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Discrete artificial bee colony algorithm for lot-streaming flowshop with total flowtime minimization

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Abstract

Unlike a traditional flowshop problem where a job is assumed to be indivisible, in the lot-streaming flowshop problem, a job is allowed to overlap its operations between successive machines by splitting it into a number of smaller sub-lots and moving the completed portion of the sub-lots to downstream machine. In this way, the production is accelerated. This paper presents a discrete artificial bee colony (DABC) algorithm for a lot-streaming flowshop scheduling problem with total flowtime criterion. Unlike the basic ABC algorithm, the proposed DABC algorithm represents a solution as a discrete job permutation. An efficient initialization scheme based on the extended Nawaz-Enscore-Ham heuristic is utilized to produce an initial population with a certain level of quality and diversity. Employed and onlooker bees generate new solutions in their neighborhood, whereas scout bees generate new solutions by performing insert operator and swap operator to the best solution found so far. Moreover, a simple but effective local search is embedded in the algorithm to enhance local exploitation capability. A comparative experiment is carried out with the existing discrete particle swarm optimization, hybrid genetic algorithm, threshold accepting, simulated annealing and ant colony optimization algorithms based on a total of 160 randomly generated instances. The experimental results show that the proposed DABC algorithm is quite effective for the lot-streaming flowshop with total flowtime criterion in terms of searching quality, robustness and effectiveness. This research provides the references to the optimization research on lot-streaming flowshop.

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Author information

Authors and Affiliations

  1. State Key Lab. of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Hongyan Sang, Liang Gao & Quanke Pan

  2. School of Mathematics Science, Liaocheng University, Liaocheng, 252059, China

    Hongyan Sang

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

    Quanke Pan

Authors
  1. Hongyan Sang
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  2. Liang Gao
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  3. Quanke Pan
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Corresponding author

Correspondence to Liang Gao.

Additional information

This project is supported by National Natural Science Foundation of China (Grant Nos. 60973085, 61174187), National Hi-tech Research and Development Program of China (863 Program, Grant No. 2009AA044601), and New Century Excellent Talents in University of China (Grant No. NCET-08-0232)

SANG Hongyan, born in 1981, is currently a PhD candidate at State Key Lab. of Digital Manufacturing Equipment & Technology, Huazhong University of Science and Technology, China. She received her bachelor degree from Liaocheng University, China, in 2003. Her research interests include intelligent algorithms and scheduling problems.

GAO Liang, born in 1974, is currently a full-time professor at Huazhong University of Science and Technology, China. He received his PhD degree in mechatronic engineering from Huazhong University of Science and Technology, China, in 2002. His research interests include collaborative design, shop scheduling, swarm intelligence, etc.

PAN Quanke, born in 1971, is a full-time professor at Liaocheng University, China. He received his PhD degree in mechatronic engineering from University of Science and Technology, China, in 2003. His research interests include collaborative design, shop scheduling, swarm intelligence, etc.

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Sang, H., Gao, L. & Pan, Q. Discrete artificial bee colony algorithm for lot-streaming flowshop with total flowtime minimization. Chin. J. Mech. Eng. 25, 990–1000 (2012). https://doi.org/10.3901/CJME.2012.05.990

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  • DOI: https://doi.org/10.3901/CJME.2012.05.990

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