Ant colony optimization algorithms for scheduling the mixed model assembly lines

Authors:
Xin-yu Sun;Lin-yan Sun
Affiliations:
School of management, the State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, P. R. China;School of management, the State Key Laboratory for Manufacturing System Engineering, Xi'an Jiaotong University, P. R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

Citing 2
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Abstract

Solving the mixed-model scheduling problem is the most important goal for Just-in-time production systems. But it is a difficult combinatorial optimization problem. This study presents a novel co-operative agents approach, Ant Colony Optimization algorithm (ACO) scheme, for solving the scheduling mixed-model assembly lines. The results show that the solution which ant algorithm produces is better than the one which Toyota's goal chasing algorithm, simulated annealing algorithm and genetic algorithm produce. Finally, this example may extend to a bigger scale, and the satisfied solutions, benchmark results and CPU time to generate a satisfied tour are given.