A Multi-Objective Optimization Approach Using Genetic Algorithms to Reduce the Level of Variability from Flow Manufacturing
This paper exemplifies a framework for development of multi-objective genetic algorithm based job sequencing method by taking account of multiple resource constraints. Along this, Theory of Constraints based Drum-Buffer-Rope methodology has been combined with genetic algorithm to exploit the system constraints. This paper introduces the Drum- Buffer-Rope to exploit the system constraints, which may affect the lead times, hroughput and higher inventory holding costs. Multi-Objective genetic algorithm is introduced for job sequence optimization to minimize the lead times and total inventory holding cost, which includes problem encoding, chromosome representation, selection, genetic operators and fitness measurements, where Queuing times and Throughput are used as fitness measures. The algorithm generates a sequence to maximize the throughput and minimize the queuing time on bottleneck/Capacity Constraint Resource (CCR). Finally, Results are analyzed to show the improvement by using current research framework.
Citation : Kang, P.S., Khalil, R. and Stockton, D. (2012) A Multi-Objective Optimization Approach Using Genetic Algorithms to Reduce the Level of Variability from Flow Manufacturing. Proceedings of IEEE International Conference on Engineering Technology and Economic Management, 21 to 22 May, 2012,Â Zhenzhou, China, pp. 115-119.
ISBN : 9781457720970
Peer Reviewed : Yes