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Must--A Multiple Solutions Technique for Balancing Single Model Assembly Lines


  • E. M. Dar-El

    (Technion-Israel Institute of Technology)

  • Y. Rubinovitch

    (Control Data Corporation, Tel-Aviv)


Research on single-model assembly line balancing has produced several good algorithms for solving large problems. All these algorithms, however, generate just one solution to the balancing problem. With these, line designers wishing to investigate alternative station combinations of work elements are forced to do so manually. This paper concerns an algorithm (MUST) which generates alternative solutions of equal quality for single-model assembly line balancing problems. The method is based on the Mansoor-Yadin algorithm and in its optimum seeking form, MUST generates all existing optimal balances in a single pass. Three heuristics, used singularly or together, are introduced for solving the larger balancing problems. These are designed to produce about 100 different balance solutions of equal quality at each pass. MUST has been successfully used in balancing assembly lines having up to 140 work elements with widely differing precedence structures. A comparison of this method with MALB, one of the most efficient known heuristic methods, results in MUST dominating or equalling MALB in every case. Reasonable computation times (average 125.4 sec.--IBM 370/168) and core usage are achieved through the use of advanced computation methods. The need to explore alternative station combinations may arise in several ways--for example, the line designer may prefer (but not require) that certain work elements be allocated to a common station either because of: access to materials handling and storage facilities, service availability (e.g., compressed air lines), use of common tools or operator skills, etc. Alternatively, minor adjustments to the line may be required between adjacent stations in order to alleviate conditions at one station which exhibits excessive variability in its performance times. The generation of multiple solutions of equal efficiency adds a new dimension to the quality of balancing effectiveness, enabling the line designer to select the alternative that best suits his requirements.

Suggested Citation

  • E. M. Dar-El & Y. Rubinovitch, 1979. "Must--A Multiple Solutions Technique for Balancing Single Model Assembly Lines," Management Science, INFORMS, vol. 25(11), pages 1105-1114, November.
  • Handle: RePEc:inm:ormnsc:v:25:y:1979:i:11:p:1105-1114

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    Cited by:

    1. Rubinovitz, J. & Levitin, G., 1995. "Genetic algorithm for assembly line balancing," International Journal of Production Economics, Elsevier, vol. 41(1-3), pages 343-354, October.
    2. Scholl, Armin & Fliedner, Malte & Boysen, Nils, 2010. "Absalom: Balancing assembly lines with assignment restrictions," European Journal of Operational Research, Elsevier, vol. 200(3), pages 688-701, February.
    3. Hsiu-Hsueh Kao & Din-Horng Yeh & Yi-Hsien Wang, 2011. "Resource Constrained Assembly Line Balancing Problem Solved with Ranked Positional Weight Rule," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 71-80, November.
    4. M. H. Alavidoost & M. H. Fazel Zarandi & Mosahar Tarimoradi & Yaser Nemati, 2017. "Modified genetic algorithm for simple straight and U-shaped assembly line balancing with fuzzy processing times," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 313-336, February.
    5. Boysen, Nils & Fliedner, Malte, 2008. "A versatile algorithm for assembly line balancing," European Journal of Operational Research, Elsevier, vol. 184(1), pages 39-56, January.
    6. repec:eee:ejores:v:263:y:2017:i:3:p:1033-1048 is not listed on IDEAS
    7. Cohen, Yuval & Vitner, Gad & Sarin, Subhash C., 2006. "Optimal allocation of work in assembly lines for lots with homogenous learning," European Journal of Operational Research, Elsevier, vol. 168(3), pages 922-931, February.
    8. Scholl, Armin & Becker, Christian, 2006. "State-of-the-art exact and heuristic solution procedures for simple assembly line balancing," European Journal of Operational Research, Elsevier, vol. 168(3), pages 666-693, February.


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