Development of Supply Chain Tools Using Genetic Algorithm and Comparison with Particle Swarm Optimization and Simulated Annealing Optimization Algorithms

Abstract

Inventories, facilities and transportation are considered to be the important tools of supply chain management. The efficiency of any manufacturing unit can be increased if the above elements are under proper control. In today’s scenario one of the significant fields in supply chain management is inventory management. To effectively manage inventory levels, it is essential to consider the appropriate reorder points as well as the optimized ordering quantity at that reorder point for the inventory items. In this paper, the optimized ordering quantity and reorder points are obtained with the aid of a proposed genetic algorithm. This proposed system considers the raw material-wise holding cost and shortage cost to find the minimized total cost. The ordering quantity and reorder points that minimize the cost function are found by using the demand rate as well as the associated solution demand matrix. Further, the robustness of the proposed technique is compared to that of the other familiar optimization algorithms such as particle swarm optimization and simulated annealing optimization techniques. The results prove that the proposed methodology is more efficient as compared to other optimization techniques.