Supply chain management through dynamic model parameters optimization

Supply chain management based on a discrete event-driven model is proposed. The model contemplates each supply chain entity as an agent whose activity is described by a collection of states and transitions. This activity is characterized by a set of parameters whose values can be optimized to achieve a better system performance. Genetic algorithms are incorporated as a useful tool to optimize large and complex problems, even subjected to uncertainty, through an oriented search, thus avoiding the exhaustive inspection of all the possible solutions. Results obtained have demonstrated that the proposed methodology has an appreciable practical importance as it may lead to substantial economic improvements. The way in which the proposed approach works is illustrated using several examples.

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