Abstract
The life cycle of a modelling and simulation process has two main parts: development, which includes initiation, design, construction, and testing; and application, which contains using and archiving. Modelling and simulation of logistics networks is divided into four main stages: modelling of logistic infrastructures using an agent based approach, modelling of logistic processes applying a dynamic based approach, modelling of demand variation using a probabilistic approach, and modelling of disturbances with an event driven approach. All these processes are described in Chapter 4 and form the basis to propose a general framework for evaluation of logistic networks operational risk and dependability in Chapter 5.
Learning by doing, peer-to-peer teaching, and computer simulation are all part of the same equation.
Nicholas Negroponte
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References
Albar FM, Jetter AJ (2009) Heuristics in decision making. In: PICMET 2009 proceedings, Portland, Oregon USA, 2–6 Aug 2009
Angerhofer BJ, Angelides MC (2000) System dynamics modelling in supply chain management: research review. In: Joines JA, Barton RR, Kang K, Fishwick PA (eds) Proceedings of the 2000 winter simulation conference, pp 342–351
AnyLogic (2018) http://www.anylogic.com/features
APQC (2015) APQC’s process classification framework version 6.1.1. https://www.qgcio.qld.gov.au/__data/assets/pdf_file/0026/5894/APQC_PCF_Ver_6.1.1-final-1.pdf
ARIS (2018) https://www.ariscommunity.com/
Arnold D (1995) Materialflusslehre. Vieweg, Braunschweig
Barbati M, Bruno G, Genovese A (2011) Applications of agent-based models for optimization problems: a literature review. Expert Syst Appl 39(5):6020–6028
Birta LG, Arbez G (2007) Modelling and simulation exploring dynamic system behaviour. Springer, London
Bonabeau E (2001) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci 99(3):7280–7287
Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford
Booch G, Jacobson I, Rumbaugh J (2005) The unified modeling language user guide, 2nd edn. Addison-Wesley, Boston
Borshchev A, Filippov A (2004) From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In: The 22nd international conference of the system dynamics society, 25–29 July 2004, Oxford, England. https://www.systemdynamics.org/assets/conferences/2004/SDS_2004/PAPERS/381BORSH.pdf
BPMN Tutorial (2018) https://camunda.com/bpmn/
Bukowski L (1995) Statistische Methoden im Ingenieurwesen. Verlag der Technischen Universität Clausthal
Bukowski L, Karkula M (2003) The simulation of logistic processes using DOSIMIS-3 simulator. In: Proceedings of the international conference on logistics, Ostrava, pp 296–300
Bukowski L. and Karkula M. (2009) Modelling and simulation of logistics processes in heat and power plants—a hybrid approach. In: Proceedings of the twentieth international conference on systems engineering—ICSE 2009, Coventry, United Kingdom. ISBN 978-1-84600-0294
Bukowski L, Karkula M (2010) Integration of discrete event simulation, decision tables and fuzzy systems in modelling of logistics processes. In: Advances in systems science. Academic Publishing House EXIT, pp 113–121
Cassandras CG, Lafortune S (2009) Introduction to discrete event systems. Springer, Berlin
Cochran JK, Mackulak GT, Savory PA (1995) Simulation project characteristics in industrial settings. Interfaces 25(4):104–113
DFD (2018) https://www.lucidchart.com/blog/data-flow-diagram-tutorial
Eberle A (2015) Markov processes. https://wt.iam.uni-bonn.de/fileadmin/WT/Inhalt/people/Andreas_Eberle/MarkovProcesses/MPSkript1415.pdf
EPC (2018) https://www.ariscommunity.com/event-driven-process-chain
ERD (2018) https://www.lucidchart.com/pages/er-diagrams
FEMA P-789 (2013) Continuity guidance circular 2. Continuity guidance for non-federal governments: mission essential functions identification process (states, territories, tribes, and local government jurisdictions). https://ready.alaska.gov/Plans/continuity/Continuity-Guidance-Circular2.pdf
Fishman GS (2001) Discrete-event simulation: modeling, programming, and analysis. Springer, Berlin
Forecast Sheets in Excel 2016—Tutorial (2016) https://www.teachucomp.com/forecast-sheets-in-excel-2016-tutorial/
Forrester JW (1958) Industrial dynamics: a major breakthrough for decision makers. Harvard Bus Rev 36(4):37–66
Forrester JW (1961) Industrial dynamics. Productivity Press, Portland
Gigerenzer G, Gaissmaier W (2011) Heuristic decision making. Annu Rev Psychol 62:451–482
Gilbert N, Troitzsch K (2005) Simulation for the social scientist, 2nd ed. McGraw-Hill, GMU (George Mason University). MASON Home Page. http://cs.gmu.edu/~eclab/projects/mason/. Accessed 29 July 2014
Gordon G (1961) A general purpose systems simulation program. In: Proceedings of EJCC, Washington D.C. McMillan, NY, pp 87–104
Gumbel EJ (1941) The return period of flood flow. Ann. Math. Stat 12:163–190
Hawa M (2007) Probability distribution summary. https://www.google.com/search?ei=vnNTW-TNFsPKwAL217G4BQ&q
Heinrich HW (1959) Industrial accidents prevention. Mc Graw Hill Book Company Inc., New York
Hollnagel E, Woods DD, Leveson N (2006) Resilience engineering: concepts and precepts. Ashgate, Aldershot
Joyce D (2016) Common probability distributions Probability and Statistics. https://mathcs.clarku.edu/~djoyce/ma218/distributions.pdf
Karkula M, Bukowski L (2012) Computational intelligence methods—joint use in discrete event simulation model of logistics processes. In: Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM (eds) Proceedings of the 2012 winter simulation conference, Berlin, © IEEE
Law AM (2007) Simulation modeling and analysis, 4th edn. McGraw-Hill, New York
Law AM, Kelton WD (1991) Simulation modelling and analysis. McGraw-Hill, Inc., NY
Macal C (2004) Emergent structures from trust relationships in supply chains. In: Macal C, Sallach D, North M (2004) Proceedings of agent: conference on social dynamics, Chicago, IL, 7–9 Oct 743–760, Argonne National Laboratory
Macal C, North M (2003) Effects of global information availability in networks of supply chain agents. In: Macal C, Sallach D, North M (eds) Proceedings of agent: conference on challenges in social simulation, Chicago, IL, Argonne National Laboratory, 2–4 Oct 2003, pp 235–252
Macal C, North M (2006) Tutorial on agent-based modeling and simulation part 2: how to model with age. In: Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM (eds) Proceedings of the 2006 winter simulation conference, pp 73–83
Macal C, North M (2010) Tutorial on agent-based modeling and simulation. J Simul 4(3):151–162
Macal C, North M (2014) Introductory tutorial: agent-based modeling and simulation. In: Tolk A, Diallo SY, Ryzhov IO, Yilmaz L, Buckley S, Miller JA (eds) Proceedings of the 2014 winter simulation conference, pp 6–20
Maria A (1997) Introduction to modeling and simulation. In: Andradóttir S, Healy KJ, Withers DH, Nelson BL (eds) Proceedings of the winter simulation conference 1997, pp 7–13. http://acqnotes.com/Attachments/White%20Paper%20Introduction%20to%20Modeling%20and%20Simulation%20by%20Anu%20Maria.pdf
Montgomery DC, Runger GC (2003) Applied statistics and probability for engineers. Wiley, Hoboken
Morecroft JDW (2015) Strategic modelling and business dynamics. A feedback systems approach. Wiley, New York
NASA (2013) NASA-STANDARDS-7009. https://spaceflightsystems.grc.nasa.gov/SpaceDOC_II/Standards/documents/NASA-STD-7009.pdf
NASA (2016) NASA-STANDARDS-7009a. https://standards.nasa.gov/standard/nasa/nasa-std-7009
NetLogo (2006) NetLogo home page. http://ccl.northwestern.edu/netlogo
Nyere J (2008) The design-chain operations model. http://www.supply-chain.org
Pate-Cornell E (2012) On “black swans” and “perfect storms”: risk analysis and management when statistics are not enough. Risk Anal 11:1823–1833
Reason J (1997) Managing the risks of organizational accidents. Ashgate, Aldershot, UK
Robinson S (2008) Conceptual modelling for simulation part I: definition and requirements. J Oper Res Soc 59(3):278–290
Ross SM (2004) Introduction to probability and statistics for engineers and scientists. Elsevier Academic Press, Cambridge
Scheaffer RL, Mulekar M, McClave JT (2011) Probability and statistics for engineers. Cengage Learning, Brooks/Cole
Scheer AW, Jost W, Heiss H, Kronz H (eds) (2006) Corporate performance management. ARIS in practice. Springer, Berlin
SCOR (2018) http://cloud.ld.ttu.ee/idu0010/Portals/0/Harjutustunnid/SCOR10.pdf
Seth D, Gupta V (2007) Application of value stream mapping for lean operations and cycle time reduction: an Indian case study. Prod Plan Control 16(1):44–59
Soong TT (2004) Fundamentals of0020probability and statistics for engineers. Wiley, Hoboken
Sornette D (2009) Dragon-kings, black swans and the prediction of crises. Int J Terraspace Sci Eng. http://www.arxiv.org›physics
Spiegelberg-Planer R (2009) A matter of degree. A revised international nuclear and radiological event scale (INES) extends its reach. IAEA Bull 51–1:46–49. https://www.iaea.org/sites/default/files/publications/magazines/bulletin/bull51-1/51102744649.pdf
Sterman JD (1989) Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiment. Manag Sci 35(3):321–339
Sterman JD (2001) System dynamics modeling: tools for learning in a complex world. California Manag Rev 43(4):8–25
Sterman JD (2000) Business dynamics: systems thinking and modeling for a complex world. Irwin McGraw-Hill, Boston
Swaminathan J, Smith S, Sadeh N (1998) Modelling supply chain dynamics: a multiagent approach. Decis Sci 29(3)
SWARM (2018) The swarm simulation system, a toolkit for building multi-agent simulations. http://www.santafe.edu/projects/swarm/overview/overview.html
Sweetser A (2009) A comparison of system dynamics (SD) and discrete event simulation. https://pdfs.semanticscholar.org/bca5/0943f66fd012dd62168433d5f04221b5d6f5.pdf
Taleb NN (2010) The black swan: the impact of the highly improbable. Penguin Books, London
Taleb NN (2013) Antifragile. Things that gain from disaster. Penguin Books, London
Wild R (2002) Operations management, 6th edn. Continuum, London
Woodward WA, Gray HL, Elliott AC (2012) Applied time series analysis. CRC Press, Boca Raton
Wooldridge M (2009) An Introduction to Multiagent Systems. John Wiley & Sons Ltd, Hoboken
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Bukowski, L. (2019). Modelling and Simulation of Logistic Networks. In: Reliable, Secure and Resilient Logistics Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-00850-5_5
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