Bayesian Optimization Algorithm with Agent-based Supply Chain Simulator for Multi-echelon Inventory Management

Abstract

Supply chain inventory optimization is essential to ensure supply chain efficiency and to increase customer satisfaction. However, it is challenging because of the inherent uncertainties and complex dynamics in real-world supply chains. Researchers and practitioners have turned to simulation-based optimization methods to solve analytically intractable multi-echelon inventory optimization problems. Whereas, simulation-based optimization methods are usually computationally expensive. An efficient optimization procedure will greatly enhance the applicability of these methods. In this paper, we propose a Bayesian optimization approach along with an agent-based supply chain simulator to solve a constrained multi-echelon inventory optimization problem that requires fewer number of interactions with the simulator. Our proposed approach is compared with the most popularly used algorithm, genetic algorithm (GA). The experimental results demonstrate that the proposed method converges to the optimal solution significantly faster than GA.