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An efficiency-based aggregate production planning model for multi-line manufacturing systems

Author

Listed:
  • S. Ali Naji Nasrabadi Yazd

    (Ferdowsi University of Mashhad)

  • Amirhossein Salamirad

    (University of British Columbia)

  • Siamak Kheybari

    (University of Cambridge)

  • Alessio Ishizaka

    (NEOMA Business School)

Abstract

Aggregate production planning (APP) is a medium-term planning in the production system, which determines the optimal production plan in the planning horizon. To allocate the optimal production quantity to the production lines, we propose an efficiency-based APP to multi-line manufacturing systems. For that purpose, first, considering the line efficiency factors, we calculate the efficiency score of production lines with an extension of data envelopment analysis (namely DEA-AR). Pollution rate, defective product rate, production capacity, downtime, and electricity consumption are the criteria employed to calculate the efficiency of production lines. Then, using the result of DEA as a parameter, we develop a bi-objectives integer mathematical model that allocates the most production to efficient lines while minimizing total production costs considering loading constraints. To solve the proposed model, the ℇ-constraint method is employed. We evaluate the performance of the multi-line APP using a set of data collected from a plastic production factory. Results indicate that in using the proposed model, both efficiency and production costs are appropriately satisfied in the efficiency-based APP. The proposed framework is generic and provides the managers of different manufacturing organizations with a powerful tool to deal with medium-term planning by taking the line efficiency into account.

Suggested Citation

  • S. Ali Naji Nasrabadi Yazd & Amirhossein Salamirad & Siamak Kheybari & Alessio Ishizaka, 2023. "An efficiency-based aggregate production planning model for multi-line manufacturing systems," Operations Management Research, Springer, vol. 16(4), pages 2008-2024, December.
  • Handle: RePEc:spr:opmare:v:16:y:2023:i:4:d:10.1007_s12063-023-00381-0
    DOI: 10.1007/s12063-023-00381-0
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