SPARE PARTS INVENTORY OPTIMIZATION FOR AUTO MOBILE SECTOR

In this paper the objective is to determine the optimal allocation of spares for replacement of defective parts on-board of a usage. The minimization of the total supply chain cost can only be achieved when optimization of the base stock level is carried out at each member of the supply chain. A serious issue in the implementation of the same is that the excess stock level and shortage level is not static for every period. This has been achieved by using some forecasting and optimization techniques. Optimal inventory control is one of the significant tasks in supply chain management. The optimal inventory control methodologies intend to reduce the supply chain cost by controlling the inventory in an effective manner, such that, the SC members will not be affected by surplus as well as shortage of inventory. In this paper, we propose an efficient approach that effectively utilizes the Genetic Algorithm for optimal inventory control. This paper reports a method based on genetic algorithm to optimize inventory in supply chain management. We focus specifically on determining the most probable excess stock level and shortage level required for inventory optimization in the supply chain so that the total supply chain cost is minimized . So, the overall aim of this paper is to find out the healthy stock level by means of that safety stock is maintained throughout the service period. Keywords: genetic algorithm, optimization, Inventor

Stability Analysis Of The Supply Chain By Using Neural Networks And Genetic Algorithms

Effectively managing a supply chain requires visibility to detect unexpected variations in the dynamics of the supply chain environment at an early stage. This paper proposes a methodology that captures the dynamics of the supply chain, predicts and analyzes future behavior modes, and indicates potentials for modifications in the supply chain parameters in order to avoid or mitigate possible oscillatory behaviors. Neural networks are used to capture the dynamics from the system dynamic models and analyze simulation results in order to predict changes before they take place. Optimization techniques based on genetic algorithms are applied to find the best setting of the supply chain parameters that minimize the oscillations. A case study in the electronics manufacturing industry is used to illustrate the methodology. © 2007 IEEE