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2-Approximation and Hybrid Genetic Algorithm for Long Chain Design Problem in Process Flexibility

  • Conference paper
Intelligent Computing for Sustainable Energy and Environment (ICSEE 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 355))

Abstract

Long chain flexibility strategy is an effective way to match the supply with the uncertain demand in manufacturing system. However there are few studies on the long chain design problem with nonhomogeneous link costs. This paper first presents a mixed 0-1 LP model and proves that it belongs to NP-complete. Then an approximation algorithm is proposed which includes three steps: 1) solve a relaxed LP; 2) generate a minimum spanning tree; 3) find the optimal local match. Under the quadrangle inequality assumption, we show that it is a 2-approximation algorithm. At last, based on another equivalent reformulation, we embed the 2-approximation algorithm and a 2-opt exchange local search into a hybrid genetic algorithm. By comparison with CPLEX solver, numerical experiments validate the effectiveness of the proposed algorithms.

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Author information

Authors and Affiliations

  1. Dept. of Automation, TNList, Tsinghua University, Beijing, 100084, P.R. China

    Yuli Zhang, Shiji Song & Cheng Wu

Authors
  1. Yuli Zhang
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  2. Shiji Song
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  3. Cheng Wu
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Editor information

Editors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK

    Kang Li

  2. Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Shaoyuan Li  & Dewei Li  & 

  3. School of Mechatronics Engineering and Automation, Shanghai University, 149 Yanchang Road, 200072, Shanghai, China

    Qun Niu

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Zhang, Y., Song, S., Wu, C. (2013). 2-Approximation and Hybrid Genetic Algorithm for Long Chain Design Problem in Process Flexibility. In: Li, K., Li, S., Li, D., Niu, Q. (eds) Intelligent Computing for Sustainable Energy and Environment. ICSEE 2012. Communications in Computer and Information Science, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37105-9_26

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  • DOI: https://doi.org/10.1007/978-3-642-37105-9_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37104-2

  • Online ISBN: 978-3-642-37105-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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