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Comparative Simulation Study for Configuring Turning Point in Multiple Robot Path Planning: Robust Data Envelopment Analysis

Published online by Cambridge University Press:  24 July 2019

Hamed Fazlollahtabar Open the ORCID record for Hamed Fazlollahtabar [Opens in a new window]
Hamed Fazlollahtabar*
Affiliation:
Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran.
*
*Corresponding author. E-mail: hfazl@du.ac.ir; hfazl@iust.ac.ir
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Summary

This paper concerns with comparing simulation studies for a newly developed concept of turning point to be used in multiple robot path planning. Different critical factors and design parameters are collected and statistical analyses are performed. After configuring different simulation scenarios, the efficient one is evaluated using a robust data envelopment analysis (RDEA). Due to uncertain aspects of various simulations scenarios, robust version of data envelopment analysis is proposed. Here, major criteria in robot path planning are deadlock and conflict avoidance, throughput, mean flow time, and effective total distance travelled. To determine the effective experiment for the proposed simulation model, RDEA is used. A comparative study with respect to different experiments having various simulation setting is developed. The results for a real robotic manufacturing cell system show effectiveness of the proposed process. Also, the efficient simulation software is determined by multiaspect analysis.

Keywords

RobotTurning pointSimulationPath planning
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 925 - 939
Copyright
© Cambridge University Press 2019

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References

Gould, L., “AGVs in America: An inside look,” Mod. Mater. Handling 45(10), 56–60 (1990).Google Scholar
Sezen, B., “Modeling automated guided vehicle systems in material handling,” Dogus Üniversitesi Dergisi 4(2), 207–216 (2003).CrossRefGoogle Scholar
Fazlollahtabar, H. and Saidi-Mehrabad, M., Autonomous Guided Vehicles: Methods and Models for Optimal Path Planning (Springer International Publishing, Cham, 2015). ISBN 978-3-319-14746-8.CrossRefGoogle Scholar
Fazlollahtabar, H. and Saidi-Mehrabad, M., “Methodologies to optimize automated guided vehicle scheduling and routing problems: A review study,” J. Intell. Robot. Syst. 77, 525–545 (2015a).CrossRefGoogle Scholar
Nouri, H. E., Driss, O. B. and Ghédira, K., “A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State-of-the-Art Review,” In: Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing (Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P. and Prokopova, Z., eds.), vol. 464 (Springer, Cham, 2016).Google Scholar
Luo, J., Wu, Y. and Mendes, A. B., “Modelling of integrated vehicle scheduling and container storage problems in unloading process at an automated container terminal,” Comput. Ind. Eng. 94, 32–44 (2016).CrossRefGoogle Scholar
Cardarelli, E., Digani, V., Sabattini, L., Secchi, C. and Fantuzzi, C., “Cooperative cloud robotics architecture for the coordination of multi-AGV systems in industrial warehouses,” Mechatronics 45, 1–13 (2017).CrossRefGoogle Scholar
Vis, I. F. A., “Survey of research in the design and control of automated guided vehicle systems,” Eur. J. Oper. Res. 170(3), 677–709 (2006).CrossRefGoogle Scholar
Vis, I. F. A., Koster, R. D., Roodbergen, K. J. and Peeters, L. W. P., “Determination of the number of AGVs required at a semiautomated container terminal,” J. Oper. Res. Soc. 52, 409–417 (2001).CrossRefGoogle Scholar
Arifin, R. and Egbelu, P. J., “Determination of vehicle requirements in automated guided vehicle systems: A statistical approach,” Prod. Planning Control 11(3), 25870 (2000).CrossRefGoogle Scholar
Varagul, J. and Ito, T., “Simulation of detecting function object for AGV using computer vision with neural network,” Procedia. Comput. Sci. 96, 159–168 (2016).CrossRefGoogle Scholar
Antakly, D., Loiseau, J. J. and Abbou, R., “A temporised conflict-free routing policy for AGVs,” IFACPapersOnLine 50(1), 11169–11174 (2017).Google Scholar
Valmiki, P., Simha Reddy, A., Panchakarla, G., Kumar, K., Purohit, R. and Suhane, A., “A study on simulation methods for AGV fleet size estimation in a flexible manufacturing system,” Mater. Today Proc. 5(2–1), 3994–3999 (2018).CrossRefGoogle Scholar
Tanchoco, J. M. A., Material Flow Systems in Manufacturing (Chapman & Hall, London, 1994).CrossRefGoogle Scholar
Gupta, A., Singh, K. and Verma, R., “A critical study and comparison of manufacturing simulation software using analytic hierarchy process,” J. Eng. Sci. Technol. 5(1), 108–129 (2010).Google Scholar
Manup, B. and Raja, P., “Collision-avoidance for mobile robots using region of certainty: a predictive approach,” J. Eng. Sci. Technol. 11(1), 18–28 (2016).Google Scholar
Um, I., Cheon, H. and Lee, H., “The simulation design and analysis of a Flexible Manufacturing System with Automated Guided Vehicle System,” Journal of Manufacturing Systems 28(4), 115–122 (2009).CrossRefGoogle Scholar
Tanchoco, J. M. A., Egbelu, P. J. and Taghaboni, F., “Determination of the total number of vehicles in an AGV-based material transport system,” Mater. Flow 4, 33–51 (1987).Google Scholar
Ott, R. L., An Introduction to Statistical Methods and Data Analysis (Dexbury Press, Belmont, 1993).Google Scholar
Charnes, A., Cooper, W. W. and Rhodes, E., “Measuring the efficiency of decision making units,” Eur. J. Oper. Res. 2(6), 429–444 (1978).CrossRefGoogle Scholar
Fazli-Khalaf, M., Mirzazadeh, A. and Pishvaee, M. S., “A robust fuzzy stochastic programming model for the design of a reliable green closed loop supply chain network,” Hum. Ecol. Risk Assess. Int. J. (2017). doi:10.1080/10807039.2017.1367644.CrossRefGoogle Scholar
Yousefi, A., “Selecting six sigma project: A comparative study of DEA and LDA techniques,” 2008 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore (2008).Google Scholar