Multi-echelon Supply Chain Inventory Planning using Simulation-Optimization with Data Resampling

Modeling and optimization of multi-echelon supply chain systems is challenging as it requires a holistic approach that exploits synergies and interactions between echelons while accurately accounting for variability observed by these systems. We develop a simulation-optimization framework that minimizes average inventory while maintaining desired average $\beta$ service level at stocking locations. We use a discrete-event simulation framework to accurately capture system interactions. Instead of a parametric estimation approach, the demand and the lead time variability are quantified by bootstrap sampling the historical data, thus preserving the true nature of the variability. We compare three different open source simulation-optimization libraries - the derivative free methods from SciPy.Optimize, a Bayesian optimization algorithm Scikit-Optimize, and a radial basis function based black-box optimizer RBFOpt. The experiments demonstrate practical applicability of our approach. While we observe substantially lower inventory levels and computationally superior results from RBFOpt, depending on the problem, search strategy, and the random start states, close enough good solution can be obtained from both RBFOpt and Scikit-Optimize. The optimization results demonstrate a preference for a centralized inventory planning scheme that help with risk pooling. Moreover, with no order placement cost, the optimal solution tends to order more frequently in order to lower inventory.