Using robust approach concept to solve the production planning problem in manufacturing systems

. The use of Linear Programming (LP) models to plan production has been widely used to provide optimal solutions. However, LP models as well as models based on Material Requirements Planning (MRP) use deterministic parameters. In this context, one of the classic approaches to deal with a dynamic and uncertain scenario is the robust approach, which proposes a suboptimal solution through deterministic approaches capable of incorporating variations in the problem solutions. A parameter that is subject to such variation is the system's production capacity, since this parameter is directly impacted by the way the system is workloaded and its cycle times. Thus, to analyze the relationship among system’s production capacity, cycle time and work-in-process, an alternative is modeling the production system as a queuing system using little’s law. This research aims to solve the production planning problem modeled as a queuing system to propose managers a production planning model that uses efficient, simple and robust methods. Therefore, a theorem was proposed to prove the effectiveness of the used method. As a result, the mathematical model obtained allows the planner to make the use of a robust linear programming model, with low computational cost, capable of obtaining good quality solutions when compared to complex nonlinear programming problems. Finally, the model was submitted to a numerical experiment to better illustrate how it works with the inputs from an electronic component manufacturing company.