Integrated Dynamic Cellular Manufacturing Systems and Hierarchical Production Planning with Worker Assignment and Stochastic Demand

Document Type : Original Article


Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran


This study deals with the interaction of dynamic cellular manufacturing (DCM) and hierarchical production planning (HPP) problems with stochastic demands for the first time. Each of these alone does not consider the system factors such as stochastic demands and dynamic cellular formation separately. Accordingly, to fill this gap, this paper presents an integrated optimized model incorporating the most comprehensive design of DCM systems and HPP problems with stochastic demands. This model helps administrators get the optimal size and number of cells to decrease costs. Also, the model applies the principles of HPP to reduce the complexity of the integrated model. Since demands are uncertain, they need to be accurately predicted. Therefore, this study aims to combine the most precise decision variables with the most realistic conditions. A case study from an agriculture mechanization and industrial development company shows that an integrated model can provide managers with a feasible solution to meet demand, reconfigure cells in each period, provide new machinery to increase the required production capacity, and adjust production capacity to help them cope with demand fluctuations. A sensitivity analysis was performed and the results show that increase in forecast error and inter-cell move cost cause less significant changes in total cost but the total cost is sensitive to intra-cell move cost, available time capacities and cell quantity. It is also shown that the total cost was very sensitive to available regular time and available over time and the system should try to increase the time capacity.


Main Subjects

  1. Arkat, J., Rahimi, V. and Farughi, H., "Reactive scheduling addressing unexpected disturbance in cellular manufacturing systems", International Journal of Engineering, Transactions A: Basics, Vol. 34, No. 1, (2021), 162-170. doi: 10.5829/IJE.2021.34.01A.18
  2. Bagheri, F., Safaei, A.S., Kermanshahi, M. and Paydar, M.M., "Robust design of dynamic cell formation problem considering the workers interest", International Journal of Engineering, Transactions C: Aspects, Vol. 32, No. 12, (2019), 1790-1797. doi: 10.5829/IJE.2019.32.12C.12
  3. Tajdin, A., Mahdavi, I. and Hashemoghli, A., "A novel interactive possibilistic mixed integer nonlinear model for cellular manufacturing problem under uncertainty", International Journal of Engineering, Transactions C: Aspects, Vol. 30, No. 3, (2017), 384-392. doi: 10.5829/idosi.ije.2017.30.03c.08
  4. Mirgorbani, S., Jolai, F., Javadi, B. and Tavakkoli-Moghaddam, R., "An efficient algorithm to inter and intra-cell layout problems in cellular manufacturing systems with stochastic demands", International Journal of Engineering, Vol. 19, No. 1, (2006), 67-78.
  5. Cakir, M., Guvenc, M.A. and Mistikoglu, S., "The experimental application of popular machine learning algorithms on predictive maintenance and the design of iiot based condition monitoring system", Computers & Industrial Engineering, Vol. 151, (2021), 106948. doi: 10.1016/j.cie.2020.106948
  6. Sharifi, S., Chauhan, S. and Bhuiyan, N., "Part-level sequence dependent setup time reduction in cms", Journal of Industrial and Systems Engineering, Vol. 5, No. 3, (2011), 142-153. doi: 20.1001.1.17358272.2011.
  7. Alimian, M., Ghezavati, V. and Tavakkoli-Moghaddam, R., "New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems", Journal of Manufacturing Systems, Vol. 56, (2020), 341-358. doi: 10.1016/j.jmsy.2020.06.011
  8. Sadjadi, S. and Makui, A., "An algorithm to compute the complexity of a static production planning (research note)", International Journal of Engineering, Vol. 16, No. 1, (2003), 57-60.
  9. Aghajani-Delavar, N., Tavakkoli-Moghaddam, R. and Mehdizadeh, E., "Design of a new mathematical model for integrated dynamic cellular manufacturing systems and production planning", International Journal of Engineering, Transactions B: Applications, Vol. 28, No. 5, (2015), 746-754. doi: 10.5829/idosi.ije.2015.28.05b.13
  10. Sakhaii, M., Tavakkoli-Moghaddam, R. and Vatani, B., "A robust model for a dynamic cellular manufacturing system with production planning", International Journal of Engineering, Transactions A: BAsics, Vol. 27, No. 4, (2014), 587-598. doi: 10.5829/idosi.ije.2014.27.04a.09
  11. Balakrishnan, J. and Cheng, C.H., "Dynamic cellular manufacturing under multiperiod planning horizons", Journal of Manufacturing Technology Management, (2005). doi: 10.1108/17410380510600491
  12. Song, S.-J. and Choi, J.-H., "Integrated autonomous cellular manufacturing-a new concept for the 21st century", International Journal of Manufacturing Technology and Management, Vol. 3, No. 3, (2001), 293-307. doi: 10.1504/IJMTM.2001.001412
  13. Ebrahimi, H., Kianfar, K. and Bijari, M., "Scheduling a cellular manufacturing system based on price elasticity of demand and time-dependent energy prices", Computers & Industrial Engineering, Vol. 159, (2021), 107460. doi: 10.1016/j.cie.2021.107460
  14. Sharma, V., Kumar, S. and Meena, M., "Key criteria influencing cellular manufacturing system: A fuzzy ahp model", Journal of Business Economics, Vol. 92, No. 1, (2022), 65-84. doi: 10.1007/s11573-021-01043-y
  15. Kumar, S., Gupta, M., Suhaib, M. and Asjad, M., "Current status, enablers and barriers of implementing cellular manufacturing system in sports industry through ism", International Journal of System Assurance Engineering and Management, Vol. 12, No. 3, (2021), 345-360. doi: 10.1007/s13198-021-01052-8
  16. Saraçoğlu, İ., Süer, G.A. and Gannon, P., "Minimizing makespan and flowtime in a parallel multi-stage cellular manufacturing company", Robotics and Computer-Integrated Manufacturing, Vol. 72, (2021), 102182. doi: 10.1016/j.rcim.2021.102182
  17. Guo, H., Chen, M., Mohamed, K., Qu, T., Wang, S. and Li, J., "A digital twin-based flexible cellular manufacturing for optimization of air conditioner line", Journal of Manufacturing Systems, Vol. 58, (2021), 65-78. doi: 10.1016/j.jmsy.2020.07.012
  18. Akturk, M.S. and Turkcan, A., "Cellular manufacturing system design using a holonistic approach", International Journal of Production Research, Vol. 38, No. 10, (2000), 2327-2347. doi: 10.1080/00207540050028124
  19. Mahdavi, I., Javadi, B., Fallah-Alipour, K. and Slomp, J., "Designing a new mathematical model for cellular manufacturing system based on cell utilization", Applied mathematics and Computation, Vol. 190, No. 1, (2007), 662-670. doi: 10.1016/j.amc.2007.01.060
  20. Defersha, F.M. and Chen, M., "A comprehensive mathematical model for the design of cellular manufacturing systems", International Journal of Production Economics, Vol. 103, No. 2, (2006), 767-783. doi: 10.1016/j.ijpe.2005.10.008
  21. Feng, H., Da, W., Xi, L., Pan, E. and Xia, T., "Solving the integrated cell formation and worker assignment problem using particle swarm optimization and linear programming", Computers & Industrial Engineering, Vol. 110, (2017), 126-137. doi: 10.1016/j.cie.2017.05.038
  22. Saxena, L.K. and Jain, P., "An integrated model of dynamic cellular manufacturing and supply chain system design", The International Journal of Advanced Manufacturing Technology, Vol. 62, No. 1, (2012), 385-404. doi: 10.1007/s00170-011-3806-4
  23. Chen, M. and Cao, D., "Coordinating production planning in cellular manufacturing environment using tabu search", Computers & Industrial Engineering, Vol. 46, No. 3, (2004), 571-588. doi: 10.1016/j.cie.2004.02.002
  24. Defersha, F.M. and Chen, M., "A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality", European Journal of Operational Research, Vol. 187, No. 1, (2008), 46-69. doi: 10.1016/j.ejor.2007.02.040
  25. Safaei, N. and Tavakkoli-Moghaddam, R., "Integrated multi-period cell formation and subcontracting production planning in dynamic cellular manufacturing systems", International Journal of Production Economics, Vol. 120, No. 2, (2009), 301-314. doi: 10.1016/j.ijpe.2008.12.013
  26. Safaei, N., Saidi-Mehrabad, M. and Jabal-Ameli, M., "A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system", European Journal of Operational Research, Vol. 185, No. 2, (2008), 563-592. doi: 10.1016/j.ejor.2006.12.058
  27. Xue, G. and Offodile, O.F., "Integrated optimization of dynamic cell formation and hierarchical production planning problems", Computers & Industrial Engineering, Vol. 139, (2020), 106155. doi: 10.1016/j.cie.2019.106155
  28. Mahdavi, I., Aalaei, A., Paydar, M.M. and Solimanpur, M., "Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems", International Journal of Production Research, Vol. 49, No. 21, (2011), 6517-6537. doi: 10.1080/00207543.2010.524902
  29. Koopman, S.J. and Lit, R., "Forecasting football match results in national league competitions using score-driven time series models", International Journal of Forecasting, Vol. 35, No. 2, (2019), 797-809. doi: 10.1016/j.ijforecast.2018.10.011
  30. Zhao, H. and Zhang, C., "An online-learning-based evolutionary many-objective algorithm", Information Sciences, Vol. 509, (2020), 1-21. doi: 10.1016/j.ins.2019.08.069
  31. Dulebenets, M.A., "An adaptive polyploid memetic algorithm for scheduling trucks at a cross-docking terminal", Information Sciences, Vol. 565, (2021), 390-421. doi: 10.1016/j.ins.2021.02.039
  32. Pasha, J., Nwodu, A.L., Fathollahi-Fard, A.M., Tian, G., Li, Z., Wang, H. and Dulebenets, M.A., "Exact and metaheuristic algorithms for the vehicle routing problem with a factory-in-a-box in multi-objective settings", Advanced Engineering Informatics, Vol. 52, (2022), 101623. doi: 10.1016/j.aei.2022.101623
  33. Kavoosi, M., Dulebenets, M.A., Abioye, O.F., Pasha, J., Wang, H. and Chi, H., "An augmented self-adaptive parameter control in evolutionary computation: A case study for the berth scheduling problem", Advanced Engineering Informatics, Vol. 42, (2019), 100972. doi: 10.1016/j.aei.2019.100972
  34. Rabbani, M., Oladzad-Abbasabady, N. and Akbarian-Saravi, N., "Ambulance routing in disaster response considering variable patient condition: Nsga-ii and mopso algorithms", Journal of Industrial & Management Optimization, Vol. 18, No. 2, (2022), 1035. doi: 10.3934/jimo.2021007
  35. Zhang, X., Yuan, J., Chen, X., Zhang, X., Zhan, C., Fathollahi-Fard, A.M., Wang, C., Liu, Z. and Wu, J., "Development of an improved water cycle algorithm for solving an energy-efficient disassembly-line balancing problem", Processes, Vol. 10, No. 10, (2022), 1908. doi: 10.3390/pr10101908
  36. Yuan, G., Yang, Y., Tian, G. and Fathollahi-Fard, A.M., "Capacitated multi-objective disassembly scheduling with fuzzy processing time via a fruit fly optimization algorithm", Environmental Science and Pollution Research, (2022), 1-18. doi: 10.1007/s11356-022-18883-y
  37. Golmohammadi, A.-M., Honarvar, M., Hosseini-Nasab, H. and Tavakkoli-Moghaddam, R., "A bi-objective optimization model for a dynamic cell formation integrated with machine and cell layouts in a fuzzy environment", Fuzzy Information and Engineering, Vol. 12, No. 2, (2020), 204-222. doi: 10.1080/16168658.2020.1747162
  38. Yazdani, M., Kabirifar, K., Fathollahi-Fard, A.M. and Mojtahedi, M., "Production scheduling of off-site prefabricated construction components considering sequence dependent due dates", Environmental Science and Pollution Research, (2021), 1-17. doi: 10.1007/s11356-021-16285-0