Dynamic cellular manufacturing system considering machine failure and workload balance
محورهای موضوعی : Mathematical Optimization
Masoud Rabbani
1
(School of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran, Iran)
Hamed Farrokhi-Asl
2
(School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran)
Mohammad Ravanbakhsh
3
(School of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran, Iran)
کلید واژه: Dynamic cellular manufacturing system . Labor utilization . Machine failure . Alternative processing routs . Multi, objective optimization,
چکیده مقاله :
Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.
Aghajani A, Ahmadi-Didehbani S, Zadahmad M, Seyedrezaei MH, Mohsenian O (2014) A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis. Int J Manuf Technol. https:// doi.org/10.1007/s00170-014-6084-0
Aramoon Bajestani M, Rabbani M, Rahimi-Vahed AR, Baharian Khoshkhou GA (2009) Multi-objective scatter search for a
dynamic cell formation problem. Comput Oper Res 36:777–794
Aryanezhad MB, Deljoo V, Mirzapour Al-e-Hashem SMJ (2009) Dynamic cell formation and the worker assignment problem: a new model. Int J Adv Manuf Techol 41(3):329–342
Azadeh A, Rezaei-Malek M, Evazabadian F, Sheikhalishahi M (2014) Improved design of CMS by considering operators decisionmaking styles. International Journal of Production Research (ahead-of-print) 1–12
Azadeh A, Sangari MS, Sangari E, Fatehi S (2015) A particle swarm algorithm for optimising inspection policies in serial multistage production processes with uncertain inspection costs. Int J Comput Integr Manuf 28(7):766–780
Browne J, Dubois D, Rathmill K, Sethi SP, Stecke KE (1984) Classification of flexible manufacturing systems. FMS mag
2(2):114–117
Chan HM, Milner DA (1982) direct clustering algorithm for group formation in cellular manufacturing. J Manuf Syst 1(1):65–75
Chung SH, Wu TH, Chang CC (2011) An efficient tabu search algorithm to the cell formation problem with alternative routings and machine reliability considerations. Comput Ind Eng 60(1):7–15
Defersha FM, Chen M (2006) A comprehensive mathematical model for the design of cellular manufacturing system. Int J Prod Econ 103:767–783
Ebeling CE (1997) An introduction to reliability and maintainability engineering. McGraw-Hill, New York
Ehrgott M (2006) Multicriteria optimization. Springer, New York
Ghotboddini MM, Rabbani M, Rahimian H (2011) A comprehensive dynamic cell formation design: benders’ decomposition approach. Expert Syst Appl 38(3):2478–2488
Hosseini A, Paydar MM, Mahdavi I, Jouzdani J (2016) Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm. J Optim Ind Eng 9(20):41–51
Jabal Ameli MS, Barzinpour F, Arkat J (2008) Modelling the effects of machine breakdowns in the generalized cell formation problem. Int J Adv Manuf Technol 39:838–850
Jouzdani J, Barzinpour F, Shafia MA, Fathian M (2014) Applying simulated annealing to a generalized cell formation problem considering alternative routings and machine reliability. AsiaPacific J Oper Res 31(04):1450021
Kannan VR (1998) Analysing the trade-off between efficiency and flexibility in cellular manufacturing system. Prod Plan Control 9(6):572–579
Karim R, Biswas SK (2015) Cell formation in a batch oriented production system using a local search heuristic with a genetic Aagorithm: an application of cellular manufacturing system. IOSR J Eng 5(4):28–41
Mehdizadeh E, Rahimi V (2016) An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts. Appl Soft Comput 42:325–341
Mehdizadeh E, Niaki SVD, Rahimi V (2016) A vibration damping optimization algorithm for solving a new multi-objective
dynamic cell formation problem with workers training. Comput Ind Eng 101:35–52
Moradgholi M, Paydar MM, Mahdavi I, Jouzdani J (2016) A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem. J Ind Eng Int 12(3):343–359
Mungwattana A (2000) Design of cellular manufacturing systems for dynamic and uncertain production requirements with presence of routing flexibility (Doctoral dissertation, Virginia Tech)
Niakan F, Baboli A, Moyaux T, Botta-Genoulaz V (2016) A biobjective model in sustainable dynamic cell formation problem with skill-based worker assignment. J Manuf Syst 38:46–62
Rabbani M, Farrokhi-Asl H, Rafiei H, Khaleghi R (2016) Using metaheuristic algorithms to solve a dynamic cell formation
problem with consideration of intra-cell layout design. Intelligent Decision Technologies, vol. Preprint, no. Preprint, pp. 1–18
Rabbania M, Keyhanianb S, Manavizadehc N, Farrokhi-Asld H (2017) Integrated dynamic cell formation-production planning: a new mathematical model. Sci Iran 24(5):2550–2566
Rabbani M, Ramezankhani MJ, Farrokhi-Asl H, Farshbaf-Geranmayeh A (2015) Vehicle routing with time windows and
customer selection for perishable goods. Int J Supply Operations Manage 2(2):700–719
Rafiei H, Ghods R (2013) A bi-objective mathematical model toward dynamic cell formation considering labor utilization. Appl Math Model 37:2308–2316
Ranjbar-Bourani M, Tavakkoli-Moghaddam R, Amoozad-Khalili H, Hashemian SM (2008) Applying scatter search algorithm based on TOPSIS to multi-objective cellular manufacturing system design. Int J Ind Eng (IJTE) 4870
Saad MS, Baykasoglu A, Gindy NNZ (2002) An integrated framework for reconfiguration of cellular manufacturing systems using virtual cells. Prod Plan Control 13(4):381–393
Safaei N, Saidi-Mehrabad M, Jabal-Ameli MS (2008) A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. Eur J Oper Res 185(2008):563–592
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. Appl Math Modell 40(1):169–191
Selim HM, Askin RG, Vakharia AJ (1998) cell formation group technology, review evaluation and direction for future research. Comput Ind Eng 34(1):3–20
Singh N, Rajamani D (2012) Cellular manufacturing systems: design, planning and control. Springer Science & Business Media, Berlin
Tavakkoli-Moghaddam R, Aryanezhad MB, Safaei N, Azaron A (2005) Solving a dynamic cell formation problem using metaheuristics. Appl Math Comput 170:761–780
Tavakkoli-Moghaddam R, Ranjbar-Bourani M, Amin GR, Siadat A (2012) A cell formation problem considering machine utilization and alternative process routes by scatter search. J Intell Manuf 23(4):1127–1139
Walpole RE, Myers RH, Myers SL, Ye K (1993) Probability and statistics for engineers and scientists, vol 5. Macmillan, New
York
Zhao C, Wu Z (2000) A genetic algorithm for manufacturing cellformation whit multi routes and multiple objective. Int J Prod Res 38(2):385–395
