Robust Aggregate Production Planning for Risk-Averse Managers in Uncertainty Conditions

It is usual for a production environment to encounter uncertainty and variable data that causes generating random parameters. Failure to pay attention to these changes will make the scheduling not adequately match the reality and cause many losses in production environments. Considering the importance of the issue, in this article we use the Robust Optimization Approach to deal with uncertainty in the aggregate production planning parameters. In this paper, in a robust model, it is assumed that the uncertainty of non-deterministic parameters is continuous and a completely new and innovative approach is proposed for Robust Optimization for risk-averse managers and then, an optimization strategy is used to examine the uncertainty. In order to investigate the model results, examples have been made in small and large sizes and the problem has been solved and analyzed using the GAMS software and Lagrange relaxation method. The results of the implementation of the proposed robust models in this paper, compared to the basic model, show that the results have more stability against uncertainty and this causes a significant reduction in risk.