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Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation

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  • Onggo, Bhakti Stephan
  • Karatas, Mumtaz

Abstract

Model verification and validation (V&V) is one of the most important activities in simulation modelling. Model validation is especially challenging for Agent-Based Simulation (ABS). Techniques that can help to improve V&V in simulation modelling are needed. This paper proposes a V&V technique called Test-Driven Simulation Modelling (TDSM) which applies techniques from Test-Driven Development in software engineering to simulation modelling. The main principle in TDSM is that a unit test for a simulation model has to be specified before the simulation model is implemented. Hence, TDSM explicitly embeds V&V in simulation modelling. We use a case study in maritime search operations to demonstrate how TDSM can be used in practice. Maritime search operations (and search operations in general) are one of the classic applications of Operational Research (OR). Hence, we can use analytical models from the vast search theory literature for unit tests in TDSM. The results show that TDSM is a useful technique in the verification and validation of simulation models, especially ABS models. This paper also shows that ABS can offer an alternative modelling approach in the analysis of maritime search operations.

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  • Onggo, Bhakti Stephan & Karatas, Mumtaz, 2016. "Test-driven simulation modelling: A case study using agent-based maritime search-operation simulation," European Journal of Operational Research, Elsevier, vol. 254(2), pages 517-531.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:2:p:517-531
    DOI: 10.1016/j.ejor.2016.03.050
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    1. G. Fagiolo & C. Birchenhall & P. Windrum, 2007. "Empirical Validation in Agent-based Models: Introduction to the Special Issue," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 189-194, October.
    2. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-8.
    3. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    4. A Emre Varol & Murat M Gunal, 2015. "Simulating prevention operations at sea against maritime piracy," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(12), pages 2037-2049, December.
    5. R R Hill & R G Carl & L E Champagne, 2006. "Using agent-based simulation to empirically examine search theory using a historical case study," Journal of Simulation, Taylor & Francis Journals, vol. 1(1), pages 29-38, December.
    6. Brian Heath & Raymond Hill & Frank Ciarallo, 2009. "A Survey of Agent-Based Modeling Practices (January 1998 to July 2008)," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-9.
    7. Jan C. Thiele & Winfried Kurth & Volker Grimm, 2014. "Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R'," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-11.
    8. Elio Marchione & Shane D Johnson & Alan Wilson, 2014. "Modelling Maritime Piracy: A Spatial Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(2), pages 1-9.
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    Cited by:

    1. Li, Xingyu & Epureanu, Bogdan I., 2020. "AI-based competition of autonomous vehicle fleets with application to fleet modularity," European Journal of Operational Research, Elsevier, vol. 287(3), pages 856-874.
    2. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    3. Mumtaz Karatas & Ertan Yakıcı & Abdullah Dasci, 2022. "Solving a bi-objective unmanned aircraft system location-allocation problem," Annals of Operations Research, Springer, vol. 319(2), pages 1631-1654, December.
    4. Troost, Christian & Huber, Robert & Bell, Andrew R. & van Delden, Hedwig & Filatova, Tatiana & Le, Quang Bao & Lippe, Melvin & Niamir, Leila & Polhill, J. Gareth & Sun, Zhanli & Berger, Thomas, 2023. "How to keep it adequate: A protocol for ensuring validity in agent-based simulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 159, pages 1-21.
    5. Noeldeke, Beatrice & Winter, Etti & Ntawuhiganayo, Elisée Bahati, 2022. "Representing human decision-making in agent-based simulation models: Agroforestry adoption in rural Rwanda," Ecological Economics, Elsevier, vol. 200(C).
    6. Mumtaz Karatas & Nasuh Razi & Murat M. Gunal, 2017. "An ILP and simulation model to optimize search and rescue helicopter operations," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1335-1351, November.

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