Optimization of a Dynamic Cellular Manufacturing System Considering Machine Breakdowns along with Sequence Monitoring of Operation Periods

Document Type : Research Paper

Authors

1 MSc. Student, Department of Industrial Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran

2 , Associate Prof., Department of Industrial Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran.

3 Assistant Prof., Department of Industrial Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran

10.22059/imj.2023.356073.1008035

Abstract

Objective
Short product life cycles, unpredictable demand patterns, and ever-decreasing time-to-market have put manufacturing companies under pressure. To face all complex production scenarios, these companies tend to the implementation of Cellular Manufacturing Systems (CMS) to reduce production costs, increase flexibility, and respond quickly to market demands. The cellular manufacturing system facilitates the control and management of the production system by dividing a large system into several small subsystems. The design of a cellular manufacturing system includes cell formation, group layout, group scheduling, and resource allocation. The first issue in designing a cellular system is the grouping of machines to produce a family of parts in production cells referred to as cell formation. The creation of efficient cells aims to achieve maximum performance of intracellular machines (intra-cellular processes) and minimize the transfer of parts from one cell to another (inter-cellular processes). The second problem is the problem of group layout, which includes the placement of cells in the workshop relative to each other (intercellular layout) and the layout of machines within the cells (intracellular layout). The optimal grouping of machines in cells, the efficient placement of cells relative to each other, as well as the machines inside cells affect the cost of intracellular movements and intercellular movements of parts. Considering that in a static cellular manufacturing system, the demand is considered constant in all periods, however, due to the advancement of technology, the environment of an industry is faced with turbulence in the types of products and demand. To overcome these problems, a dynamic cellular manufacturing system was introduced, which forms optimal cells in each period according to the demand conditions and changes in product composition. This study addresses the simultaneous challenges of a dynamic cellular manufacturing system (DCMS) with unreliable machines and production planning and intercellular layout problems. The proposed model seeks to minimize the costs of Inter and intra-cellula movement, reconfiguration, machine breakdown, part production, keeping parts in the warehouse, and back-ordering parts in production cycles.
 
Methods
At first, a mixed integer nonlinear programming mathematical model for the considered problem was presented, Next, linearized and validated with a case study in GAMS software with a GUROBI solver. In the following, the impact of moving machines between periods and the sensitivity analysis of the MTBF parameter were discussed.
 
Results
Flexibility in routing, optimal location of cells, and optimal grouping of machines reduced production costs, and also by moving two machines m1 and m3, production costs improved by 353,870 Tomans.
 
Conclusion
The reconfiguration of machine cells in the new period improves the cost of production and also the model is flexible in routing part production. In this study, MTBF sensitivity analysis showed that the number of failures affects the system's performance.

Keywords

Main Subjects


References
Ajmal Sheriff, M., Jayakumar, V., Ahmed, T., & Kumar, P. S. (2020). A comparative study on design of static and dynamic cellular manufacturing system under deterministic production environment. Materials Today: Proceedings, 24, 1468-1477.
Al-Zuheri, A., Ketan, H. S., & Vlachos, I. (2022). Grouping technology and a hybrid genetic algorithm-desirability function approach for optimum design of cellular manufacturing systems. IET Collaborative Intelligent Manufacturing, 4(4), 267-285.
Bayram, H. & Şahin, R. (2016). A comprehensive mathematical model for dynamic cellular manufacturing system design and Linear Programming embedded hybrid solution techniques. Computers & Industrial Engineering, 91, 10-29.
Danilovic, M., & Ilic, O. (2019). A novel hybrid algorithm for manufacturing cell formation problem. Expert Systems with Applications, 135, 327-350.
Deep, K., & Singh, P. K. (2015). Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm. Journal of Manufacturing Systems, 35, 155-163.
Dehnavi-Arani, S., Sadegheih, A., Zare Mehrjerdi, Y., & Honarvar, M. (2020). A new bi-objective integrated dynamic cell formation and AGVs’ dwell point location problem on the inter-cell unidirectional single loop. Soft Computing, 24, 16021-16042.
Doroudyan, M. & Khoshghalb, A. (2021). Robust design for facility layout problem in cellular manufacturing systems with uncertain demand. Journal of Industrial and Systems Engineering, 13 (Special issue: 17th International Industrial Engineering Conference),1-11.
Farughi, H., Mostafayi, S. & Afrasiabi, A. (2019). Bi-objective robust optimization model for configuring cellular manufacturing system with variable machine reliability and parts demand: A real case study. Journal of Industrial Engineering and Management Studies, 6(2), 120-146.
Feng, H., Xi, L., Xia, T., & Pan, E. (2018). Concurrent cell formation and layout design based on hybrid approaches. Applied Soft Computing, 66, 346-359.
Forghani, K., & Ghomi, S. F. (2020). Joint cell formation, cell scheduling, and group layout problem in virtual and classical cellular manufacturing systems. Applied Soft Computing, 97, 106719.
Forghani, K., Fatemi Ghomi, S. M. T., & Kia, R. (2020). Solving an integrated cell formation and group layout problem using a simulated annealing enhanced by linear programming. Soft Computing, 24(15), 11621-11639.
Ghezavati, V. R. (2015). Designing integrated cellular manufacturing systems with tactical decisions. Journal of the Chinese Institute of Engineers, 38(3), 332-341.
Golmohammadi, A. M., Honarvar, M., Tavakkoli_Moghaddam, R., & Hosseini-Nasab, H. (2021). A Novel Cell Layout Problem with Reliability and Stochastic Failures. International Journal of Supply and Operations Management, 8(2), 165-175.
Golmohammadi, A., Asadi, A., Amiri, Z., & Behzad, M. (2018). Design of a facility layout problem in cellular manufacturing systems with stochastic demands. Management Science Letters, 8(11), 1133-1148.
Hooshyar Telegraphi, A., & Bulgak, A. A. (2021). A mathematical model for designing a reliable cellular hybrid manufacturing-remanufacturing system considering alternative and contingency process routings. SN Applied Sciences, 3, 1-22.
Karoum, B., & Elbenani, Y. B. (2017). A clonal selection algorithm for the generalized cell formation problem considering machine reliability and alternative routings. Production Engineering, 11, 545-556.
Karoum, B., & Elbenani, Y. B. (2019). Optimization of the material handling costs and the machine reliability in cellular manufacturing system using cuckoo search algorithm. Neural Computing and Applications, 31, 3743-3757.
Khamlichi, H., Oufaska, K., Zouadi, T., & Dkiouak, R. (2020). A Hybrid GRASP algorithm for an integrated production planning and a group layout design in a dynamic cellular manufacturing system. IEEE Access, 8, 162809-162818.
Kheirkhah, A., & Ghajari, A. (2018). A three-phase heuristic approach to solve an integrated cell formation and production planning problem. Uncertain Supply Chain Management, 6(2), 213-228.
Kumar, R., & Singh, S. P. (2017). A similarity score-based two-phase heuristic approach to solve the dynamic cellular facility layout for manufacturing systems. Engineering Optimization, 49(11), 1848-1867.
Maroof, A., Tariq, A., & Noor, S. (2021). An integrated approach for the operational design of a cellular manufacturing system. Mehran University Research Journal Of Engineering & Technology, 40(2), 265-278.
Mehdizadeh, E., Shamoradifar, M., & Niaki, S. T. A. (2020). An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources. International Journal of Services and Operations Management, 37(1), 1-26.
Mukattash, A. M., Tahboub, K. K. & Adil, M. B. (2018). Interactive design of cellular manufacturing systems, optimality and flexibility. International Journal on Interactive Design and Manufacturing (IJIDeM), 12, 769-776.
Nasiri, M. M., & Naseri, F. (2019). Metaheuristic algorithms for the generalised cell formation problem considering machine reliability. International Journal of Process Management and Benchmarking, 9(4), 469-484.
Perera, G., & Ratnayake, V. (2019). Mathematical model for dynamic cell formation in fast fashion apparel manufacturing stage. Journal of Industrial Engineering International, 15, 1-16.
Rabbani, M., Farrokhi-Asl, H. & Ravanbakhsh, M. (2019). Dynamic cellular manufacturing system considering machine failure and workload balance. Journal of Industrial Engineering International, 15, 25–40.
Rahimi, V., Arkat, J., & Farughi, H. (2020). A vibration damping optimization algorithm for the integrated problem of cell formation, cellular scheduling, and intercellular layout. Computers & Industrial Engineering, 143, 106439.
Rezazadeh, H., & Khiali-Miab, A. (2017). A two-layer genetic algorithm for the design of reliable cellular manufacturing systems. International Journal of Industrial Engineering Computations, 8(3), 315-332.
Rheault, M., Drolet, J. R., & Abdulnour, G. (1995). Physically reconfigurable virtual cells: a dynamic model for a highly dynamic environment. Computers & Industrial Engineering, 29(1-4), 221-225.
Sadat Khorasgani, S. M., & Ghaffari, M. (2018). Developing a cellular manufacturing model considering the alternative routes, tool assignment, and machine reliability. Journal of Industrial Engineering International, 14(3), 627-636.
Saeidi, S., & Nikakhtar, N. A revised model for solving the Cell formation problem and solving by gray wolf optimization algorithm. (2020). Journal of Industrial Engineering and Management Studies, 9(7), 81-94.
Sakhaii, M., Tavakkoli-Moghaddam, R., Bagheri, M., & Vatani, B. (2016). A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines. Applied Mathematical Modelling, 40(1), 169-191.
Salimpour, S., Pourvaziri, H., & Azab, A. (2021). Semi-robust layout design for cellular manufacturing in a dynamic environment. Computers & Operations Research, 133, 105367.
Saxena, L., & Jain, P. (2011). Dynamic cellular manufacturing systems design-a comprehensive model. International Journal of Advanced Manufacturing Technology, 53 (1), 11-34.
Soto, R., Crawford, B., Olivares, R., Carrasco, C., Rodriguez-Tello, E., Castro, C., ... & de la Fuente-Mella, H. (2020). A reactive population approach on the dolphin echolocation algorithm for solving cell manufacturing systems. Mathematics, 8(9), 1389.
Vafaeinezhad, M., Kia, R., & Shahnazari-Shahrezaei, P. (2016). Robust optimization of a mathematical model to design a dynamic cell formation problem considering labor utilization. Journal of Industrial Engineering International, 12(1), 45-60.
Xue, G., & Offodile, O. F. (2020). Integrated optimization of dynamic cell formation and hierarchical production planning problems. Computers & Industrial Engineering, 139, 106155.