A Multi-objective Optimization for Supply Chain Network using Intelligent Water Drop

摘要

In this paper, the supply chain is modeled by a network of stages, which may have one or more options for being fulfilled, where each option is characterized by its time and cost. The objective is to determine the option for every stage that minimizes the Cost of Goods Sold and the Lead Time simultaneously for a set of products represented by a Generic Bill of Materials. In order to achieve it, this work proposes using a new meta-heuristic called Intelligent Water Drop, which mimics the process that takes place between water drops in a river and the changes that happen in the river bed. Artificial water drops minimize the cost and the lead time for every order using the concept of Pareto Optimal Set that, instead of obtaining a single solution, determines a set of optimal solutions by using the proposed equations to compute the velocity and time of the artificial drops. A widely used notebook supply chain in literature is solved and we provide some performance metrics of the algorithm. We compare the results of the proposed algorithm with the results computed by other meta-heuristics.