Proceedings of the 2011 Winter Simulation Conference S. Jain, R.R. Creasey, J. Himmelspach, K.P. White, and M. Fu, eds. A MULTICRITERIA SIMULATION OPTIMIZATION METHOD FOR INJECTION MOLDING María G. Villarreal-Marroquín Mauricio Cabrera-Ríos José M. Castro The Ohio State University University of Puerto Rico at Mayagüez 1971 Neil Ave., Room 210 PO Box 9000 Columbus, OH 43210, USA Mayagüez, PR 00681, USA ABSTRACT Injection Molding is one of the most important processes for mass-producing plastic products. To help improve and facilitate the molding of plastic parts, advanced computer simulation tools have been devel- oped. While modeling is complicated by itself, the difficulty of optimizing the injection molding process is that its performance measures usually show conflicting behaviors. Therefore, the best solution for one performance measure is usually not the best for other performance measures. This paper introduces a simulation optimization method that considers multiple performance measures and is able to find a set of efficient solutions without having to evaluate a large number of simulations. The main components of the method are metamodeling and design of experiments. The method is illustrated and detailed here using a simple test example. Furthermore, it is applied to a real injection molding case. The performance of the method using different design of experiments is also discussed. 1 INTRODUCTION Polymers have been increasingly replacing metallic components in many applications such as the manu- facture of automobiles, aircrafts, toys, appliances, office equipment, among others. This is because they are very versatile materials. Nowadays, many consumer products such as computer and automobile com- ponents rely on the technology and production of polymer companies. Thus, it is important to design reli- able processes to ensure low cost and high quality products. In Injection Molding (IM), for instance, processing conditions such as melt temperature, mold tem- perature, pack/hold pressure and duration, and cooling time have to be properly set to ensure the quality of the molded components. Often, these conditions are set by process engineers based on prior experi- ence, resin supplier’s recommendations, and/or reference handbooks. These conditions are usually further adjusted by trial and error on the shop floor. This approach is highly dependent on the experience of molding operators and can be costly and time consuming, especially with new resins and/or new applica- tions (Zhou and Turng 2007). However, with recent advances in numerical modeling and computer simu- lation techniques, a large effort has been made in developing computer simulation tools to help improve and facilitate the modeling of plastic parts. The use of simulation for selecting injection molding processing conditions has been the subject of much research in the past (Smith, Tortorelli, and Tucker 1998; Alam and Kamal 2005). Specialists usual- ly generate a limited number of solutions from which one is finally selected. Nevertheless, this does not guarantee having found the optimal solution. Therefore, there is a lot of potential to be exploited in the adequate and efficient selection of optimization techniques for the design of manufacturing processes through computer simulations. Such potential explains the relatively recent and rapidly increasing interest in Simulation Optimization (SO) or Optimization via Simulation (OvS) as a field on its own. The objec- tive of a SO method is to provide a structure to determine the values of the controllable variables that op- 2395 978-1-4577-2109-0/11/$26.00 ©2011 IEEE