Converging linear particle swarm optimization and intelligent genetic algorithm for a simple multi-echelon distribution and multi-product inventory control in a supply chain model

Inventory management in supply chain networks involves keeping track of hundreds of items spread across multiple locations with complex interrelationships between them. However, it is not computationally reasonable and feasible to consider each item individually during the decision making process. This research optimizes the various decision variables of the supply chain by considering the cost components such as ordering material cost and processing costs for various products at all plants, distribution costs from each plant to all distributors and from each distributor to all retailers inventory carrying costs at all plants, distributors and retailers for the simple multi-echelon supply chain model (MESCM) using two evolutionary computations (ECs), intelligent genetic algorithm (IGA) embedded to Genetic Algorithm for Numerical Optimization with Linear Constraints II (GENOCOP II) and converging linear particles swarm optimization (CLPSO). This simple MESCM is constructed as a large scale linear programming problem. Finally, a simplified rear-word industry case of multi-echelon distribution and multi-product inventory control problem in this simple MESCM are solved using the existing software package, LINGO, and the two proposed ECs in order to validate their effectiveness and determine the optimal inventory solutions for and comparison. The performance of these two ECs which one can adequately obtain the optimal solutions and their computing accuracy is the major works of this research.