Simulation-optimization for business decision support in a global specialty chemicals enterprise

Abstract

To tap on the vast business opportunities offered by globalization, companies are increasingly shifting from single-site manufacturing to multi-site enterprise operation. This results in a more complex supply chain structure, involving numerous entities in different locations with intricate dynamics. The optimization of such systems is not amenable to mathematical programming approaches. In this paper, we propose a simulation-optimization framework for business decision support in a global specialty chemicals enterprise. A dynamic simulation model is used to capture the behavior of the entities, their interactions, the various uncertainties, and the resulting dynamics. Optimization is done by coupling simulation with a non-dominated sorting genetic algorithm, implemented in a parallel computing environment for computational efficiency. The application of the proposed approach for business decision support are demonstrated in two case studies. © 2010 Elsevier B.V.