Solving a Supply Chain Management Problem to Near Optimality Using Ant Colony Optimization, in an International Context

The importance of achieving optimality or near optimality in supply routing is on the rise as globalization leads to scenarios in which multiple, heterogeneous and highly spatially distributed demands have to be satisfied under stringent constraints. However, there is no consensus concerning what constitutes an all-encompassing objective function for the supply planner, who faces what can easily constitute a problem requiring Non-deterministic Polynomial time for determining the solution even in its simplest formulations. The work presented in this article proposes a mathematically grounded approach that uses Ant Colony Optimisation to yield near optimal results across a large set of problem formulations and objective functions. The latter are designed to capture real-world goals such as cost reduction, optimal transportation management, flexibility and minimal lead-time. This study adds a new dimension to topics traditionally encountered in the literature, namely that of the cultural differences between partners engaged in international trade relations. Furthermore, the impact of the lag between determining and implementing the quasi-optimal strategy is forecast for an array of objective functions tailored to represent approaches encountered in international companies dealing with supply challenges in fields such as Information Technology. Finally, the framework thus established is employed to analyse the indirect relationship between Asian “white box” suppliers and a Romanian firm operating in Mobile Integrated Device space.