A critical decision companies are faced with on a regular basis is the ordering of products and/or raw materials. Poor decisions can lead to excess inventories that are costly or to insufficient inventory that cannot meet its customer demands. These decisions may be as simple as "How much to order" or "How often to order" to more complex decision forecasting models. This paper addresses optimizing these sourcing decisions within a supply chain to determine robust solutions. Utilizing an existing supply chain simulator, an optimization methodology that employs genetic algorithms is developed to optimize system parameters. The performance measure that is optimized plays a very important role in the quality of the results. The deficiencies in using traditionally used performance measures in optimization are discussed and a new multi-objective GA methodology is developed to overcome these limitations.
['Jeffrey A. Joines', 'Deepak Gupta', 'Mahmut Ali Gökçe', 'Russell E. King', 'M. Kay']
Maufacturing supply chain applications 1: supply chain multi-objective simulation optimization