A Simulation-Optimization Model for Dynamic Cellular Manufacturing System in Uncertainty Condition
Mehdi Anvari
1
(
Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch, Qazvin, Iran
)
Alireza Alinezhad
2
(
Department of Industrial Engineerin, Qa. c., Isalamic Azad university, Qazvin, Iran
)
Davoud gharakhani
3
(
Faculty of Management and Accounting, Islamic Azad University, Qazvin Branch, Qazvin, Iran.
)
کلید واژه: Metaheuristic Algorithm, Manufacturing System, Simulation, Robust Optimization, Cellular Manufacturi,
چکیده مقاله :
Dynamic Cellular Manufacturing System (DCMS) is a new manufacturing system and in recent years this system has been attented by most of the researchers. While many available models focus only on one of the simulation or optimization aspects, in this research, we designed a comprehensive simulation-optimization model; which Discrete-Event Simulation (DES) has been used for modeling production processes, and robust optimization is used for handling uncertainties. Take into consideration that the studied problem is an NP-hard, hence Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA) are used in this step to evaluate the mathematical model. As a result, the combination models can dramatically improve the quality of optimal solutions and increase the efficiency of manufacturing systems. These approaches can be further steps to develop simulation-optimization models in DCMSs and pave the way for more extensive studies in this field.
چکیده انگلیسی :
Dynamic Cellular Manufacturing System (DCMS) is a new manufacturing system and in recent years this system has been attented by most of the researchers. While many available models focus only on one of the simulation or optimization aspects, in this research, we designed a comprehensive simulation-optimization model; which Discrete-Event Simulation (DES) has been used for modeling production processes, and robust optimization is used for handling uncertainties. Take into consideration that the studied problem is an NP-hard, hence Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA) are used in this step to evaluate the mathematical model. As a result, the combination models can dramatically improve the quality of optimal solutions and increase the efficiency of manufacturing systems. These approaches can be further steps to develop simulation-optimization models in DCMSs and pave the way for more extensive studies in this field.
Alinezhad, A., Khalili, J. (2019). MABAC method. New Methods and Applications in Multiple Attribute Decision Making (MADM), 193-198.
Alinezhad, A., Khalili, J. (2019). DEMATEL method. New Methods and Applications in Multiple Attribute Decision Making (MADM), 103-108.
AL.Nima,A.T.(2022).Dynamiccellular manufacturing system design using genetic algorithm: a proposed model A case study in the Light Industries Company-Ashtar in Baghdad. Tikrit Journal of Administration and Economics Sciences, 18(58 part 2).
Bagheri, F., Safaei, A. S., Kermanshahi, M., & Paydar, M. M. (2019). Robust design of dynamic cell formation problem considering the workers interest. International Journal of Engineering, 32(12), 1790-1797.
Bakhshi-Khaniki, H., & Fatemi Ghomi, S. M. T. (2023). Integrated Dynamic Cellular Manufacturing Systems and Hierarchical Production Planning with Worker Assignment and Stochastic Demand. International Journal of Engineering, 36(2), 348-359.
Bandarzadeh.Shahab.(2024).the complex challenges of a Dynamic Cellular Manufacturing System (DCMS) by simultaneously tackling issues of unreliable machines, production planning, and intercellular layout.Journal of industrial management, Volume. 15, Issue. 3.page. 508 - 533
Bortolini, M., Ferrari, E., Galizia, F. G., & Regattieri, A. (2021). An optimisation model for the dynamic management of cellular reconfigurable manufacturing systems under auxiliary module availability constraints. Journal of manufacturing systems, 58, 442-451.
Burhenne, S. (2013). Monte Carlo based uncertainty and sensitivity analysis for building performance simulation.
Kazemi,A., Alinezhad,A., Shakourloo,A.(2017). A Fuzzy Goal Programming Model for Efficient Portfolio Selection. Journal of optimization in Industrial Engineering (JOIE), Vol. 10, No. 22, 61-71.
Lihong, Q., & Shengping, L. (2012). An improved genetic algorithm for integrated process planning and scheduling. The International Journal of Advanced Manufacturing Technology, 58, 727-740.
Malakooti, B. (2014). Operations and production systems with multiple objectives. John Wiley & Sons.
Najat Almasarwah, Esraa Abdelall, Mohammed Khurrum S. Bhutta & Mohammad Saraireh.(2025). Designing a cellular manufacturing system as a step toward creating sustainable cells under uncertain demand condition. Volume 15, pages 151–177, (2025)
Niakan, F., Baboli, A., Moyaux, T., & Botta-Genoulaz, V. (2016). A new multi-objective mathematical model for dynamic cell formation under demand and cost uncertainty considering social criteria. Applied Mathematical Modelling, 40(4), 2674-2691.
Ohno, K., Yamamoto, R., & Tanaka, H. (2016). Dynamic task scheduling scheme for a GPGPU programming framework. International Journal of Networking and Computing, 6(2), 290-308.
Rabbani, M., Farrokhi-Asl, H., & Ravanbakhsh, M. (2019). Dynamic cellular manufacturing system considering machine failure and workload balance. Journal of Industrial Engineering International, 15(1), 25-40.
Ramezanian, R., & Khalesi, S. (2019). Integration of multi-product supply chain network design and assembly line balancing. Operational Research, 1-31.
Rafiee. A New Robust Optimization Approach to Solve a Dynamic Cellular Manufacturing Functional Machines. Journal of Industrial Engineering Research in Production Systems.2016.
Sarrafha, K., Alinezhad, A., Amini,A. (2014). Sensitivity Analysis of SAW Technique: The Impact of Changing the Decision Making Matrix Elements on the Final Ranking of Alternatives. Iranian Journal of operations research (IJOR), Vol. 5, No. 1, 82-94.
Sarrafha,k., Kazemi,A., Alinezhad,A. (2014). A multi-objective evolutionary approach for integrated production-distribution planning problem in a supply chain network. Journal of Optimization in Industrial Engineering, Vol. 7, No. 14, 89-102.
Saxena, L. K., & Jain, P. K. (2011). Dynamic cellular manufacturing systems design—a comprehensive model. The International Journal of Advanced Manufacturing Technology, 53, 11-34.
Shafiee-Gol, S., Kia, R., Kazemi, M., Tavakkoli-Moghaddam, R., & Mostafayi Darmian, S. (2021). A mathematical model to design dynamic cellular manufacturing systems in multiple plants with production planning and location–allocation decisions. Soft Computing, 25, 3931-3954.
Shahraki&Farast A Multi-response simulation optimization-based model for operator allocation and job dispatching rule in a cellular. Journal of production and Operations Management, Vol. 10, Issue 1, No. 18, Spring & Summer 2019
Siedlak, D. J., Pinon, O. J., Robertson, B. E., & Mavris, D. N. (2018). Robust simulation-based scheduling methodology to reduce the impact of manual installation tasks on low-volume aerospace production flows. Journal of manufacturing systems, 46, 193-207.
Sadegheih,A.,Dehnavi,S.(2019).Developing the dynamic cell formation and production planning considering the inter/intra-cell material handling equipment. Production and Operations Management, Vol. 10, Issue 2, No. 1.
Taherinezhad,A., Alinezhad,A., Gholami,S., Abdolvand,M.(2024). Organizational Competitive Advantage Analysis using SWOT Matrix and Hybrid Approach of Fuzzy-DEMATEL, ANP, and DEA. Journal of Optimization in Industrial Engineering, Vol. 17, No. 1, 17-34.
Wang, L., Zhang, Z., & Yin, Y. (2022). Order acceptance and scheduling problem with outsourcing in seru production system considering lot-spitting. European Journal of Industrial Engineering, 16(1), 91-116.
Yazhey, M., Zhang, L., & Zhang, Z. (2015). Optimal scheduling of logistical support for medical resources order and shipment in community health service centers. Journal of Industrial Engineering and Management (JIEM), 8(5), 1362-1379.
Yin, Y., Sa, J., & Zhou, J. (2021). Modeling and Simulation on Error Spreading Over Partially and Directionally Interconnected Networks of Virtual Cellular Manufacturing System. IEEE Access, 9, 103385-103392.
YounesSinaki, R., Sadeghi, A., Mosadegh, H., Almasarwah, N., & Suer, G. (2023). Cellular manufacturing design 1996–2021: A review and introduction to applications of Industry 4.0. International Journal of Production Research, 61(16), 5585-5636.
Zhao, Z., & Zhou, H. (2022). A Hybrid Optimization Method for Manufacturing Cell Scheduling with Random Interruptions Based on Improved Wolf Pack Algorithm and Simulation. In Journal of Physics: Conference Series (Vol. 2173, No. 1, p. 012075). IOP Publishi.
Zhao, Z., Zhou, H., & Lei, Y. (2022). A Simulation Optimization Model for Cell Scheduling Considering Energy Consumption and Machine Breakdown. In Proceedings of 2021 Chinese Intelligent Systems Conference (pp. 126-133). Springer, Singapore.
