The Combination of Discrete-Event Simulation and Genetic Algorithm for Solving the Stochastic Multi-Product Inventory Optimization Problem

• Main Authors: Jackson Ilya, Tolujevs Jurijs, Reggelin Tobias
• Format: Article
• Language: English
• Published: Sciendo 2018-09-01
• Series: Transport and Telecommunication
• Subjects: stochastic inventory optimization; simulation-based optimization; simheuristics; smart solutions; non-binary chromosome encoding
• Online Access: https://doi.org/10.2478/ttj-2018-0020

The paper describes an eventual combination of discrete-event simulation and genetic algorithm to define the optimal inventory policy in stochastic multi-product inventory systems. The discrete-event model under consideration corresponds to the just-in-time inventory control system with a flexible reorder point. The system operates under stochastic demand and replenishment lead time. The utilized genetic algorithm is distinguished for a non-binary chromosome encoding, uniform crossover and two mutation operators. The paper contains a detailed description of the optimization technique and the numerical example of six- product inventory model. The proposed approach contributes to the field of industrial engineering by providing a simple, but still efficient way to compute nearly-optimal inventory parameters with regard to risk and reliability policy. Besides, the method may be applied in automated ordering systems.