Surrogate-Assisted Evolutionary Computation for Distributed Simulation-Based Inventory Optimization in Serial Supply Chains

Inventory control is an essential component in supply chain management. Several attempts have been made to optimize the inventory problem using surrogate-assisted optimization methods. However, these studies have assumed that the decision-maker has access to all the supply chain information for building the simulation model. This study proposes a surrogate-assisted evolutionary algorithm for distributed simulation-based inventory optimization in serial supply chains. We propose a surrogate-assisted multi-strategy differential evolution algorithm. Two search strategies are used to generate candidate solutions, and two surrogate models filter the candidate solutions. A regression model is used to estimate the total cost, and a classification model is trained to estimate the feasibility of the solutions. Then, we develop a distributed simulation method for estimating each stage's performance for a given inventory policy while allowing the simulation models to be located in different locations. Our proposed approach is evaluated using 10-stage serial supply chain problems. The results show that the proposed surrogate-assisted distributed simulation-based optimization method can obtain significantly better solutions than the baseline algorithms. In addition, the distributed simulation method can accurately estimate the supply chain's total cost and service level.