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An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling

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Abstract

In this paper, an enhanced Pareto-based artificial bee colony (EPABC) algorithm is proposed to solve the multi-objective flexible job-shop scheduling problem with the criteria to minimize the maximum completion time, the total workload of machines, and the workload of the critical machine simultaneously. First, it uses multiple strategies in a combination way to generate the initial solutions as the food sources with certain quality and diversity. Second, exploitation search procedures for both the employed bees and the onlooker bees are designed to generate the new neighbor food sources. Third, crossover operators are designed for the onlooker bee to exchange useful information. Meanwhile, it uses a Pareto archive set to record the nondominated solutions that participate in crossover with a certain probability. To enhance the local intensification, a local search based on critical path is embedded in the onlooker bee phase, and a recombination and select strategy is employed to determine the survival of the individuals. In addition, population is suitably adjusted to maintain diversity in scout bee phase. By using Taguchi method of design of experiment, the influence of several key parameters is investigated. Simulation results based on the benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed EPABC algorithm.

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Acknowledgments

The authors would like to thank the editor and the anonymous referees for their valuable comments to improve this paper. This research is partially supported by National Science Foundation of China (61174189, 61025018, 70871065, and 60834004), Program for New Century Excellent Talents in University (NCET-10-0505), Doctoral Program Foundation of Institutions of Higher Education of China (20100002110014), the National Key Basic Research and Development Program of China (no. 2009CB320602), and National Science and Technology Major Project of China (no. 2011ZX02504-008).

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Authors and Affiliations

  1. Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, 100084, China

    Ling Wang, Gang Zhou, Ye Xu & Min Liu

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  1. Ling Wang
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  2. Gang Zhou
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  3. Ye Xu
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  4. Min Liu
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Correspondence to Ling Wang.

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Wang, L., Zhou, G., Xu, Y. et al. An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. Int J Adv Manuf Technol 60, 1111–1123 (2012). https://doi.org/10.1007/s00170-011-3665-z

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  • DOI: https://doi.org/10.1007/s00170-011-3665-z

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