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Simulation Approach for Logistical Planning in a Warehouse:A Review


Affiliations
1 Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia
2 Multimedia University, 75450 Ayer Keroh, Melaka, Malaysia
3 Silterra Malaysia Sdn Bhd, Kulim Hi-Tech Park, 09000, Kulim, Kedah, Malaysia
 

Simulation approach has been widely utilized for design and planning of a warehouse logistic system. As technology advances, better simulation tools have been developed. This enables decision makers to create more realistic models to represent the actual scenario. As a result, better evaluation could be made especially regarding dynamic factors within system. Thus, complex logistic problems could be solved. This paper reviews the fundamentals of warehouse logistic and the simulation tools available to analyze logistic problems.

Keywords

Automated Guided Vehicle, Material Transportation System, Simulation, Warehouse Logistic.
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  • I.Bychkov, G. Oparin, A. Tchernykh, A. Feoktistov, V. Bogdanova, Yu Dyadkin, V. Andrukhova, and O. Basharina, “Simulation modeling in heterogeneous distributed computing environments to support decisions making in warehouse logistics.” Procedia Engineering 201, pp. 524–533, 2017.

  • I. Sulírova, Ľ. Závodska, M. Rakyta, V. Pelantova, “State-of-the-art approaches to material transportation, handling and warehousing,” Procedia Engineering, vol. 192, pp. 857 – 862, 2017

  • M.H.F.B.M. Fauadi, S.H. Yahaya, T. Murata, “Intelligent combinatorial auctions of decentralized task assignment for AGV with multiple loading capacity,” IEEJ Transactions on electrical and electronic Engineering, vol. 8 no. 4, pp.371-379, 2013.

  • R. De Koster, T. Le-Duc, and K.J. Roodbergen, “Design and control of warehouse order picking: A literature review,” European journal of operational research, vol. 182 no. 2, pp.481-501, 2007.

  • E. H. Grosse, C. H. Glock, M. Y. Jaber & W. P. Neumann, “Incorporating human factors in order picking planning models: framework and research opportunities,” International Journal of Production Research, vol. 53, no. 3, pp. 695-717, 2015.

  • J.A. Tompkins, J.A. White, Y.A. Bozer, E.H. Frazelle, and J.M.A. Tanchoco, Facilities Planning, NJ: John Wiley & Sons, 2003.

  • S. Bandyopadhyay (ed.): Intelligent Vehicles and Materials Transportation in the Manufacturing Sector: Emerging Research and Opportunities: Emerging Research and Opportunities, IGI Global, 2017.

  • D. Culler and J. Long, “A Prototype Smart Materials Warehouse Application Implemented using Custom Mobile Robots and Open Source Vision Technology Developed using EmguCV,” Procedia Manufacturing. vol. 5, pp. 1092–1106, 2016.

  • M. Shneier, R. Bostelman: Literature Review of Mobile Robots for Manufacturing, National Institute of Standards and Technology, US Department of Commerce, 2015.

  • M.H.F.B.M. Fauadi, W.L. Li, and T. Murata, “Combinatorial auction method for decentralized task assignment of multiple-loading capacity AGV based on intelligent agent architecture,” In Innovations in Bio-inspired Computing and Applications (IBICA), IEEE 2011 Second International Conference on, 2011, pp. 207-211.

  • L.F.V. Leite, R.M.A. Esposito, A.P. Vieira, F. Lima, “Simulation of a production line with automated guided vehicles: A Case Study,” Independent journal of management & production, vol. 6 no. 2, pp. 269-285, 2015.

  • A. Negahban, J.S. Smith, “Simulation for manufacturing system design and operation: Literature review and analysis,” Journal of Manufacturing Systems, vol. 33, no. 2, pp. 241-261, 2014.

  • Y.D. Zhan, Y.G. Guo, J.G. Zhu, “Intelligent coordination steering control of automated guided vehicle,” In Applied Superconductivity and Electromagnetic Devices (ASEMD), 2011 IEEE International Conference on, 2011, pp. 204-207.

  • V. Vavrik, M. Gregor, P. Grznar, “Computer simulation as a tool for the optimization of logistics using automated guided vehicles,” Procedia engineering, vol. 192, pp.923-928, 2017

  • M.H.F. bin Md Fauadi, H. Lin, T. Murata, “Dynamic task assignment of autonomous AGV system based on multi agent architecture,” In Progress in Informatics and Computing (PIC), 2010 IEEE International Conference on, vol. 2, 2010, pp. 1151-1156.

  • M.H.F. bin Md Fauadi, H.W. Lin, and T. Murata, “Dynamic Task Assignment of Autonomous Distributed AGV in an Intelligent FMS Environment,” IEEJ Transactions on Electronics, Information and Systems, vol. 132 no. 5, pp.697-705, 2012

  • P. Sharma, A. Jain, “Stochastic Dynamic Job Shop Scheduling with Sequence Dependent Setup Times: Simulation Experimentation. Journal of Engineering and Technology”, vol. 5, pp. 19-25, 2017.

  • R. Pisuchpen, “A simulation of AGV dispatching rules for supplying parts to assembly lines,” International Journal of Modelling in Operations Management vol. 2, no. 1, pp. 1-25, 2012

  • K. Vivaldini, L.F. Rocha, N.J. Martarelli, M. Becker, A.P. Moreira, “Integrated tasks assignment and routing for the estimation of the optimal number of AGVS,” The Inter national Journal of Advanced Manufacturing Technology, vol. 82 no. 1-4, pp. 719-736, 2016.

  • I.F.A.Vis, “Survey of research in the design and control of automated guided vehicle systems,” European Journal of Operational Research, vol. 170, no. 3, pp. 677-709, 2006.

  • J.W. Hauge, K.N. Paige: Learning SIMUL8: The Complete Guide, Second Edition, Plain Vu Publishers, 2004.

  • T.L. Garwood, B.R. Hughes, M.R. Oates, D. O’Connor, R. Hughes, “A review of energy simulation tools for the manufacturing sector,” Renewable and Sustainable Energy Reviews, vol. 81, pp.895-911, 2018.

  • O. Omogbai, K. Salonitis,. “Manufacturing system lean improvement design using discrete event simulation,” Procedia CIRP, vol. 57, pp.195-200, 2016.

  • M. Badr, M.M.A. Sayed, A.E.R. Aref, and A. Salah, “New Model for Material Transportation to Improve Efficiency of Production Line,” International Journal of Science and Qualitative Analysis, vol. 4 no. 2, pp. 60-64, 2018.

  • D. Benson, “Simulation modeling and optimization using ProModel,” In Proceedings of the 29th conference on Winter simulation- IEEE Computer Society, 1997, pp. 587-593.

  • M. Kljajic, I. Bernik, A. Skraba, “Simulation approach to decision assessment in enterprises,” Simulation, vol. 75 no. 4, pp.199-210, 2000.

  • A. Borshchev: The Big Book of Simulation Modeling: Multimethod Modeling with Anylogic 6, AnyLogic North America, 2013.

  • A.B. Nikolaev, V.S. Starikov, G.G. Yagudaev, “Analytical And Simulation Planning Model Of Urban Passenger Transport,” International Journal of Advanced Studies, vol. 7 no. 1, pp. 65-77, 2017.

  • M.Topolski “Application of a telematics system to the improvement of transport processes in intelligent high bay warehouses,” Archives of Transport System Telematics, vol. 9, no. 2, pp. 52-56, 2016.

  • FlexSim Software Products, Inc. FlexSim 3d Simulation Software- User Manual FlexSim 2017 Update 1. 2017.

  • B.S. Kumar, V. Mahesh, B.S. Kumar, “Modeling and analysis of flexible manufacturing system with FlexSim,” International Journal of Computational Engineering Research, vol. 5 no. 10, pp. 1-6, 2015.

  • Y.R. Wang, A.N. Chen, “Production logistics simulation and optimization of industrial enterprise based on Flexsim,” International Journal of Simulation Modelling, vol. 15 no. 4, pp.732-741, 2016.

  • F. De Felice, A. Petrillo, F. Zomparelli, “Prospective design of smart manufacturing: An Italian pilot case study,” Manufacturing Letters, vol. 15, pp.81-85, 2018.

  • J. Garrido, “Introduction to Arena,” In: Object Oriented Simulation, Springer, Boston, MA, 2009, pp. 19-29.

  • Rockwell Software Inc. Arena User's Guide (Doc ID ARENA-UM001A-EN-P). Rockwell Software Inc. 2004

  • S.B. Emami, S.M. Arabzad, S.M. Sajjadi, “A simulation study on warehouse loading system: the case of poultry feed production factory,” International Journal of Logistics Systems and Management, vol. 19 no. 3, pp.347-355, 2014.

  • R. Peixoto, L. Dias, M.S. Carvalho, G. Pereira, C.A. Geraldes, “An automated warehouse design validation using discrete simulation,” In Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on, 2016, pp. 199-204.

  • R. Al Aomar, E.J. Williams, O.M. Ülgen: Process Simulation Using WITNESS, John Wiley & Sons, Inc. 2015

  • E.M. Abogrean, M. Latif, “Effective maintenance enabled by the use of witness simulation in a Libyan cement factory,” International Journal of Advances in Management and Economics, vol.1 no. 5, pp. 138-147, 2018.

  • J. Dobránsky, P. Baron, E. Vojnová, D. Manduľák, “Optimization of the production and logistics processes based on computer simulation tools,” Key Engineering Materials, vol. 669, pp. 532-540, 2016.


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  • Simulation Approach for Logistical Planning in a Warehouse:A Review

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Authors

M. H. F. Md. Fauadi
Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia
N. Z. Azimi
Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia
N. I. Anuar
Multimedia University, 75450 Ayer Keroh, Melaka, Malaysia
M. M. Ali
Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia
M. K. Sued
Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia
S. Ramlan
Silterra Malaysia Sdn Bhd, Kulim Hi-Tech Park, 09000, Kulim, Kedah, Malaysia

Abstract


Simulation approach has been widely utilized for design and planning of a warehouse logistic system. As technology advances, better simulation tools have been developed. This enables decision makers to create more realistic models to represent the actual scenario. As a result, better evaluation could be made especially regarding dynamic factors within system. Thus, complex logistic problems could be solved. This paper reviews the fundamentals of warehouse logistic and the simulation tools available to analyze logistic problems.

Keywords


Automated Guided Vehicle, Material Transportation System, Simulation, Warehouse Logistic.

References





DOI: https://doi.org/10.13005/ojcst11.04.05