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A comparative study of Multi-Objective Algorithms for the Assembly Line Balancing and Equipment Selection Problem under consideration of Product Design Alternatives

Listed author(s):
  • Jonathan Oesterle


    (Fraunhofer Institute for Manufacturing Engineering and Automation - IPA)

  • Lionel Amodeo

    (University of Technology of Troyes)

  • Farouk Yalaoui

    (University of Technology of Troyes)

Registered author(s):

    Abstract A realistic and accurate product cost estimation is of high importance during the design phases of products and assembly lines. This paper presents a methodology that aims at supporting decision makers during the design phases of assembly lines by taking into consideration product designs, processes and resources alternatives. First, we introduce a new variant of the Assembly Line Balancing and Equipment Selection Problem, in which Product Design Alternatives are considered. Since the ability to estimate product costs provides grounds for making better decisions, a new detailed cost model whose aim is to translate the complex and interrelated consequences of product design and manufacturing technologies and processes choice into one single cost metric is proposed. In order to solve the problem under study, 34 Multi-Objective Algorithms were developed. The list of developed algorithms includes variants of Evolutionary Algorithms, Ant Colony Optimisation, Artificial Bee Colony, Cuckoo Search Optimisation, Flower Pollination Algorithm, Bat Algorithm and Particle Swarm Optimisation. The performances of all these algorithms are compared based on fifty well-known problem instances in accordance with four multi-objective quality indicators. Finally, the algorithms are ranked using a nonparametric statistical test.

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    Article provided by Springer in its journal Journal of Intelligent Manufacturing.

    Volume (Year): ()
    Issue (Month): ()
    Pages: 1-26

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    Handle: RePEc:spr:joinma:v::y::i::d:10.1007_s10845-017-1298-2
    DOI: 10.1007/s10845-017-1298-2
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    1. Johnson, Michael D. & Kirchain, Randolph E., 2009. "Quantifying the effects of product family decisions on material selection: A process-based costing approach," International Journal of Production Economics, Elsevier, vol. 120(2), pages 653-668, August.
    2. Amen, Matthias, 2000. "An exact method for cost-oriented assembly line balancing," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 187-195, March.
    3. Armin Scholl & Nils Boysen & Malte Fliedner, 2009. "Optimally solving the alternative subgraphs assembly line balancing problem," Annals of Operations Research, Springer, vol. 172(1), pages 243-258, November.
    4. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
    5. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2007. "A classification of assembly line balancing problems," European Journal of Operational Research, Elsevier, vol. 183(2), pages 674-693, December.
    6. Stephen C. Graves & Bruce W. Lamar, 1983. "An Integer Programming Procedure for Assembly System Design Problems," Operations Research, INFORMS, vol. 31(3), pages 522-545, June.
    7. Battaïa, Olga & Dolgui, Alexandre, 2013. "A taxonomy of line balancing problems and their solutionapproaches," International Journal of Production Economics, Elsevier, vol. 142(2), pages 259-277.
    8. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.
    9. Bahriye Akay, 2013. "Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms," Journal of Global Optimization, Springer, vol. 57(2), pages 415-445, October.
    10. Hamta, Nima & Fatemi Ghomi, S.M.T. & Jolai, F. & Akbarpour Shirazi, M., 2013. "A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, sequence-dependent setup times and learning effect," International Journal of Production Economics, Elsevier, vol. 141(1), pages 99-111.
    11. Peter A. Pinto & David G. Dannenbring & Basheer M. Khumawala, 1983. "Assembly Line Balancing with Processing Alternatives: An Application," Management Science, INFORMS, vol. 29(7), pages 817-830, July.
    12. Levitin, Gregory & Rubinovitz, Jacob & Shnits, Boris, 2006. "A genetic algorithm for robotic assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 811-825, February.
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