An Immune Genetic Algorithm for Multi-Echelon Inventory Cost Control of IOT Based Supply Chains

Abstract

Internet of Things (IOT) is being widely used especially in industry sectors. The IOT techniques provide more information for the inventory control. With the increased fierce competition in market economy, the supply chain is at the core of a successful enterprise. In today's context, it is an inevitable trend to optimize the inventory cost of supply chains. Separating all aspects of the supply chain impedes controlling inventory costs of the whole system with traditional approaches. Therefore, in this paper, we consider supply chains consisting of multiple suppliers, a manufacturer, and multiple distributors. The time cost of delayed transportation is integrated into previous studies to construct a new model, which is solved with an immune genetic algorithm. Unlike the genetic algorithm, the memory function and adjustment function of the immune algorithm are included in this algorithm. Different from the immune algorithm, genetic operators of the genetic algorithm are included. The immune genetic algorithm effectively overcomes the disadvantages of the genetic algorithm, improving global search ability and search efficiency. The validity and rationality of the optimized model are assessed in comparison with the previous results.

Keywords

• Internet of Things
• Multi-echelon inventory
• delayed transportation
• time cost
• immune genetic algorithm