Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm |
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Authors: | Anoop Prakash M K Tiwari R Shankar |
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Affiliation: | (1) Computer Aided Manufacturing Laboratory, Department of Mechanical Engineering, University of Cincinnati, Cincinnati, USA;(2) Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, India;(3) Department of Management Studies, Indian Institute of Technology (IIT), Hauz Khas, New Delhi, 110016, India |
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Abstract: | In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine
loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked
with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance
and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance
of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate
that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies
in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the
algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm
viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In
addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate
the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis. |
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Keywords: | Flexible manufacturing system Machine loading Random search optimization ANOVA Heuristic gap |
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