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Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm

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Intelligent Robotics and Applications (ICIRA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5315))

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Abstract

This paper is concerned about how to optimize the input sequence for a mixed-model assembly line (MMAL) with limited intermediate buffers. Three optimization objectives are considered simultaneously: minimizing the total production rate variation, the total setup, and the total assembly cost. The mathematical model is presented by incorporating the three objectives. Since the problem is NP-hard, a hybrid algorithm based on genetic algorithm (GA) and simulated annealing (SA), is proposed for solving the model. The performance of the proposed algorithm is compared with a genetic algorithm for different-sized sequencing problems in MMALs that consist of different number of machines and different production plans. The computational results show that the proposed hybrid algorithm finds solutions with better quality and often needs a smaller number of generations to converge to a final stable state, especially in the case of large-sized problems.

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References

  1. Bard, J.F., Shtub, A., Joshi, S.B.: Sequencing Mixed-Model Assembly Lines to Level Parts Usage and Minimizing Line Length. International Journal of Production Research 32(10), 2431–2454 (1994)

    Article  MATH  Google Scholar 

  2. Hyun, C.J., Kim, Y., Kim, Y.K.: A Genetic Algorithm for Multiple Objective Sequencing Problems in Mixed Model Assembly Lines. Computers and Operations Research 25(7/8), 675–690 (1998)

    MATH  Google Scholar 

  3. McMullen, P.R., Frazier, G.V.: A Simulated Annealing Approach to Mixed-Model Sequencing with Multiple Objectives on a JIT Time. IIE Transactions 3(8), 679–686 (2000)

    Google Scholar 

  4. McMullen, P.R.: JIT Sequencing for Mixed-Model Assembly Lines with Setups Using Tabu Search. Production Planning and Control 9(5), 504–510 (1998)

    Article  Google Scholar 

  5. McMullen, P.R.: An Efficient Frontier Approach to Addressing JIT Sequencing Problems with Setups Via Search Heuristics. Computers and Industrial Engineering 41, 335–353 (2001)

    Article  Google Scholar 

  6. McMullen, P.R.: A Kohonen Self-Organizing Map Approach to Addressing a Multiple Objective, Mixed-Model JIT Sequencing Problem. International Journal of Production Economics 72, 59–71 (2001)

    Article  Google Scholar 

  7. McMullen, P.R.: An Ant Colony Optimization Approach to Addressing a JIT Sequencing Problem with Multiple Objectives. Artificial Intelligence in Engineering 15, 309–317 (2001)

    Article  Google Scholar 

  8. Guo, Z.X., Wong, W.K., Leung, S.Y.S., Fan, J.T.: A Genetic-Algorithm-Based Optimization Model for Scheduling Flexible Assembly Lines. International Journal of Advanced Manufacturing Technology (2007), doi:10.1007/s00170-006-0818-6

    Google Scholar 

  9. Tavakkoli-Moghaddam, R., Rahimi-Vahed, A.R.: Multi-Criteria Sequencing Problem for a Mixed-Model Assembly Line in a JIT Production System. Applied Mathematics and Computation 181, 1471–1481 (2006)

    Article  MATH  Google Scholar 

  10. Yu, J.f., Yin, Y.H., Chen, Z.N.: Scheduling of an Assembly Line with a Multi-Objective Genetic Algorithm. International Journal of Advanced Manufacturing Technology 28, 551–555 (2006)

    Article  Google Scholar 

  11. Miltenburg, J.: Level Schedules for Mixed-Model Assembly Lines in Just-In-Time Production Systems. Management Science 35(2), 192–207 (1989)

    Article  MATH  Google Scholar 

  12. Wang, L., Zhang, L., Zheng, D.Z.: An Effective Hybrid Genetic Algorithm for Flow Shop Scheduling with Limited Buffers. Computers & Operations Research 33, 2960–2971 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  13. Metropolis, N., Rosenbluth, A., Rosenbluth, N., Teller, A., Teller, E.: Equation of State Calculations by Fast Computing Machines. Journal of Chemical Physics 21, 1087–1092 (1953)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Binggang Wang, Yunqing Rao, Xinyu Shao & Mengchang Wang

Authors
  1. Binggang Wang
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  2. Yunqing Rao
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  3. Xinyu Shao
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  4. Mengchang Wang
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Editor information

Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074, Wuhan, China

    Caihua Xiong

  2. The Institute of Industrial Research, University of Portsmouth, UK

    Honghai Liu

  3. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 430074, Wuhan, P. R. China

    Yongan Huang  & Youlun Xiong  & 

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© 2008 Springer-Verlag Berlin Heidelberg

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Wang, B., Rao, Y., Shao, X., Wang, M. (2008). Scheduling Mixed-Model Assembly Lines with Cost Objectives by a Hybrid Algorithm. In: Xiong, C., Liu, H., Huang, Y., Xiong, Y. (eds) Intelligent Robotics and Applications. ICIRA 2008. Lecture Notes in Computer Science(), vol 5315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88518-4_41

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  • DOI: https://doi.org/10.1007/978-3-540-88518-4_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88516-0

  • Online ISBN: 978-3-540-88518-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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