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.

The productivity of any manufacturing organization depends on the availability of raw materials and other component parts in the proper quantity, quality, price range and time (Chitriki and Ravindranath, 2010). In the organization, instead of supplying inventories neck to neck, effective control over inventories helps management in purchasing efficiently. The productivity and profitability of an organization can increase drastically with effective control on inventories (Luca et al., 2002). The blocked revenue can be decreased to achieve higher Return on Investment (ROI).

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 (Radhakrishnan et al., 2009). 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 (Arumugam and Prahlada Rao, 2010). The optimized ordering quantity and reorder points are obtained with the aid of a Genetic Algorithm (GA) (Radhakrishnan et al., 2009). GA is an optimization method based on genetic concepts. It is an adaptive search technique which simulates an evolutionary process, and uses parameters such as population generation, fitness, crossover and mutation, and generates the optimum chromosome which represents the optimized reorder points. The ordering quantity is determined by the associated demand matrix which will be calculated by the actual existing demand.

Supply Chain Management Journal, Supply Chain Tools, Genetic Algorithm (GA), Comparison, Particle Swarm Optimization, Simulated Annealing, Optimization Algorithms.