Data Envelopment Analysis-based Scenario Selection for Sequencing Pattern in a Simulated Robotic Cell

Bahareh Vaisi; Hiwa Farughi; Sadigh Raissi

doi:10.14500/aro.11668

Bahareh Vaisi Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0003-0404-0440
Hiwa Farughi Department of Engineering, University of Kurdistan, Sanandaj, Iran https://orcid.org/0000-0001-9745-9691
Sadigh Raissi School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran https://orcid.org/0000-0002-4939-1335

Keywords: Data envelopment analysis, Part sequencing, Robotic cell, Scenario design, Simulation

Abstract

In this study, the performance of suggested scenarios for part input sequences in a 3-machine robotic cell producing different parts is determined through the application of data envelopment analysis (DEA) and the Banker–Charnes–Cooper model. A single gripper robot supports the manufacturing process by loading and unloading products and moving them inside the system. This study addresses random machine failures and repairs to minimize cycle time based on two robot move cycles in a three-machine robotic cell and overall production costs. Here, simulation assists in the modeling of uncertainty and a simulation-based optimization approach is applied to find the best scenarios for sequencing patterns in the cell through several numerical examples using DEA. The results displayed that, efficient scenarios satisfying minimum time and cost, are those, in which the percentages of operations assigned to the machines are close to each other. This enables decision-makers in manufacturing systems to make precise selections of the optimal part sequencing pattern with the lowest production cost and cycle time for robotic cells.

Downloads

Download data is not yet available.

Author Biographies

Bahareh Vaisi, Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran

Bahareh Vaisi received her B.Sc. and M.Sc. degrees in the field of industrial engineering from Kurdistan University and Islamic Azad University, Qazvin Branch, and her Ph.D. degree from the School of Industrial Engineering, Islamic Azad University, South Tehran Branch (IAU-STB). She is a member of the Young Researchers and Elite Club in the Islamic Azad University. Her main research areas are multi-objective optimization, scheduling, system simulation, and data envelopment analysis.

Hiwa Farughi, Department of Engineering, University of Kurdistan, Sanandaj, Iran

Hiwa Farughi is a Professor at the University of Kurdistan. He received his B.Sc. and M.Sc. degrees in Industrial Engineering from Amir Kabir University of Technology in 1998 and 2000, respectively. He received his Ph.D. degree in Industrial Engineering from Iran University of Science and Technology in 2012. His research interest topics include quality control and reliability engineering, production planning, and operations research applications in health care.

Sadigh Raissi, School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Sadigh Raissi is a Professor working at the School of Industrial Engineering, Islamic Azad University, South Tehran Branch (IAU-STB). He received his industrial engineering Ph.D. degree from IAU- Science & Research Branch in 2002, Tehran, Iran. He has been engaged in many industrial engineering technology developments through consulting from 1988 up to the present. His main research fields are quality & reliability engineering, system simulation, and statistical methods in engineering.

References

Akturk, M.S., and Gurel, A.S., 2007. Machining conditions-based preventive maintenance. International Journal of Production Research, 45(8), pp.1725-1743. DOI: https://doi.org/10.1080/00207540600703587

Ali, A.K., 2024. A comprehensive framework for integrating robotics and digital twins in façade perforation. Aro-the Scientific Journal of Koya University, 12(1), pp.191-202. DOI: https://doi.org/10.14500/aro.11351

Banker, R.D., Charnes, A., and Cooper, W.W., 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), pp.1078-1092. DOI: https://doi.org/10.1287/mnsc.30.9.1078

Banker, R., Førsund, F.R., and Zhang, D., 2017. Use of data envelopment analysis for incentive regulation of electric distribution firms. Data Envelopment Analysis Journal, 3(1-2), pp.1-47. DOI: https://doi.org/10.1561/103.00000020

Banks, J., Ed. 1998. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice. John Wiley and Sons, United States. Batur, G.D., Karasan, O.E., and Akturk, M.S., 2012. Multiple part-type scheduling in flexible robotic cells. International Journal of Production Economics, 135(2), pp.726-740. DOI: https://doi.org/10.1016/j.ijpe.2011.10.006

Emrouznejad, A., and Yang, G.L., 2018. A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences, 61, pp.4-8. DOI: https://doi.org/10.1016/j.seps.2017.01.008

Farughi, H., Dolatabadiaa, M., Moradi, V., Karbasi, V., and Mostafayi, S., 2017. Minimizing the number of tool switches in flexible manufacturing cells subject to tools reliability using genetic algorithm. Journal of Industrial and Systems Engineering, 10 (special issue on Quality Control and Reliability), pp.17-33.

Florescu, A., Barabaş, S., and Sârbu, F., 2017. Operational parameters estimation for a flexible manufacturing system. A case study. MATEC Web of Conferences. 112, p.05008. DOI: https://doi.org/10.1051/matecconf/201711205008

Foumani, M., and Tavakkoli Moghaddam, R., 2019. A scalarization-based method for multiple part-type scheduling of two-machine robotic systems with non-destructive testing technologies. Iranian Journal of Operations Research, 10(1), pp.1-17. DOI: https://doi.org/10.29252/iors.10.1.1

Fu, M.C., 2002. Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing, 14(3), pp.192-215. DOI: https://doi.org/10.1287/ijoc.14.3.192.113

Gultekin, H., Akturk, M.S., and Karasan, O.E., 2007. Scheduling in a three-machine robotic flexible manufacturing cell. Computers and Operations Research, 34(8), pp.2463-2477. DOI: https://doi.org/10.1016/j.cor.2005.09.015

Gultekin, H., Akturk, M.S., and Karasan, O.E., 2008. Bicriteria robotic cell scheduling. Journal of Scheduling, 11, pp.457-473. DOI: https://doi.org/10.1007/s10951-007-0033-9

Jain, S., Triantis, K.P., and Liu, S., 2011. Manufacturing performance measurement and target setting: Adata envelopment analysis approach. European Journal of Operational Research, 214(3), pp.616-626. DOI: https://doi.org/10.1016/j.ejor.2011.05.028

Kamalabadi, I.N., Sadeghi, H., and Maihami, R., 2012. Optimization of total cost of production and time in three-machine robotic cell. International Journal of Industrial Engineering, 23(3), pp.293-302.

Karagiannis, P., Togias, T., Michalos, G., and Makris, S., 2021. Operators training using simulation and VR technology. Procedia CIRP, 96, pp.290-294. DOI: https://doi.org/10.1016/j.procir.2021.01.089

Khebouche, A., and Boudhar, M., 2024. Two-machine reentrant circular robotic cells with swap ability. International Journal of Computing Science and Mathematics, 19(3), pp.221-231. DOI: https://doi.org/10.1504/IJCSM.2024.137825

Kolny, D., Kaczmar-Kolny, E., and Dulina, Ľ., 2023. Modeling and simulation of the furniture manufacturing and assembly process in the arena simulation software. Technologia i Automatyzacja Montażu, 119(1), pp.13-22. DOI: https://doi.org/10.7862/tiam.2023.1.2

Leung, C.S., and Lau, H.Y., 2019. A multi-objective simulation-based optimization approach applied to material handling system. In: Innovative Computing Trends and Applications. Springer, Cham, pp.1-12. DOI: https://doi.org/10.1007/978-3-030-03898-4_1

Linnéusson, G., Ng, A.H., and Aslam, T., 2020. A hybrid simulation-based optimization framework supporting strategic maintenance development to improve production performance. European Journal of Operational Research, 281(2), pp.402-414. DOI: https://doi.org/10.1016/j.ejor.2019.08.036

Liu, J.S., Lu, L.Y., Lu, W.M., and Lin, B.J., 2013. Asurvey of DEA applications. Omega, 41(5), pp.893-902. DOI: https://doi.org/10.1016/j.omega.2012.11.004

Mohammed, A.J., Abdulghafour, A.B., and AL-Enzi, A.M.J., 2024. Modeling of automobile assembly line performance using ARENA simulation software. Salud, Ciencia y Tecnología-Serie de Conferencias, 3, pp.828-828. DOI: https://doi.org/10.56294/sctconf2024828

Moradi, V., Yousefi Nejad Attari, M., and Farughi, H., 2018. Modeling for minimizing cycle time in a three-machine robotic cell with‎ assumption of tool switching. Journal of Industrial Engineering Research in Production Systems, 6(12), pp.1-17.

Mourtzis, D., Doukas, M., and Bernidaki, D., 2014. Simulation in manufacturing: Review and challenges. Procedia CIRP, 25, pp.213-229. DOI: https://doi.org/10.1016/j.procir.2014.10.032

Mourtzis, D., Tsoubou, S., and Angelopoulos, J., 2023. Robotic cell reliability optimization based on digital twin and predictive maintenance. Electronics, 12(9), p.1999. DOI: https://doi.org/10.3390/electronics12091999

Negahban, A., and Smith, J.S., 2014. Simulation for manufacturing system design and operation: Literature review and analysis. Journal of Manufacturing Systems, 33(2), pp.241-261. DOI: https://doi.org/10.1016/j.jmsy.2013.12.007

Pidd, M., 1986. Computer Simulation in Management Science. Wiley, United States.

Pjevcevic, D., Nikolic, M., Vidic, N., and Vukadinovic, K., 2017. Data envelopment analysis of AGV fleet sizing at a port container terminal. International Journal of Production Research, 55(14), pp.4021-4034. DOI: https://doi.org/10.1080/00207543.2016.1241445

Sethi, S.P., Sriskandarajah, C., Sorger, G., Blazewicz, J., and Kubiak, W., 1992. Sequencing of parts and robot moves in a robotic cell. International Journal of Flexible Manufacturing Systems, 4, pp.331-358. DOI: https://doi.org/10.1007/BF01324886

Sinha, R., Vaisi, B., and Edalatpanah, S.A., 2024. Does managerial ability promote firm efficiency? Journal of Applied Research on Industrial Engineering, (Articles in Press). doi: 10.22105/jarie. 2024.448994.1600

Shannon, R.E., 1998. Introduction to the Art and Science of Simulation. In: 1998 Winter Simulation Conference. Proceedings (Cat. No. 98ch36274). Vol. 1. IEEE, Washington, DC, pp.7-14. DOI: https://doi.org/10.1109/WSC.1998.744892

Solgi, O., Gheidar-Kheljani, J., Saidi-Mehrabad, M., and Dehghani, E., 2019. Implementing an efficient data envelopment analysis method for assessing suppliers of complex product systems. Journal of Industrial and Systems Engineering, 12(2), pp.113-137.

Vaisi, B., and Raissi, S., 2014. Productivity improvement in the pride’s spare parts manufacturing using computer simulation and data envelopment analysis. International Journal of Computer Applications, 95(7), pp.12-18. DOI: https://doi.org/10.5120/16605-6431

Vaisi, B., Raissi, S., and Vaisi, A., 2015. A simulation based strategy using data envelope analysis-goal programming for increasing customer satisfaction in a chain store. International Journal of Innovative Science and Research Technology, 2, pp.513-520.

Vaisi, B., 2017. Productivity improvement in a manufacturing system using computer simulation: A comparison between DEA and DEAGP. Recent Applications of Data Envelopment Analysis, pp. 210-217.

Vaisi, B., Farughi, H., and Raissi, S., 2018. Two-machine robotic cell sequencing under different uncertainties. International Journal of Simulation Modelling, 17(2), pp.284-294. DOI: https://doi.org/10.2507/IJSIMM17(2)434

Vaisi, B., Farughi, H., and Raissi, S., 2020. Schedule-allocate and robust sequencing in three-machine robotic cell under breakdowns. Mathematical Problems in Engineering, 2020, p.24. DOI: https://doi.org/10.1155/2020/4597827

Vaisi, B., Farughi, H., and Raissi, S., 2021. Utilization of response surface methodology and goal programming based on simulation in a robotic cell to optimize sequencing. Journal of Quality Engineering and Management, 10(4), pp.327-338.

Vaisi, B., 2022. Areview of optimization models and applications in robotic manufacturing systems: Industry 4.0 and beyond. Decision Analytics Journal, 2, p.100031. DOI: https://doi.org/10.1016/j.dajour.2022.100031

Vaisi, B., Farughi, H., Raissi, S., and Sadeghi, H., 2023. A bi-objective optimal task scheduling model for two-machine robotic-cell subject to probable machine failures. Journal of Applied Research on Industrial Engineering, 10(1), pp.141-154.

Vaisi, B., 2023. Simulation-based optimization of a transport robot via super-efficiency DEAGP approach. In: Transport and Logistics Planning and Optimization. IGI Global, United States, pp.256-273. DOI: https://doi.org/10.4018/978-1-6684-8474-6.ch011

Wen, Q., Hong, J., Liu, G., Xu, P., Tang, M., and Li, Z., 2020. Regional efficiency disparities in China’s construction sector: Acombination of multiregional input-output and data envelopment analyses. Applied Energy, 257, p.113964. DOI: https://doi.org/10.1016/j.apenergy.2019.113964

Woerner, S., Laumanns, M., and Wagner, S.M., 2018. Simulation-based optimization of capacitated assembly systems under beta-service level constraints. Decision Sciences, 49(1), pp.180-217. DOI: https://doi.org/10.1111/deci.12260

Yang, B., Chen, W., and Lin, C., 2017. The algorithm and simulation of multi-objective sequence and balancing problem for mixed mode assembly line. International Journal of Simulation Modelling, 16(2), pp.357-367. DOI: https://doi.org/10.2507/IJSIMM16(2)CO10

Zhao, X.F., and Guo, X.P., 2018. An effective chemical reaction optimization for cyclic multi-type parts robotic cell scheduling problem with blocking. Journal of Intelligent and Fuzzy Systems, 35,pp.3567-3579. DOI: https://doi.org/10.3233/JIFS-18096

Zhu, N., Zhu, C., and Emrouznejad, A., 2020. A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese anufacturing listed companies. Journal of Management Science and Engineering, 6, pp.435-448. DOI: https://doi.org/10.1016/j.jmse.2020.10.001