Simulation-based Ga Optimization for Production Planning

Effective production planning requires models that are capable of accounting for the complexity and uncertainty intrinsic to manufacturing systems. While the identification of a globally optimal plan is desirable, a more important requirement is the ability of a model to produce production plans that are sufficiently realistic to be implemented in practice and are robust to perturbations in the system. Here, we present a simulation-based optimization approach that employs discrete event simulation and a genetic algorithm as a methodology to support decision making in the area of production planning. The model aims to minimize the sum of expected backorders and inventory costs, while incorporating system constraints and the uncertainty that derives from variations of manufacturing lead times, occurrence of work centre failures and repair service times. Preliminary results for a real-world problem indicate that the model is capable of producing feasible production plans that correctly account for the uncertainty intrinsic to the underlying manufacturing system.