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A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand

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  • Chica, Manuel
  • Bautista, Joaquín
  • Cordón, Óscar
  • Damas, Sergio

Abstract

Changes in demand when manufacturing different products require an optimization model that includes robustness in its definition and methods to deal with it. In this work we propose the r-TSALBP, a multiobjective model for assembly line balancing to search for the most robust line configurations when demand changes. The robust model definition considers a set of demand scenarios and presents temporal and spatial overloads of the stations in the assembly line of the products to be assembled. We present two multiobjective evolutionary algorithms to deal with one of the r-TSALBP variants. The first algorithm uses an additional objective to evaluate the robustness of the solutions. The second algorithm employs a novel adaptive method to evolve separate populations of robust and non-robust solutions during the search. Results show the improvements of using robustness information during the search and the outstanding behavior of the adaptive evolutionary algorithm for solving the problem. Finally, we analyze the managerial impacts of considering the r-TSALBP model for the different organization departments by exploiting the values of the robustness metrics.

Suggested Citation

  • Chica, Manuel & Bautista, Joaquín & Cordón, Óscar & Damas, Sergio, 2016. "A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand," Omega, Elsevier, vol. 58(C), pages 55-68.
    • Handle: RePEc:eee:jomega:v:58:y:2016:i:c:p:55-68
    DOI: 10.1016/j.omega.2015.04.003
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    Cited by:

    1. Manzini, Massimo & Unglert, Johannes & Gyulai, Dávid & Colledani, Marcello & Jauregui-Becker, Juan Manuel & Monostori, László & Urgo, Marcello, 2018. "An integrated framework for design, management and operation of reconfigurable assembly systems," Omega, Elsevier, vol. 78(C), pages 69-84.
    2. Bautista, Joaquín & Batalla-García, Cristina & Alfaro-Pozo, Rocío, 2016. "Models for assembly line balancing by temporal, spatial and ergonomic risk attributes," European Journal of Operational Research, Elsevier, vol. 251(3), pages 814-829.
    3. Manuel Chica & Joaquín Bautista & Jesica de Armas, 2019. "Benefits of robust multiobjective optimization for flexible automotive assembly line balancing," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 75-103, March.
    4. Taube, F. & Minner, S., 2018. "Resequencing mixed-model assembly lines with restoration to customer orders," Omega, Elsevier, vol. 78(C), pages 99-111.
    5. Mike G. Tsionas, 2021. "Multi-criteria optimization in regression," Annals of Operations Research, Springer, vol. 306(1), pages 7-25, November.
    6. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    7. Tsai, Shing Chih & Chen, Sin Ting, 2017. "A simulation-based multi-objective optimization framework: A case study on inventory management," Omega, Elsevier, vol. 70(C), pages 148-159.
    8. Schmid, Nico André & Limère, Veronique & Raa, Birger, 2021. "Mixed model assembly line feeding with discrete location assignments and variable station space," Omega, Elsevier, vol. 102(C).
    9. Boysen, Nils & Schulze, Philipp & Scholl, Armin, 2022. "Assembly line balancing: What happened in the last fifteen years?," European Journal of Operational Research, Elsevier, vol. 301(3), pages 797-814.
    10. Battaïa, Olga & Dolgui, Alexandre, 2022. "Hybridizations in line balancing problems: A comprehensive review on new trends and formulations," International Journal of Production Economics, Elsevier, vol. 250(C).
    11. Barbati, Maria & Corrente, Salvatore & Greco, Salvatore, 2020. "A general space-time model for combinatorial optimization problems (and not only)," Omega, Elsevier, vol. 96(C).
    12. Tiacci, Lorenzo & Mimmi, Mario, 2018. "Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines," Omega, Elsevier, vol. 78(C), pages 112-138.
    13. Tsionas, Mike G., 2018. "A Bayesian approach to find Pareto optima in multiobjective programming problems using Sequential Monte Carlo algorithms," Omega, Elsevier, vol. 77(C), pages 73-79.

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