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Using the modelling frame in the conceptual modelling activity to improve the advantages of hybridisation

Jones, William and Kotiadis, Kathy and O'Hanley, Jesse R. and Robinson, Stewart (2024) Using the modelling frame in the conceptual modelling activity to improve the advantages of hybridisation. In: Fakhimi, Masoud and Mustafee, Navonil, eds. Hybrid Modeling and Simulation: Conceptualizations, Methods, and Applications. Springer Nature. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:102909)

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Language: English

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Abstract

Conceptual modelling (CM) is fundamental to the complex activity of simulation modelling, yet it remains widely misunderstood. The emergence and growing adoption of hybrid simulation (HS) have added layers of complexity due to the multitude of methods and the diverse expertise of modellers. This chapter introduces a novel representation method to elucidate the modelling frame for HS studies. This method, designed to complement existing CM techniques, guides modellers in pinpointing and communicating the best possible amalgamation of modelling methods tailored for specific projects. The introduced approach pivots on five core components: the combination of simulation techniques, the combination of simulation with analytic techniques, the modelling environment, the experimentation approach, and study outputs. By enhancing the conceptual clarity of HS models, our representation method paves the way for improved model quality, more effective stakeholder engagement, and expedited project timelines. Furthermore, it facilitates a smoother translation from system descriptions to operational computer models. The chapter underscores the pivotal role of our method in bridging communication divides, fostering understanding, and enabling agile software development, ultimately aiding in streamlining the creation of simulation models.

Item Type: Book section
Subjects: H Social Sciences
Institutional Unit: Schools > Kent Business School
Former Institutional Unit:
Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Jesse O'Hanley
Date Deposited: 22 Sep 2023 15:09 UTC
Last Modified: 29 Oct 2025 14:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/102909 (The current URI for this page, for reference purposes)

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