A Robust Aggregate Production Planning model for risk-averse managers under uncertainty

It is common to encounter uncertainty in productive environments in which some variables and parameters are stochastic. Inattention to this problem leads to weak production plans that make a lot of disadvantages. Aggregate production planning (APP) is concerned with determining the quantity of production, inventory and work force levels for the intermediate future to satisfy fluctuating demand over a planning horizon. The objective is to minimize costs over the planning period and other objective is to minimize fluctuations in the work force and inventory levels. This paper is concentrated on multi-period, multi-product and multi-machine systems with setup decisions in which some parameters such as production cost and customer demand are uncertain. In this study a Robust aggregate production planning model is developed to minimize the total cost so that it is insensitive against uncertain parameters. In this study the uncertain parameters are estimated via continuous and specific spans. The computational results show that this model is appropriate for risk-averse managers.