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Adaptive Immune-Genetic Algorithm for Fuzzy Job Shop Scheduling Problems

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Advances in Swarm Intelligence (ICSI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8794))

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Abstract

In recent years, fuzzy job shop scheduling problems (FJSSP) with fuzzy triangular processing time and fuzzy due date have received an increasing interests because of its flexibility and similarity with practical problems. The objective of FJSSP is to maximize the minimal average customer’s degree of satisfaction. In this paper, a novel adaptive immune-genetic algorithm (CAGA) is proposed to solve FJSSP. CAGA manipulates a number of individuals to involve the progresses of clonal proliferation, adaptive genetic mutations and clone selection. The main characteristic of CAGA is the usage of clone proliferation to generate more clones for fitter individuals which undergo the adaptive genetic mutations, thus leading a fast convergence. Moreover, the encoding scheme of CAGA is also properly adapted for FJSSP. Simulation results based on several instances verify the effectiveness of CAGA in terms of search capacity and convergence performance.

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Author information

Authors and Affiliations

  1. Department of Automation, Donghua University, Shanghai, 201620, China

    Beibei Chen & Shangce Gao

  2. Faculty of Engineering, University of Toyama, Toyama, 930-8555, Japan

    Shangce Gao & Aorigele Bao

  3. Department of Computer Science, Tongji University, Shanghai, 201804, China

    Shuaiqun Wang

Authors
  1. Beibei Chen
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  2. Shangce Gao
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  3. Shuaiqun Wang
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  4. Aorigele Bao
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Computer Science Department, CINVESTAV-IPN, Mexico

    Carlos A. Coello Coello

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© 2014 Springer International Publishing Switzerland

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Chen, B., Gao, S., Wang, S., Bao, A. (2014). Adaptive Immune-Genetic Algorithm for Fuzzy Job Shop Scheduling Problems. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_28

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  • DOI: https://doi.org/10.1007/978-3-319-11857-4_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11856-7

  • Online ISBN: 978-3-319-11857-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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