Evolutionary Adaptation of (r, Q) Inventory Management Policy in Complex Distribution Systems

Abstract

The paper addresses the inventory control problem in logistic networks with complex, mesh-type interconnection structure. Contrary to the majority of previously analyzed models, the considered topology does not assume any simplifications nor restrictions in the way the nodes are linked with each other. The system encompasses two types of actors – retailers and suppliers – connected via unidirectional links with non-negligible transshipment delay. The uncertain external demand may be imposed on any retailer and backordering is not allowed. The resource distribution is governed using the classical (r, Q) inventory management policy implemented in a distributed way. In this work, the continuous genetic algorithm is applied for automatic selection of reorder point r and shipment quantity Q. The optimization process aims to provide a trade-off between the economic costs and customer satisfaction. Numerous simulations are performed to evaluate the effectiveness of genetic algorithm performance in the considered class of problems.