Automated Guided Vehicle Scheduling in a Complex Network using Simulation Based Optimization
Keywords:
AGV scheduling- Discrete event simulation- Optimization- Flexible manufacturing systems
Abstract
Abstract
Transportation is one of the important cases in flexible manufacturing systems. One of the means of transportation in flexible manufacturing systems is AGVs. Given that path planning of AGVs is of NP-Hard type problems, metaheuristic algorithms or simulation method should be used for the analysis of such problems. In this study, AGVs motion in a complicated network of a manufacturing system was explored and the purpose was to determine the best distribution rule of AGVs in machines and the number of AGVS to decrease the waiting time of parts in warehouses. Considering that the time of manufacturing of the parts by machines as well as AGVs' motion and their Load and Unload time are probable, discrete event simulation is an efficient tool in analysis of the problem. The results of this study revealed efficiency of the proposed method when analytical solution is not possible.
References
References
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[2] R. Tavakkoli-Moghaddam , M. B. Aryanezhad & H. Kazemipoor & A. Salehipour, Partitioning machines in tandem AGV systems based on “balanced flow strategy” by simulated annealing, Int J Adv Manuf Technol (2008) 38:355–366.
[3] H. Rashidi , E. P. K. Tsang, A complete and an incomplete algorithm for automated guided vehicle scheduling in container terminals, Computers and Mathematics with Applications (2011) 61: 630–641.
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[10] H. Hosseini Nasab, S. Barak, S.M. Hosseini , Hybrid Fuzzy Knowledge Based FMS Facility Layout with AGV using Imperialist Competitive Algorithm and Genetic Algorithm , International Journal of Industrial Engineering & Production Management , (2013) 24: 67-79
[11] A. Salehipour , M. M. Sepehri, Optimal location of workstations in tandem automated-guided vehicle systems, Int J Adv Manuf Technol, (2014) 72: 1429-143.
[12] M. Saidi-Mehrabad, S. Dehnavi-Arani , F. Evazabadian, V. Mahmoodian, An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs, Computers & Industrial Engineering, (2015).
[13] D. Pjevcevica, I. Vladisavljevica, K. Vukadinovica, , Dusan Teodorovica, Application of DEA to the analysis of AGV fleet operations in a port container terminal, Procedia Social and Behavioral Sciences, (2011) 20: 816–825.
[14] S. Jeon , K. Kim, H. Kopfer, Routing automated guided vehicles in container terminals through the Q-learning technique, Logist. Res. (2011) 3:19–27.
[15] E. Masehian, F. Barzinpour , S. Saedi, Mobile Robot Path Planning Using a New GA-based Method with Variable Chromosome Length, International Journal of Industrial Engineering & Production Management (2011) 22: 99-111.
[16] Mansor, Hasmah, Amir Hossein Zaeri, Samsul Bahari Mohd Noor, Raja Kamil Raja Ahmad, Farah Saleena Taip, and Hazem Ibrahim Ali. "Design of QFT controller for a bench-top helicopter system model." International Journal of Simulation System, Science & Technology 11, no. 5 (2010): 8-16.
[17] Hu, Hao, Xiaoliang Jia, Qixuan He, Shifeng Fu, and Kuo Liu. "Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0." Computers & Industrial Engineering 149 (2020): 106749.
[18] Ma, Ning, Chenhao Zhou, and Aloisius Stephen. "Simulation model and performance evaluation of battery-powered AGV systems in automated container terminals." Simulation Modelling Practice and Theory 106 (2021): 102146.
[19] Liu, Zhengchao, Shunsheng Guo, and Lei Wang. "Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption." Journal of cleaner production 211 (2019): 765-786.
[1] J. Jerald , P. Asokan , G. Prabaharan , R. Saravanan, Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm, Int J Adv Manuf Technol (2005) 25: 964–971.
[2] R. Tavakkoli-Moghaddam , M. B. Aryanezhad & H. Kazemipoor & A. Salehipour, Partitioning machines in tandem AGV systems based on “balanced flow strategy” by simulated annealing, Int J Adv Manuf Technol (2008) 38:355–366.
[3] H. Rashidi , E. P. K. Tsang, A complete and an incomplete algorithm for automated guided vehicle scheduling in container terminals, Computers and Mathematics with Applications (2011) 61: 630–641.
[4] L. Lin , S. Shinn, M. Gen , H. Hwang, Network model and effective evolutionary approach for AGV
dispatching in manufacturing system, J Intell Manuf (2006) 17:465–477.
[5] G. Confessore , M. Fabiano , G. Liotta, A network flow based heuristic approach for optimising AGV
Movements, J Intell Manuf (2013) 24:405–419.
[6] H. Fazlollahtabar , N. Mahdavi-Amiri, Producer’s behavior analysis in an uncertain bicriteria AGV-based flexible jobshop manufacturing system with expert system, Int J Adv Manuf Technol (2013) 65:1605–1618.
[7] P. Azimi, H. Haleh, M. Alidoost, The Selection of the Best Control Rule for a Multiple-Load AGV System Using Simulation and Fuzzy MADM in a FlexibleManufacturing System, Modelling and Simulation in Engineering, (2010), 11 pages.
[8] S. Kesen, O. Baykoc, Simulation of automated guided vehicle (AGV) systems based on just-in-time (JIT) philosophy in a job-shop environment, Simulation Modelling Practice and Theory (2007) 15: 272–284.
[9] R.Farahani , G. Laporte , E. Miandoabchi , S. Bina, Designing efficient methods for the tandem AGV network design problem using tabu search and genetic algorithm, Int J Adv Manuf Technol (2008) 36:996–1009.
[10] H. Hosseini Nasab, S. Barak, S.M. Hosseini , Hybrid Fuzzy Knowledge Based FMS Facility Layout with AGV using Imperialist Competitive Algorithm and Genetic Algorithm , International Journal of Industrial Engineering & Production Management , (2013) 24: 67-79
[11] A. Salehipour , M. M. Sepehri, Optimal location of workstations in tandem automated-guided vehicle systems, Int J Adv Manuf Technol, (2014) 72: 1429-143.
[12] M. Saidi-Mehrabad, S. Dehnavi-Arani , F. Evazabadian, V. Mahmoodian, An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs, Computers & Industrial Engineering, (2015).
[13] D. Pjevcevica, I. Vladisavljevica, K. Vukadinovica, , Dusan Teodorovica, Application of DEA to the analysis of AGV fleet operations in a port container terminal, Procedia Social and Behavioral Sciences, (2011) 20: 816–825.
[14] S. Jeon , K. Kim, H. Kopfer, Routing automated guided vehicles in container terminals through the Q-learning technique, Logist. Res. (2011) 3:19–27.
[15] E. Masehian, F. Barzinpour , S. Saedi, Mobile Robot Path Planning Using a New GA-based Method with Variable Chromosome Length, International Journal of Industrial Engineering & Production Management (2011) 22: 99-111.
[16] Mansor, Hasmah, Amir Hossein Zaeri, Samsul Bahari Mohd Noor, Raja Kamil Raja Ahmad, Farah Saleena Taip, and Hazem Ibrahim Ali. "Design of QFT controller for a bench-top helicopter system model." International Journal of Simulation System, Science & Technology 11, no. 5 (2010): 8-16.
[17] Hu, Hao, Xiaoliang Jia, Qixuan He, Shifeng Fu, and Kuo Liu. "Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0." Computers & Industrial Engineering 149 (2020): 106749.
[18] Ma, Ning, Chenhao Zhou, and Aloisius Stephen. "Simulation model and performance evaluation of battery-powered AGV systems in automated container terminals." Simulation Modelling Practice and Theory 106 (2021): 102146.
[19] Liu, Zhengchao, Shunsheng Guo, and Lei Wang. "Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption." Journal of cleaner production 211 (2019): 765-786.
Published
2021-09-01
How to Cite
Amelian, S. S., & Zaeri, A. H. (2021). Automated Guided Vehicle Scheduling in a Complex Network using Simulation Based Optimization. Majlesi Journal of Mechatronic Systems, 10(3), 1-5. Retrieved from https://ms.majlesi.info/index.php/ms/article/view/494
Section
Articles
