<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Institute of Logistics and Material Handling Systems

Transport and Telecommunication Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The paper describes an eventual combination of discrete-event simulation and genetic algorithm to define the optimal inventory policy in stochastic...

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

Published Online: Jun 26, 2018

Page range: 233 - 243

DOI: https://doi.org/10.2478/ttj-2018-0020

Keywords
stochastic inventory optimization, simulation-based optimization, simheuristics, smart solutions, non-binary chromosome encoding

© 2018 Transport and Telecommunication Institute, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Jun 26, 2018

Page range: 233 - 243

DOI: https://doi.org/10.2478/ttj-2018-0020

Keywordsstochastic inventory optimization, simulation-based optimization, simheuristics, smart solutions, non-binary chromosome encoding

© 2018 Transport and Telecommunication Institute, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
stochastic inventory optimization, simulation-based optimization, simheuristics, smart solutions, non-binary chromosome encoding