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An Advanced Modeling & Simulation Tool for Investigating the Behavior of a Manufacturing System in the Hazelnuts Industry Sector

  • Agostino G. Bruzzone and Francesco Longo EMAIL logo
Published/Copyright: September 27, 2013

Abstract: This article proposes an advanced java-based simulation tool developed in order to support decision making in a manufacturing system operating in the hazelnuts industry sector. By providing the user with high flexibility in terms of manufacturing scenarios definition, the simulation tool can be used as an advanced decision-making tool to understand the dynamic interactions between multiple performance measures (that include both production lines and inventory system performances) and a set of user-defined factors (the latter defined by the production manager according to his/her needs in terms of manufacturing scenarios investigation). To understand how the simulation model can be used as decision-making tool and for testing the tool effectiveness, simulation results analyses and analytical meta-models are provided for different manufacturing scenarios.

References

1. GroumposPP, MerkuryevY. A methodology of discrete-event simulation of manufacturing systems: an overview. Stud Inform Control2002;11:5360.Search in Google Scholar

2. BruzzoneAG, TremoriA, TaroneF, MadeoF. Intelligent Agents driving computer generated forces for simulating human behavior in urban riots. International Journal of Simulation and Process Modeling2011c;6(4):30816.10.1504/IJSPM.2011.048011Search in Google Scholar

3. CallahanRN, HubbardKM, BacoskiNM. The use of simulation modelling and factorial analysis as a method for process flow improvement. Adv Manufacturing Technol2006;29:2028.10.1007/s00170-004-2497-5Search in Google Scholar

4. HusseinWB, HeckerF, MitzscherlingM, BeckerT. Computer modelling and simulation of bakeries’ production planning. Int J Food Eng2009;5:Article number 8.10.2202/1556-3758.1565Search in Google Scholar

5. ReinerG, TrckaM. Customized supply chain design: problems and alternatives for a production company in the food industry. A simulation based analysis. Int J Production Econ2004;89:21729.10.1016/S0925-5273(03)00054-9Search in Google Scholar

6. HudaAM, ChungCA. Simulation modeling and analysis issues for high-speed combined continuous and discrete food industry manufacturing processes. Comput Ind Eng2002;43:47383.10.1016/S0360-8352(02)00120-1Search in Google Scholar

7. LipnizkiF, OlssonJ, TrägårdhG. Scale-up of pervaporation for the recovery of natural aroma compounds in the food industry. Part 1: simulation and performance. J Food Eng2002;54:18395.10.1016/S0260-8774(01)00200-XSearch in Google Scholar

8. BanksJ. Handbook of simulation. USA: John Wiley & Sons, 1998.Search in Google Scholar

9. Eben-ChaimeM, PliskinN, SosnaD. An integrated architecture for simulation. Comput Ind Eng2004;46:15970.10.1016/j.cie.2004.01.005Search in Google Scholar

10. KaracalSC. A novel approach to simulation modeling. Computers Ind Eng1998;34:57387.10.1016/S0360-8352(97)00324-0Search in Google Scholar

11. BruzzoneAG. Preface to modeling and simulation methodologies for logistics and manufacturing optimization. Simulation2004;80:11920. ISSN: 0037–5497. DOI:10.1177/0037549704045812.Search in Google Scholar

12. SmithJS. Survey in the use of simulation for manifacturing system design and operation. J Manufacturing Syst2003;22:15771.10.1016/S0278-6125(03)90013-6Search in Google Scholar

13. BoccaE, LongoF. Simulation tools, ergonomics principles and work measurement analysis for workstation design. Proceedings of the Summer Computer Simulation Conference 2008, Edinburgh, Scotland, June 16–19. 2008;4816Search in Google Scholar

14. BerryWL. Priority scheduling and inventory control in a job shop lot manufacturing systems. AIIE Trans1972;4:26776.10.1080/05695557208974862Search in Google Scholar

15. NunnikhovenTS, EmmonsH. Scheduling on parallel machines to minimize two criteria related to job tardiness. AIIE Trans1977;3:28896.10.1080/05695557708975157Search in Google Scholar

16. StengerAJ. Reducing inventories in a multi-echelon manufacturing firm: a case study. Production Econ1996;45:23949.10.1016/0925-5273(94)00146-4Search in Google Scholar

17. MullarkeyP, GavirneniS, MorriceDJ. Dynamic output analysis for simulations of manufacturing environments. Proceedings of the 2000 Winter Simulation Conference, December 10–13, Orlando, FL, 2000:12906.Search in Google Scholar

18. LongoF, MasseiM, NicolettiL. An application of modeling and simulation to support industrial plants design. Int J Modeling Simulation Sci Comput2012;3:1240001-11240001-26. ISSN: 1793-9623. DOI:10.1142/S1793962312400016.Search in Google Scholar

19. RenL, ZhangL, TaoF, ZhangX, LuoY, ZhangY. A methodology towards virtualisation-based high performance simulation platform supporting multidisciplinary design of complex products. Enterprise Inf Syst2012;6:26790.10.1080/17517575.2011.592598Search in Google Scholar

20. AlinoviA, BottaniE, MontanariR. Reverse logistics: a stochastic EOQ-based inventory control model for mixed manufacturing/remanufacturing systems with return policies. Int J Production Res2012;50:124364.10.1080/00207543.2011.571921Search in Google Scholar

21. AndradottirS. An overview of simulation optimization via random search. In: HendersonSG, NelsonBL, editors. Handbooks in operations research and management science: simulation, Chapter 21. Netherlands: Elsevier, 2005.Search in Google Scholar

22. FulcherJ. Computational intelligence: an introduction. Stud Computational Intelligence2008;115:378.10.1007/978-3-540-78293-3_1Search in Google Scholar

23. BehamA, KoflerM, WagnerS, AffenzellerM. Coupling simulation with HeuristicLab to solve facility layout problems. Proceedings of the 2009 Winter Simulation Conference, Austin, TX, 2009:220517.10.1109/WSC.2009.5429238Search in Google Scholar

24. De FeliceF, PetrilloA. Productivity analysis through simulation technique to optimize an automated assembly line. Proceedings of the IASTED International Conference, June 25–27, Napoli, Italy. Applied Simulation and Modelling (ASM 2012), 2012:3542. DOI:10.2316/P.2012.776-048.Search in Google Scholar

25. MotaMM, PieraMA. A compact timed state space approach for the analysis of manufacturing systems: key algorithmic improvements. Int J Comput Integr Manufacturing2011;24:13553.10.1080/0951192X.2010.543153Search in Google Scholar

26. BruzzoneAG, LongoF. An advanced system for supporting the decision process within large scale retail stores. Simulation2010;86:74262. DOI:10.1177/0037549709348801.Search in Google Scholar

27. BruzzoneAG, LongoF. 3D simulation as training tool in container terminals: the TRAINPORTS Simulator. J Manufacturing Syst2013;32:8598.10.1016/j.jmsy.2012.07.016Search in Google Scholar

28. CurcioD, LongoF. Inventory and internal logistics management as critical factors affecting the supply chain performances. Int J Simulation Process2009;5:27888. ISSN: 1740-2123.10.1504/IJSPM.2009.032591Search in Google Scholar

29. Del Rio VilasD, LongoF, Rego MontelN. A combined ergonomic and operational optimization approach for the manufacturing wokrstation design: a general framework. Simulation2013;89:30629.10.1177/0037549712462862Search in Google Scholar

30. SilverEA, PykeDF, PetersonR. Inventory management and production planning and scheduling, 3rd ed. USA: Wiley & Sons, 1998.Search in Google Scholar

31. MontgomeryDC, RungerGC. Applied statistics and probability for engineers. USA: John Wiley & Sons, 2006.Search in Google Scholar

32. BalciO. Verification, validation and testing. In: BanksJ, editor. Handbook of simulation. USA: John Wiley & Sons, 1998:33593.Search in Google Scholar

33. LongoF. Design and integration of the containers inspection activities in the container terminal operations. Int J Production Econ2010;125:27283. ISSN: 0925-5273. DOI:10.1016/j.ijpe.2010.01.026.Search in Google Scholar

34. Bruzzone, AG, Fadda, P, Fancello, G. Massei, M, Bocca, E, Tremori, A, Tarone, F, D’Errico, G. Logistics node simulator as an enabler for supply chain development: innovative portainer simulator as the assessment tool for human factors in port cranes. Simulation2011a;87(10):857874.10.1177/0037549711418688Search in Google Scholar

Published Online: 2013-09-27

©2013 by Walter de Gruyter Berlin / Boston

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