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A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem

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  • Chang, Pei-Chann
  • Huang, Wei-Hsiu
  • Wu, Jheng-Long
  • Cheng, T.C.E.

Abstract

The goal of block mining is to obtain a set of genes that contain dependency among gene relationships. Such blocks without overlapping of genes can be further merged to form a new chromosome and the quality of the new chromosome can be greatly improved. Based on this concept, we propose a novel block mining method that is able to locate common structures or to establish new blocks (like a small piece of puzzle) from a set of high fit chromosomes. The identified blocks (puzzles) will also be updated generation by generation through the newly updated high fit chromosomes. We develop a heuristic re-combination procedure to form a new chromosome by re-combining the blocks. We call the new chromosomes generated as artificial chromosomes (ACs) and inject them into the evolutionary process when the convergence slope of the evolutionary process is less than a predefined threshold. This new algorithm retains the regular simple genetic algorithm (SGA) operations of crossover and mutation, and utilizes the ACs generated from elites to speed up the convergence process. Experimental results indicate that the puzzle-based method of chromosome generation is very efficient and effective in solving the traditional permutation flowshop scheduling problem. The new method can be applied to tackle other NP-complete problems such as scheduling and vehicle routing problems.

Suggested Citation

  • Chang, Pei-Chann & Huang, Wei-Hsiu & Wu, Jheng-Long & Cheng, T.C.E., 2013. "A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 141(1), pages 45-55.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:45-55
    DOI: 10.1016/j.ijpe.2012.06.007
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    References listed on IDEAS

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    1. Chen, Chuen-Lung & Vempati, Venkateswara S. & Aljaber, Nasser, 1995. "An application of genetic algorithms for flow shop problems," European Journal of Operational Research, Elsevier, vol. 80(2), pages 389-396, January.
    2. Zhang, Yi & Li, Xiaoping & Wang, Qian, 2009. "Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization," European Journal of Operational Research, Elsevier, vol. 196(3), pages 869-876, August.
    3. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    4. Pei-Chann Chang & Shih-Hsin Chen & Chin-Yuan Fan & V. Mani, 2010. "Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems," Annals of Operations Research, Springer, vol. 180(1), pages 197-211, November.
    5. Tasgetiren, M. Fatih & Liang, Yun-Chia & Sevkli, Mehmet & Gencyilmaz, Gunes, 2007. "A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1930-1947, March.
    6. Ruiz, Rubén & Maroto, Concepciøn & Alcaraz, Javier, 2006. "Two new robust genetic algorithms for the flowshop scheduling problem," Omega, Elsevier, vol. 34(5), pages 461-476, October.
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    Cited by:

    1. Chen, Gang & Govindan, Kannan & Yang, Zhong-Zhen & Choi, Tsan-Ming & Jiang, Liping, 2013. "Terminal appointment system design by non-stationary M(t)/Ek/c(t) queueing model and genetic algorithm," International Journal of Production Economics, Elsevier, vol. 146(2), pages 694-703.
    2. Xuemei Qi & Hongtao Wang & Haihong Zhu & Ji Zhang & Fulong Chen & Jie Yang, 2016. "Fast local neighborhood search algorithm for the no-wait flow shop scheduling with total flow time minimization," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4957-4972, August.
    3. Yifei Tong & Jingwei Li & Shai Li & Dongbo Li, 2016. "Research on Energy-Saving Production Scheduling Based on a Clustering Algorithm for a Forging Enterprise," Sustainability, MDPI, vol. 8(2), pages 1-17, February.
    4. Burcu Yılmaz Kaya & Aylin Adem & Metin Dağdeviren, 2020. "A DSS-Based Novel Approach Proposition Employing Decision Techniques for System Design," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 413-445, March.
    5. Zhang, Zhenzhen & Wei, Lijun & Lim, Andrew, 2015. "An evolutionary local search for the capacitated vehicle routing problem minimizing fuel consumption under three-dimensional loading constraints," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 20-35.

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