Abstract
This paper investigates the scheduling problem of parallel batch-processing machines which involves the constraints of non-identical job sizes, unequal release times, and batch dependent processing times for minimizing makespan. We proposed an Ant Colony Optimization (ACO) algorithm to solve the problem. of the ACO algorithm. In order to utilize the available information and obtain a tradeoff between exploitation and exploration, a novel construction policy and an efficient candidate list strategy were introduced during the process of solution construction of the ACO algorithm. Through extensive computational experiments, the effectiveness of the proposed algorithm was validated on different test problems. The results demonstrated that the proposed ACO algorithm had a superior performance compared to other benchmark algorithms.
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Xu, R., Chen, H., Shao, H. (2013). An Effective Ant Colony Approach for Scheduling Parallel Batch-Processing Machines. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_57
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DOI: https://doi.org/10.1007/978-3-642-41278-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41277-6
Online ISBN: 978-3-642-41278-3
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