Abstract
Scheduling problems are difficult combinatorial problems because of the extremely large search space of possible solutions and the large number of local optima that arise. A multi-objective genetic algorithm is presented as an intelligent algorithm for scheduling of the mixed-model assembly line in this paper. The Pareto ranking method and distance-dispersed approach are employed to evaluate the fitness of the individuals. The computational results show that the proposed multi-objective genetic algorithm is quite effective.
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References
Steiner G, Yeomans JS (1996) Optimal level schedules in mixed-model, multi-level JIT assembly systems with pegging. Eur J Oper Res 95(1):38–52
Reeves CR (1995) A genetic algorithm for flowshop sequencing. Comput Oper Res 22(1):5–13
Chang PC, Hsieh JC, Lin SG (2002) The development of gradual-priority weighting approach for the multi-objective flowshop scheduling problem. Int J Prod Econ 79:171–183
Jaszkiewicz A (2002) Genetic local search for multi-objective combinatorial optimization. Eur J Oper Res 137:50–71
Kiyota T, Tsuji Y, Kondo E (2003) Unsatisfying functions and multiobjective fuzzy satisfaction design using genetic algorithms. IEEE Trans Syst Man Cybern B Cybern 33(6):889–897
Obayashi S, Tsukahara T, Nakamura T (2000) Multiobjective genetic algorithm applied to aerodynamic design of cascade airfoils. IEEE Trans Ind Electron 47(1):211–216
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Yu, J., Yin, Y. & Chen, Z. Scheduling of an assembly line with a multi-objective genetic algorithm. Int J Adv Manuf Technol 28, 551–555 (2006). https://doi.org/10.1007/s00170-004-2387-x
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DOI: https://doi.org/10.1007/s00170-004-2387-x