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

S. Ali Naji Nasrabadi Yazd (), Amirhossein Salamirad (), Siamak Kheybari () and Alessio Ishizaka ()
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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

Operations Management Research, 2023, vol. 16, issue 4, No 19, 2008-2024

Abstract: 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.

Keywords: Aggregate production planning (APP); Line efficiency; Efficiency-based APP; Ɛ-constraint; Data envelopment analysis with assurance region (DEA-AR) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12063-023-00381-0

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