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Scheduling a Flow Line Manufacturing Cell with Sequence Dependent Family Setup Times Using Bilateral Genetic Algorithm (BGA)
Current Issue
Volume 3, 2016
Issue 3 (June)
Pages: 14-20   |   Vol. 3, No. 3, June 2016   |   Follow on         
Paper in PDF Downloads: 52   Since Jul. 1, 2016 Views: 667   Since Jul. 1, 2016
Authors
[1]
Ghorbanali Mohammadi, Industrial Engineering Department, College of Engineering, Qom University of Technology, Qom, Iran.
[2]
Darius Mohammadi, Electrical and Computer Engineering Department, College of Engineering, Iowa State University, Ames, IA, USA.
Abstract
This paper addressed the permutation of the flow line of manufacturing cell with sequence dependent parts family setup times to minimize the makespan criteria. We used the proposed BGA approach (Bilateral Genetic Algorithm) which is the evolution of GA algorithm procedure. Since the proposed BGA approach is being on the basis of GA algorithm, always does offer better solution than the GA algorithm. The proposed algorithm is evaluated by numerical experiments. By comparing the results of two methods BGA and GA, we realize that the converging rate and dispersion search in offered BGA algorithm would be more suitable and desirable than GA algorithm.
Keywords
Bilateral Genetic Algorithm, BGA, Flow Line Manufacturing Cell, Makespan, Setup Time
Reference
[1]
Allahverdi, A. (1999). Stochastically minimizing total flow time in flow shops with no waiting space. European Journal of Operational Research. 113, 101-112.
[2]
Billo, RE. (1998). A design methodology for configuration of manufacturing cells. Computers and Industrial Engineering. 34:63–75.
[3]
Chryssolouris, G., Subramanian, V. (2001). Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing. 281-293.
[4]
Franc-a PM, Gupta, JND, Mendes, AS, Moscato, P, Veltink, KJ. (2005). Evolutionary algorithms for scheduling a flow shop manufacturing cell with sequence dependent family setups. Computers and Industrial Engineering. 48(3):491–506.
[5]
Fattahi, P., Fallahi, A. (2010) Dynamic scheduling in flexible job shop system by considering simultaneously efficiency and stability. CIRP Journal of Manufacturing Science and Technology. 2:114-123.
[6]
Holland JH. Adaptation in natural and artificial Systems. Ann Arbor, MI: University of Michigan Press; 1975.
[7]
Hendizadeh, H., Faramarzi, H., Mansouri, SA. Gupta, JND. Elmekkawy, TY. (2008) Metaheuristics for scheduling a flow shop manufacturing cell with sequence dependent family setup times. International Journal of Production Economics 2008; 111:593–605.
[8]
Isler, M. C., Toklu, B., Celic V. (2012) Scheduling in a two-machine flow-shop for earliness/tardiness under learning effect. The International Journal of Advanced Manufacturing Technology. 61:1129-1137.
[9]
Jeon, G, Leep HR. Forming part families by using genetic algorithm and designing machine cells under demand change. Computers and Operations Research 2006; 33:263–83.
[10]
Lalas C., Mourtizis, D., Papakostas, N., Chryssolouris, G. (2009). A Simulation-Based Hybrid Backwards Scheduling Framework for Manufacturing Systems. International Journal of Computer Integrated Manufacturing. 19:762-774.
[11]
Lin, SW., Ying, KC. Lee, ZJ. (2009). Metaheuristics for scheduling a non-permutation flow line manufacturing cell with sequence dependent family setup times. Computers and Operations Research. 36:1110–21.
[12]
Mungwattana, A Design of Cellular Manufacturing Systems for Dynamic and Uncertain Production Requirements with Presence of Routing Flexibility, Doctoral Dissertation, The Virginia Polytechnic Institute and State University, Blacksburg, Virginia, 2000.
[13]
Michalos, G., Makris, S., Mourtzis, DS. (2011). A web based tool for dynamic job rotation scheduling using multiple criteria. CIRP Annals-Manufacturing Technology. 60, 453-456.
[14]
Mohammadi, G. (2015) Multi objective flow shop production scheduling via robust genetic algorithms optimization technique. International Journal of Service science, Management and Engineering. 2 (1), 1-8.
[15]
Schaller, J. E., Gupta, J. N. D., and Vakharia, A. J. (2000). Scheduling a Flow line Manufacturing Cell with Sequence Dependent Family Setup Times, European Journal of Operational Research, 125, 324–339.
[16]
Schaller, J. A. (2000). Comparison of heuristics for family and job scheduling in a flow line Manufacturing cell. International Journal of Production Research. 38(2):287–308.
[17]
Skorin-Kapov, J., Vakharia, AJ. (1993). Scheduling a flow line manufacturing cell: ATabu search approach. International Journal of Production Research. 31(7):1721–1734.
[18]
Solimanpur, M., Vrat, P., and Shanker, R. (2004). A Heuristic to Minimize Makespan of Cell scheduling Problem. International Journal Production Economics. 88:3, 231-241.
[19]
Schaller,JE. Gupta, JND. Vakharia, AJ. (2000). Scheduling a flow line manufacturing cell with sequence dependent family setup times. European Journal of Operational Research. 125:324–39.
[20]
Radhouan, B., Bassem, J., Mansour, E., Abdelwaheb, R. (2011). A branch and bound enhanced genetic algorithm for scheduling a flow line manufacturing cell with sequence dependent family setup times, Computers & Operations Research38, 387–393.
[21]
Rajendran, C. and Ziegler, H. (1999). Heuristics for Scheduling in Flow shops and Flow line-based Manufacturing Cells to Minimize the Sum of Weighted Flow time and Weighted Tardiness of Jobs. Computers and Industrial Engineering. 37, 671-690.
[22]
Vakharia, AJ. Chang, YL. (1990). A simulated annealing approach to scheduling a manufacturing cell. Naval Research Logistics. 37:559–77.
[23]
Wemmerlov, U. and Hyer, N. L. (1989). Cellular Manufacturing in U. S. Industry: A Survey of Users. International Journal of Production Research.27, 1511-1530, 1989.
[24]
Yang, w. h. and Liao, C. J. (1996). Group Scheduling on Two Cells with Inter cell Movement. Computers and Operations Research. 23:10, 997-1006.
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