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Modeling and solving the mixed-model sequencing problem to improve productivity

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  • Bautista, Joaquín
  • Alfaro, Rocío
  • Batalla, Cristina

Abstract

In this paper, it is presented an extension of the mixed-model sequencing problem with work overload minimization (MMSP-W) for production lines with serial workstations, parallel homogeneous processors, and variable operation processing times. This extension is intended to consider that the processing times of the operations can be prolonged or shrunk with respect to the established standard processing times depending on the work pace of the workers. To do this, the activity of workers is set by means of functions which take into account the periods of adaptation and fatigue of the beginning and end of the workday, respectively. Thus, two mathematical models and four functions for the work pace factor are presented and their performances are analyzed through a case study of the Nissan powertrain plant in Barcelona, using the Gurobi solver. The results show that the work overload can completely be either eliminated with an increase of the activity of operators of 5% over their normal work pace or reduced by 88% with an increase of 3.33%. Consequently, the losses due to the uncompleted work or the hiring costs of auxiliary operators can be avoided by demanding a greater effort to workers at certain moments of their workday, but always respecting the limits set by collective agreement.

Suggested Citation

  • Bautista, Joaquín & Alfaro, Rocío & Batalla, Cristina, 2015. "Modeling and solving the mixed-model sequencing problem to improve productivity," International Journal of Production Economics, Elsevier, vol. 161(C), pages 83-95.
    • Handle: RePEc:eee:proeco:v:161:y:2015:i:c:p:83-95
    DOI: 10.1016/j.ijpe.2014.11.018
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    References listed on IDEAS

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    Cited by:

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    2. Sadeghi, Parisa & Rebelo, Rui Diogo & Ferreira, José Soeiro, 2021. "Using variable neighbourhood descent and genetic algorithms for sequencing mixed-model assembly systems in the footwear industry," Operations Research Perspectives, Elsevier, vol. 8(C).
    3. 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.
    4. F. Tanhaie & M. Rabbani & N. Manavizadeh, 2020. "Applying available-to-promise (ATP) concept in mixed-model assembly line sequencing problems in a Make-To-Order (MTO) environment: problem extension, model formulation and Lagrangian relaxation algori," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 320-346, June.

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