jats:p