Document Type : Research Paper
Authors
1 Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Department of Engineering, University of Kurdistan, Sanandaj, Iran.
3 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Abstract
In this study, we model a stochastic scheduling problem for a robotic cell with two unreliable machines susceptible to breakdowns and subject to the probability of machine failure and machine repair. A single gripper robot facilitates the loading/unloading of parts and cell-internal movement. Since it is more complicated than the other cycles, the focus has been on the S_2 cycle as the most frequently employed robot movement cycle. Therefore, a multi-objective mathematical formulation is proposed to minimize cycle time and operational costs. The -constraint method is used to solve small-sized problems. Non-dominated sorting genetic algorithm II (NSGA-II), is used to solve large-sized instances based on a set of randomly generated test problems. The results of several Test problems were compared with those of the GAMS software to evaluate the algorithm's performance. The computational results indicate that the proposed algorithm performs well. Compared to GAMS software, the average results for maximum spread (D) and non-dominated solutions (NDS) are 0.02 and 0.04, respectively.
Keywords
Main Subjects
- Farughi, H., Dolatabadiaa, M., Moradi, V., Karbasi, V., & 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(Spec. Issue), 17-33. http://www.jise.ir/article_33655_b336d3237fefd7e86dedeaad450bb15b.pdf
- Zanjani, B., Amiri, M., Hanafizadeh, P., & Salahi, M. (2021). Robust multiobjective hybrid flow shop scheduling. Journal of applied research on industrial engineering, 8(1), 40-55. https://dx.doi.org/10.22105/jarie.2021.252651.1202
- Rashidi, H., & Hassanpour, M. (2020). A deep-belief network approach for course scheduling. Journal of applied research on industrial engineering,7(3), 221-237. https://dx.doi.org/10.22105/jarie.2020.243184.1185
- Hamid, M., & Tavakkoli-Moghaddam, R., Vahedi-Nouri, B., & Arbabi, H. (2020). A mathematical model for integrated operating room and surgical member scheduling considering lunch break.International journal of research in industrial engineering, 9(4), 304-312.
- Khalili, N., & Shahnazari Shahrezaei, P., & Abri, A. G. (2020). A multiobjective optimization approach for a nurse scheduling problem considering the fatigue factor (case study: Labbafinejad hospital). Journal of applied research on industrial engineering, 7(4), 396-423. https://dx.doi.org/10.22105/jarie.2020.259483.1215
- Sethi, S. P., Sriskandarajah, C., Sorger, G., Blazewicz, J. & Kubiak, W. (1992). Sequencing of parts and robot moves in a robotic cell. International journal of flexible manufacturing systems, 4, 331-358. https://doi.org/10.1007/BF01324886
- Fathian, M., Kamalabadi, I. N., Heydari, M., & Farughi, H. (2011). A Petri net model for part sequencing and robot moves sequence in a 2-machine robotic cell. Journal of software engineering and applications, 4(11), 603. http://www.SciRP.org/journal/jsea
- Fathian, M., Nakhai Kamalabadi, I., Heydari, M., & Farughi, H. (2012). A petri net model for part sequencing and robot moves sequence in a 2-machine robotic cell. Journal of software engineering and applications, 4, 603-608. https://www.scirp.org/html/8574.html
- Fathian, M., Nakhai Kamalabadi, I., Heydari, M., Farughi, H., & Naseri, F. (2013). Applying metaheuristic algorithms for output rate analysis in two-machine robotic manufacturing cells. International journal of advanced robotic systems, 10(169), 1-11. https://doi.org/10.5772%2F56051
- Majumder, A., & Laha, D. (2016). A new cuckoo search algorithm for 2-machine robotic cell scheduling problem with sequence-dependent setup times. Swarm and evolutionary computation, 28, 131-143. https://doi.org/10.1016/j.swevo.2016.02.001
- Wang, Z., Zhou, B., Trentesaux, D., & Bekrar, A. (2017). Approximate optimal method for cyclic solutions in multi-robotic cell with processing time window. Robotics and autonomous systems, 98, 307-316. https://doi.org/10.1016/j.robot.2017.09.020
- Gultekin, H., Coban, B., & Akhlaghi, V. E. (2018). Cyclic scheduling of parts and robot moves in m-machine robotic cells. Computers & operations research, 90, 161-172. https://doi.org/10.1016/j.cor.2017.09.018
- Tonke, D., Grunow, M., & Akkerman, R. (2019). Robotic-cell scheduling with pick-up constraints and uncertain processing times. IISE transactions, 51(11), 1217-1235. https://doi.org/10.1080/24725854.2018.1555727
- Bożejko, W., Pempera, J., Smutnicki, C., & Wodecki, M. (2019). Cyclic scheduling in the manufacturing cell. In Modelling and performance analysis of cyclic systems(pp. 49-62). Cham: Springer International Publishing.
- Kim, H. J., & Lee, J. H. (2021). Scheduling of dual-gripper robotic cells with reinforcement learning. IEEE transactions on automation science and engineering, 19(2), 1120-1136. https://doi.org/10.1109/TASE.2020.3047924
- Hoogeveen, H. (2005). Multicriteria scheduling. European journal of operational research, 167(3), 592–623. https://doi.org/10.1016/j.ejor.2004.07.011
- Kayan, R. K. & Akturk, M. S. (2005). A new bounding mechanism for the cnc machine scheduling problems with controllable processing times. European journal of operational research, 167(3), 624–643. https://doi.org/10.1016/j.ejor.2004.07.012
- Gurel, S. & Akturk, M. S. (2007). Considering manufacturing cost and scheduling performance on a CNC turning machine. European journal of operational research, 177(1), 325-343. https://doi.org/10.1016/j.ejor.2005.11.029
- Turkcan, A., Akturk, M. S., & Storer, R. H. (2007). Due date and cost-based FMS loading, scheduling, and tool management. International journal of production research, 45(5), 1183–1213. https://doi.org/10.1080/00207540600559955
- Restrepo, I. M., & Balakrishnan, S. (2008). Fuzzy-based methodology for multiobjective scheduling in a robot-centered flexible manufacturing cell. Journal of intelligent manufacturing, 19, 421–432. https://doi.org/10.1007/s10845-008-0093-5
- Vaisi, B., Farughi, H., & Raissi, S. (2020). Multiobjective optimal model for task scheduling and allocation in a two machines robotic cell considering breakdowns. WSEAS transactions on information science and applications, 17, 1-8. https://www.wseas.com/journals/isa/2020/a025103-920.pdf
- Yildiz, S., Akturk, M. S., & Karasan, O. E. (2011). Bicriteria robotic cell scheduling with controllable processing times. International journal of production research, 49(2), 569-583. https://doi.org/10.1080/00207540903491799
- Feng, J., Che, A., & Wang, N. (2014). Bi-objective cyclic scheduling in a robotic cell with processing time windows and non-Euclidean travel times. International journal of production research, 52(9), 2505-2518. https://doi.org/10.1080/00207543.2013.849015
- Ma, K., Yan, P., & Dai, W. (2016). A hybrid discrete differential evolution algorithm for dynamic scheduling in robotic cells. 2016 13th international conference on service systems and service management (ICSSSM)(pp. 1-6). IEEE.
- Abd, K., Abhary, K., & Marian, R. (2016). Multiobjective optimization of dynamic scheduling in robotic flexible assembly cells via fuzzy-based Taguchi approach. Computers & industrial engineering, 99, 250-259. https://doi.org/10.1016/j.cie.2016.07.028
- Mansouri, S. A., Aktas, E., & Besikci, U. (2016). Green scheduling of a two-machine flow shop: trade-off between makespan and energy consumption. European journal of operational research, 248(3), 772-788. https://doi.org/10.1016/j.ejor.2015.08.064
- Ghadiri Nejad, M., Shavarani, S. M., Vizvári, B., & Barenji, R. V. (2018). Trade-off between process scheduling and production cost in cyclic flexible robotic cells. The international journal of advanced manufacturing technology, 96, 1081-1091. https://doi.org/10.1007/s00170-018-1577-x
- Vaisi, B., Farughi, H., & Raissi, S. (2018). Bi-criteria robotic cell scheduling and operation allocation in the presence of break-downs. International journal of industrial engineering & production research, 29(3), 343-357. DOI: 22068/ijiepr.29.3.343
- Wu, X., Yuan, Q., & Wang, L. (2020). Multiobjective differential evolution algorithm for solving robotic cell scheduling problem with batch-processing machines. IEEE transactions on automation science and engineering, 18(2), 757-775. DOI: 1109/TASE.2020.2969469
- Vaisi, B., Farughi, H., & Raissi, S. (2020). Schedule-allocate and robust sequencing in three-machine robotic cell under breakdowns. Mathematical problems in engineering, 2020, 1-24. https://doi.org/10.1155/2020/4597827
- Sadeghi, H., Makui, A., & Heydari, M. (2016). Multilevel production systems with dependent demand with uncertainty of lead times. Mathematical problems in engineering, 2016. https://doi.org/10.1155/2016/4967341
- Sadeghi, H. (2019). A forecasting system by considering product reliability, POQ policy, and periodic demand. Journal of quality engineering and production optimization, 4(2), 133-148. DOI: 22070/JQEPO.2020.5087.1123
- Shafiei-Monfared, S., Salehi-Gilani, K., & Jenab, K. (2009). Productivity analysis in a robotic cell. International journal of production research, 47(23), 6651-6662. https://doi.org/10.1080/00207540802372298
- Geismar, H. N., & Pinedo, M. (2010). Robotic cells with stochastic processing times. IIE transactions, 42(12), 897-914. https://doi.org/10.1080/0740817X.2010.491505
- Al-Salem, M., & Kharbeche, M. (2017). Throughput optimization for the robotic cell problem with controllable processing times. RAIRO-operations research, 51(3), 805-818. https://doi.org/10.1051/ro/2016064
- Savsar, M., & Aldaihani, M. (2008). Modeling of machine failures in a flexible manufacturing cell with two machines served by a robot. Reliability engineering & system safety, 93(10), 1551-1562. https://doi.org/10.1016/j.ress.2007.06.002
- Savsar, M. (2010). Reliability modeling of a manufacturing cell operated under degraded mode. Proceedings of the international conference on industrial engineering and operations management (IEOM 2010) (PP. 374-385). IEOM. http://www.iieom.org/paper/211%20Savsar.pdf
- Hamasha, M. M., Alazzam, A., Hamasha, S., Aqlan, F., Almeanazel, O., & Khasawneh, M. T. (2015). Multimachine flexible manufacturing cell analysis using a Markov chain-based approach. IEEE transactions on components, packaging and manufacturing technology, 5(3), 439-446. https://www.researchgate.net
- Vaisi, B., Farughi, H., & 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), 327-338. https://dorl.net/dor/20.1001.1.23221305.1399.10.4.5.7
- Vaisi, B., Farughi, H., & Raissi, S. (2018). Two-machine robotic cell sequencing under different uncertainties. International journal of simulation modelling, 17(2), 284-294. https://doi.org/10.2507/IJSIMM17(2)434
- Zahrouni, W., & Kamoun, H. (2021). Scheduling in robotic cells with time window constraints. European journal of industrial engineering, 15(2), 206-225. https://doi.org/10.1504/EJIE.2021.114001
- Lee, J. H., & Kim, H. J. (2022). Reinforcement learning for robotic flow shop scheduling with processing time variations. International journal of production research, 60(7), 2346-2368. https://doi.org/10.1080/00207543.2021.1887533
- Selim, M., & Gurel, A. S. (2007). Machining conditions-based preventive maintenance. International journal of production research, 45(8), 1725-1743. http://dx.doi.org/10.1080/00207540600703587
- Vaisi, B. (2022). A review of optimization models and applications in robotic manufacturing systems: industry 4.0 and beyond. Decision analytics journal, 2, 100031. https://doi.org/10.1016/j.dajour.2022.100031
- Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. International conference on parallel problem solving from nature(pp. 849-858). Springer Berlin Heidelberg. https://doi.org/10.1007/3-540-45356-3_83
- Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems(Vol. 5, pp. 79-104). New York: Springer. https://doi.org/10.1007/978-0-387-36797-2
- Kiyani Ghalehno, R., Niroomand, S., Didekhani, H., & Mahmoodirad, A. (2022). A multi-objective formulation for portfolio optimization of credit institutions branches: case study of Keshavarzi bank of Sistan and Baloochestan. Journal of decisions and operations research, 7(2), 299-315. (In Persian). DOI: 22105/dmor.2021.257591.1260
- Sadeghi, H., & Mahmoodi, A. (2022). Multi-objective inventory model for material requirements planning with uncertain lead-time. International journal of operational research, 43(4), 391-415. https://doi.org/10.1504/IJOR.2022.122811
- Shoaee, M., & Samouei, P. (2021). A cross-dock warehouse layout design using multi-objective gray wolf optimization algorithm. Journal of decisions and operations research, 7(3). 453-465. (In Persian). DOI: 22105/dmor.2021.267139.1302
)