Abstract
Decision making in modern supply chains can be extremely daunting due to their complex nature. Discrete-event simulation is a technique that can support decision making by providing what-if analysis and evaluation of quantitative data. However, modelling supply chain systems can result in massively large and complicated models that can take a very long time to run even with today's powerful desktop computers. Distributed simulation has been suggested as a possible solution to this problem, by enabling the use of multiple computers to run models. To investigate this claim, this paper presents experiences in implementing a simulation model with a ‘conventional’ approach and with a distributed approach. This study takes place in a healthcare setting, the supply chain of blood from donor to recipient. The study compares conventional and distributed model execution times of a supply chain model simulated in the simulation package Simul8. The results show that the execution time of the conventional approach increases almost linearly with the size of the system and also the simulation run period. However, the distributed approach to this problem follows a more linear distribution of the execution time in terms of system size and run time and appears to offer a practical alternative. On the basis of this, the paper concludes that distributed simulation can be successfully applied in certain situations.
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References
Banks J, Buckley S, Jain S, Lendermann P and Manivannan M (2002). Opportunities for simulation in supply chain management. In: Yücesan E, Chen CH, Snowdon JL and Charnes JM (eds). Proceedings of the 34th Winter Simulation Conference, 8–11 December 2002, San Diego CA. IEEE Press: New York, pp 1652–1658.
BCSH Blood Transfusion Task Force (1996). Guidelines for pre-transfusion compatibility procedures in blood transfusion laboratories. Transfus Med 6: 273–283.
Chandy KM and Misra J (1979). Distributed simulation: A case study in design and verification of distributed programs. IEEE Trans Software Eng 5(5): 440–452.
Chapman RL and Corso M (2005). From continuous improvement to collaborative innovation: The next challenge in supply chain management. Prod Plann Control 16: 339–344.
Cooper MC and Ellgram LM (1993). Characteristics of supply chain management and the implications for purchasing and logistics strategy. Int J Logist Mngt 4: 13–24.
Fujimoto RM (1999). Parallel and distributed simulation. In: Farrington PA, Nembhard HB, Sturrock DT and Evans GW (eds). Proceedings of the 31st Winter Simulation Conference, 5–8 December 1999, Phoenix AZ. IEEE Press: New York, pp 122–131.
Fujimoto RM (2001). Parallel and distributed simulation systems. In: Peters BA, Smith JS, Medeiros DJ and Rohrer MW (eds). Proceedings of the 33rd Winter Simulation Conference, 9–12 December 2001, Arlington, VA. IEEE Press: New York, pp 147–157.
Fujimoto RM (2003). Distributed simulation systems. In: Chick S, Sánchez PJ, Ferrin D and Morrice DJ (eds). Proceedings of the 35th Winter Simulation Conference, 7–10 December 2003, New Orleans, LA. IEEE Press: New York, pp 124–134.
Gan BP, Lendermann P, Low MYH, Turner SJ, Wang X and Taylor SJE (2005). Interoperating Autosched AP using the high level architecture. In: Kuhl ME, Steiger NM, Armstrong FB and Joines JA (eds). Proceedings of the 37th Winter Simulation Conference, 4–7 December 2005, Orlando, FL. IEEE Press: New York, pp 394–401.
Goyal S and Giri B (2001). Recent trends in modeling of deteriorating inventory. Eur J Opl Res 134: 1–16.
Hibino H, Fukuda Y, Yura Y, Mitsuyuki K and Kaneda K (2002). Manufacturing adapter of distributed simulation systems using HLA. In: Yücesan E, Chen CH, Snowdon JL and Charnes JM (eds). Proceedings of the 34th Winter Simulation Conference, 8–11 December 2002, San Diego, CA. IEEE Press: New York, pp 1099–1107.
IEEE 1516 (2000). IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA). Institute of Electrical and Electronics Engineers: New York.
Katsaliaki K and Brailsford SB (2007). Using simulation to improve the U.K. blood supply chain. J Opl Res Soc 58: 219–227.
Kuhl F, Weatherly R and Dahmann J (1999). Creating Computer Simulation Systems, An Introduction to the High Level Architecture. Prentice-Hall: New Jersey.
Law AM and Kelton WD (2000). Simulation Modeling and Analysis, 3rd edn. McGraw-Hill: New York.
Lendermann P, Gan BP and McGinnis LF (2001). Distributed simulation with incorporated APS procedures for high-fidelity supply chain optimization. In: Peters BA, Smith JS, Medeiros DJ and Rohrer MW (eds). Proceedings of the 33rd Winter Simulation Conference. 9–12 December 2001, Arlington VA. IEEE Press: New York, 1138–1145.
Linn RJ, Chen CS and Lozan JA (2002). Development of distributed simulation model for the transporter entity in a supply chain process. In: Yücesan E, Chen CH, Snowdon JL and Charnes JM (eds). Proceedings of the 34th Winter Simulation Conference, 8–11 December 2002, San Diego, CA. IEEE Press: New York, pp 1319–1326.
Mertins K, Rabe M and Jäkel FW (2000). Neutral template libraries for efficient distributed simulation within a manufacturing system engineering platform. In: Joines JA, Barton RR, Kang K and Fishwick PA (eds). Proceedings of the 32nd Winter Simulation Conference, 10–13 December 2000, Orlando, FL. IEEE Press: New York, pp 1549–1557.
McLean C and Riddick F (2000). The IMS MISSION architecture for distributed manufacturing simulation. In: Joines JA, Barton RR, Kang K and Fishwick PA (eds). Proceedings of the 32nd Winter Simulation Conference, 10–13 December 2000, Orlando, FL. IEEE Press: New York, pp 1539–1548.
Pidd M (1998). Computer Simulation in Management Science, 4th edn. Wiley: Chichester, UK.
Rabe M and Jäkel FW (2003). On Standardization Requirements for Distributed Simulation in Production and Logistics. In: Cunningham P, Cunningham M and Fatelnig P (eds). Building the Knowledge Economy. IOS Press: Twente, The Netherlands, pp. 399–406.
Robinson S (2005). Discrete-event simulation: From the pioneers to the present what next? J Opl Res Soc 56: 619–629.
Stevens GC (1989). Integrating the supply chain. Int J Phys Distrib Mater Mngt 19: 3–8.
Straßburger S (2001). Distributed Simulation Based on the High Level Architecture in Civilian Application Domains. Society for Computer Simulation International: Ghent, Belgium.
Straßburger S, Schmidgall G and Haasis S (2003). Distributed manufacturing simulation as an enabling technology for the digital factory. J Advanced Manufact Sys 2: 111–126.
Surana A, Kumara S, Greaves M and Raghavan UN (2005). Supply-chain networks: A complex adaptive systems perspective. Int J Prod Res 43: 4235–4265.
Swain JJ (2003). Simulation reloaded: Sixth biennial survey of discrete-event software tools. OR/MS Today 30(4): 46–57.
Taylor SJE, Bohli L, Wang X, Turner SJ and Ladbrook J (2005a). Investigating distributed simulation at the Ford Motor Company. In: Boukerche A and Turner SJ. (eds). Proceedings of the Ninth IEEE International Symposium on Distributed Simulation and Real-Time Applications, 10–12 October 2005, Montreal, Quebec. IEEE Computer Society Press: New York, pp 139–147.
Taylor SJE, Turner SJ and Low MYH (2005b). The COTS Simulation Interoperability Product Development Group. In: Adelantado M. et al (eds). Proceedings of the 2005 European simulation Interoperability Workshop, 27–29 June 2005, Toulouse, France. Simulation Interoperability Standards Organisation, Institute for Simulation and Training: Florida, p 56.
Taylor SJE, Turner SJ, Mustafee N, Ahlander H and Ayani R (2005c). COTS distributed simulation: A comparison of CMB and HLA interoperability approaches to type I interoperability reference model problems. Simulation 81: 33–43.
Taylor SJE, Wang X, Turner SJ and Low MYH (2006). Integrating heterogeneous distributed COTS discrete-event simulation packages: An emerging standards-based approach. IEEE Trans Syst Man Cybernet: Part A 36: 109–122.
Terzi S and Cavalieri S (2004). Simulation in the supply chain context: A survey. Comput Ind 53: 3–16.
van Donselaar KH, van Woensel T, Broekmeulen RACM and Fransoo JC (2006). Inventory control of perishables in supermarkets. Int J Prod Econ 104: 462–472.
Wang X, Turner SJ, Taylor SJE, Low MYH and Gan BP (2005). A COTS Simulation Package Emulator (CSPE) for Investigating COTS Simulation package Interoperability architecture. In: Kuhl ME, Steiger NM, Armstrong FB and Joines JA (eds). Proceedings of the 37th Winter Simulation Conference, 4–7 December 2005, Orlando, FL. IEEE Press: New York, pp 395–411.
Acknowledgements
The authors thank the following people for their time and their help with data and model validation: Crispin Wickenden and Andrew Oliver from the NBS; the employees of the Southampton PTI Centre, particularly Mike Northcott; Tracey Lofting from Southampton General Hospital; Rob Hick and Judith Chapman from the Blood Stocks Management Scheme. The authors would also like to thank Dr Mark Elder (founder and CEO of Simul8 Corporation) for providing the Simul8 licences and his generous ongoing support. Finally, the authors would like to acknowledge the contribution of Dr Allan Tucker, John Saville and Dr Steven Swift from Brunel University who generously offered assistance during the experimentation process.
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Katsaliaki, K., Mustafee, N., Taylor, S. et al. Comparing conventional and distributed approaches to simulation in a complex supply-chain health system. J Oper Res Soc 60, 43–51 (2009). https://doi.org/10.1057/palgrave.jors.2602531
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DOI: https://doi.org/10.1057/palgrave.jors.2602531