5 research outputs found

    Hybrid approach of discrete event simulation integrated with location search algorithm in a cells assignment problem: a case study

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    [EN] This paper presents a case study describing a cell assignment problem in an assembly facility. These cells receive parts from external suppliers, and sort and sequence these parts to feed the final assembly line. Therefore, to each cell are associated important inbound and outbound flows generating hundreds of material handling equipment movements along the facility, impacting the traffic density and causing eventually safety issues in the plant. Following an important plant redesign, cells have been relocated, and the plant managers need to decide how to manage the new logistic flows. To that aim, a hybrid approach encompassing mathematical optimization and discrete event simulation (DES) is proposed. This approach allows us to reduce complexity by decomposing the design into two phases. The first phase deals with the problem of generating cell¿s assignment alternatives by using a heuristic method to find good quality solutions. Then, a DES software is used to dynamically evaluate the performance of the solutions with respect to operational features such as traffic congestion and intensity. This second phase provides interesting managerial insights on the manufacturing system from both quantitative and qualitative aspects related to in-plant safety and traffic.Saez-Mas, A.; García Sabater, JJ.; García Sabater, JP.; Maheut, J. (2020). Hybrid approach of discrete event simulation integrated with location search algorithm in a cells assignment problem: a case study. Central European Journal of Operations Research. 28(1):125-142. https://doi.org/10.1007/s10100-018-0548-5S125142281Anjos MF, Vieira MVC (2017) Mathematical optimization approaches for facility layout problems: the state-of-the-art and future research directions. Eur J Oper Res 261(1):1–16. https://doi.org/10.1016/j.ejor.2017.01.049Battini D, Boysen N, Emde S (2013) Just-in-time supermarkets for part supply in the automobile industry. J Manag Control 24(2):209–217. https://doi.org/10.1007/s00187-012-0154-yBenjaafar S (2002) Modeling and analysis of congestion in the design of facility layouts. Manag Sci 48(5):679–704. https://doi.org/10.1287/mnsc.48.5.679.7800Board TR (2010) Highway capacity manual. Environmental ProtectionBoysen N, Emde S, Hoeck M, Kauderer M (2015) Part logistics in the automotive industry: decision problems, literature review and research agenda. Eur J Oper Res 242(1):107–120. https://doi.org/10.1016/j.ejor.2014.09.065Caputo AC, Pelagagge PM, Salini P (2015) Modeling errors in kitting processes for assembly lines feeding. IFAC Proc Vol (IFAC PapersOnline) 48(3):338–344. https://doi.org/10.1016/j.ifacol.2015.06.104Centobelli P, Cerchione R, Murino T (2016) Layout and material flow optimization in digital factory. Int J Simul Model 15(2):223–235. https://doi.org/10.2507/IJSIMM15(2)3.327Dehghanimohammadabadi M, Keyser TK (2017) Intelligent simulation: integration of SIMIO and MATLAB to deploy decision support systems to simulation environment. Simul Model Pract Theory 71:45–60. https://doi.org/10.1016/j.simpat.2016.08.007Ficko M, Palcic I (2013) Designing a layout using the modified triangle method, and genetic algorithms. Int J Simul Model 12(4):237–251. https://doi.org/10.2507/IJSIMM12(4)3.244Gamberi M, Manzini R, Regattieri A (2009) An new approach for the automatic analysis and control of material handling systems: integrated layout flow analysis (ILFA). Int J Adv Manuf Technol 41(1–2):156–167. https://doi.org/10.1007/s00170-008-1466-9Gould O, Colwill J (2015) A framework for material flow assessment in manufacturing systems. J Ind Prod Eng 32(1):55–66. https://doi.org/10.1080/21681015.2014.1000403Hasda RK, Bhattacharjya RK, Bennis F (2016) Modified genetic algorithms for solving facility layout problems. Int J Interact Des Manuf (IJIDeM) 11(3):1–13. https://doi.org/10.1007/s12008-016-0362-zImran M, Kang C, Hae Lee Y, Zaib J, Aziz H (2017) Cell formation in a cellular manufacturing system using simulation integrated hybrid genetic algorithm. Comput Ind Eng 105:123–135. https://doi.org/10.1016/j.cie.2016.12.028Iqbal M, Hashmi MSJ (2001) Design and analysis of a virtual factory layout. J Mater Process Technol 118(1–3):403–410. https://doi.org/10.1016/S0924-0136(01)00908-6Jainury SM, Ramli R, Ab Rahman MN, Omar A (2014) Integrated Set Parts Supply system in a mixed-model assembly line. Comput Ind Eng 75(1):266–273. https://doi.org/10.1016/j.cie.2014.07.008Kanduc T, Rodic B (2016) Optimisation of machine layout using a force generated graph algorithm and simulated annealing. Int J Simul Model 15(2):275–287. https://doi.org/10.2507/IJSIMM15(2)7.335Kang J (2001) A new trend of parts supply system in Korean automobile industry; the case of the modular production system at Hyundai Motor Company. In: Proceedings of the fifth Russian-Korean international symposium on science and technology, 2001. KORUS '01. IEEE, Tomsk, Russia, Russia. https://doi.org/10.1109/KORUS.2001.975268Kim J, Yu G, Jang YJ (2016) Semiconductor FAB layout design analysis with 300-mm FAB data: “is minimum distance-based layout design best for semiconductor FAB design?”. Comput Ind Eng 99:330–346. https://doi.org/10.1016/j.cie.2016.02.012Krishnan KK, Jithavech I, Liao H (2009) Mitigation of risk in facility layout design for single and multi-period problems. Int J Prod Res 47(21):5911–5940. https://doi.org/10.1080/00207540802175337Ku M-Y, Hu MH, Wang M-J (2011) Simulated annealing based parallel genetic algorithm for facility layout problem. Int J Prod Res 49(6):1801–1812. https://doi.org/10.1080/00207541003645789Kulturel-Konak S (2017) A matheuristic approach for solving the dynamic facility layout a matheuristic approach for problem solving the dynamic facility layout problem. Proc Comput Sci 108(June):1374–1383. https://doi.org/10.1016/j.procs.2017.05.234Leveson N (2004) A new accident model for engineering safer systems. Saf Sci 42(4):237–270. https://doi.org/10.1016/S0925-7535(03)00047-XNegahban A, Smith JS (2014) Simulation for manufacturing system design and operation: literature review and analysis. J Manuf Syst 33(2):241–261. https://doi.org/10.1016/j.jmsy.2013.12.007Prajapat N, Tiwari A (2017) A review of assembly optimisation applications using discrete event simulation. Int J Comput Integr Manuf 30(2–3):215–228. https://doi.org/10.1080/0951192X.2016.1145812Saez-Mas A, Garcia-Sabater JP, Morant-Llorca J (2018) Using 4-layer architecture to simulate product and information flows in manufacturing. Int J Simul Model 17(1):30–41. https://doi.org/10.2507/IJSIMM17(1)408Seebacher G, Winkler H, Oberegger B (2015) In-plant logistics efficiency valuation using discrete event simulation. Int J Simul Model 14:60–70. https://doi.org/10.2507/IJSIMM14(1)6.289Singh RR, Sharma SPK (2006) A review of different approaches to the facility layout problems. Int J Adv Manuf Technol 30(5–6):425–433. https://doi.org/10.1007/s00170-005-0087-9Tompkins J, White J, Bozer Y, Tanchoco J (2003) Facilities planning. Wiley, New YorkTugnoli A, Khan F, Amyotte P, Cozzani V (2008) Safety assessment in plant layout design using indexing approach: implementing inherent safety perspective. Part 1—guideword applicability and method description. J Hazard Mater 160(1):100–109. https://doi.org/10.1016/j.jhazmat.2008.02.089Zhang M, Batta R, Nagi R (2009) Modeling of workflow congestion and optimization of flow routing in a manufacturing/warehouse facility. Manag Sci 55(2):267–280. https://doi.org/10.1287/mnsc.1080.0916Zhou F, AbouRizk SM, AL-Battaineh H (2009) Optimisation of construction site layout using a hybrid simulation-based system. Simul Model Pract Theory 17(2):348–363. https://doi.org/10.1016/j.simpat.2008.09.011Zhuo L, Chua Kim Huat D, Wee KH (2012) Scheduling dynamic block assembly in shipbuilding through hybrid simulation and spatial optimisation. Int J Prod Res 50(20):5986–6004. https://doi.org/10.1080/00207543.2011.639816Zupan H, Herakovic N, Starbek M (2016) hybrid algorithm based on priority rules for simulation of workshop production. Int J Simul Model 15(1):29–41. https://doi.org/10.2507/IJSIMM15(1)3.31

    Using 4-layer architecture to simulate product and information flows in manufacturing systems

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    [EN] This work illustrates the application of novel simulation architecture with two case studies where the proposed architecture, the so-called 4-layer, allowed us to address the complexity of the analysed systems. The fundamental objective of this work is to show the structure of layers, how layers interact with one another and with the user, and what benefits this separation proposes. The first case study deals with moving car bodies from the paint plant to the assembly line through a sequencing system that involves distributed decision-making processes in an ASRS. The second case study focuses on analysing a layout of a section used to assemble the engine and transmission set, where the quality of the material flow is evaluated. The work highlights some of the advantages of modelling with 4-layer architecture, and explains the key processes that connect different elementsSaez-Mas, A.; GarcĂ­a Sabater, JP.; Morant -Llorca, J. (2018). Using 4-layer architecture to simulate product and information flows in manufacturing systems. International Journal of Simulation Modelling. 17(1):30-41. https://doi.org/10.2507/IJSIMM17(1)408S304117

    Hybrid approach of discrete event simulation integrated with location search algorithm in a cells assignment problem: a case study

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    Hybrid approach of discrete event simulation integrated with location search algorithm in a cells assignment problem: a case stud

    Hybrid approach of discrete event simulation integrated with location search algorithm in a cells assignment problem: a case study

    No full text
    Hybrid approach of discrete event simulation integrated with location search algorithm in a cells assignment problem: a case stud

    Redesigning the in-plant supply logistics: A case study

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    [EN] This paper addresses the redesign of an industrial assembly facility's internal logistics. To this end, it proposes a mathematical formulation that optimizes the components and parts' flow to feed the different workstations of the production lines. This flow of components starts at the reception docks where suppliers' trucks arrive. Components unloaded from trucks are moved to one or several storage areas by means of adequate handling equipment. Finally, components are transported to demand point located along the assembly line when required. Numerical results produced by the mathematical formulation for the studied plant show that savings of almost 33% in the total distribution time might be achieved by a better assignment of suppliers to reception docks and parts to storage areas, and by adequately choosing the capacity of the material handling equipment.The work described in this paper has been partially supported by the project "Hiperheuristico Lenitivo de la Variabilidad del Entorno Industrial en la Programacion de Produccion del Lote Econonimo GVA/2017/008" by the Conselleria de Educacion, Investigacion, Cultura y Deporte of the Generalitat Valenciana within the Program "Proyectos de I+D+I para grupos de investigacion emergentes". We would like to thank the two anonymous reviewers and the editor for their valuable comments and suggestions.Saez-Mas, A.; García Sabater, JP.; García Sabater, JJ.; Ruiz, A. (2020). Redesigning the in-plant supply logistics: A case study. 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