Accelerating Benders decomposition approach for robust aggregate production planning of products with a very limited expiration date

The price of products with a very limited expiration date reduces dramatically after a certain period, say a season. Thus, overproduction or deficiency of such products will end in loss of profit. This study determines aggregate production planning (APP) of products with a very limited expiration date, such as seasonal clothing, New Year gifts, yearbooks and calendars using postponement policy with uncertain conditions. In order to apply the concept of postponement for these products, three types of production activities including direct production, semi-finished production and final assembly are taken into account. Additionally, a robust optimization model is expanded to deal with the inherent uncertainty of the model parameters. Moreover, since the proposed problem is NP-hard, a Benders decomposition algorithm is developed by using two efficient acceleration inequalities to tackle large-scale computational complexity. Finally, a set of real data from a calendar producing company in Tehran called “NIK Calendar” are used to validate the model and show the efficiency as well as convergence of the developed Benders decomposition algorithm. The computational results clearly show efficiency and effectiveness of the devised algorithm.