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An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems

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Abstract

Lot-streaming scheduling problem has been an active area of research due to its important applications in modern industries. This paper deals with the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion. An effective discrete invasive weed optimization (DIWO) algorithm is presented with new characteristics. A job permutation representation is utilized and an adapted Nawaz–Enscore–Ham heuristic is employed to ensure an initial weed colony with a certain level of quality. A new spatial dispersal model is designed based on the normal distribution and the property of tangent function to enhance global search. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. The presented DIWO is calibrated by means of the design of experiments approach. A comparative evaluation is carried out with several best performing algorithms based on a total of 280 randomly generated instances. The numerical experiments show that the presented DIWO algorithm produces significantly better results than the competing algorithms and it constitutes a new state-of-the-art solution for the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion.

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Acknowledgments

This research is partially supported by National Science Foundation of China (515775212, 61503170, 61573178, 61374187), Program for New Century Excellent Talents in University (NCET-13-0106), Specialized Research Fund for the Doctoral Program of Higher Education (20130042110035), Shandong Province Higher Educational Science and Technology Program (J14LN28), Science Foundation of Hubei Province in China (2015CFB560), Key Laboratory Basic Research Foundation of Education Department of Liaoning Province (LZ2014014). Open Research Fund Program of the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, China.

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Authors and Affiliations

  1. School of Computer Science, Liaocheng University, Liaocheng, 252059, China

    Hong-Yan Sang, Pei-Yong Duan & Jun-Qing Li

  2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Quan-Ke Pan

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  1. Hong-Yan Sang
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Correspondence to Quan-Ke Pan.

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Sang, HY., Pan, QK., Duan, PY. et al. An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems. J Intell Manuf 29, 1337–1349 (2018). https://doi.org/10.1007/s10845-015-1182-x

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