Optimization of a production inventory model with reliability considerations

Abstract

The classical production inventory control models assume that products are produced by perfectly reliable production process with no defective items. However, in reality, products are not always perfect but are directly affected by the reliability of the production process. While the reliability of the production process cannot be increased without a price, its rejection and inspection cost can be reduced with investment in flexibility and reliability improvement. In this thesis, a production inventory model with reliability of production process consideration is developed which considers the combined effect of production cost, setup cost, holding cost, inspection cost, depreciation cost, rejection cost and backorder cost on total cost minimization. The economic production lot size and the reliability of the production process along with the production period are the decision variables and total cost per cycle is the objective function which is to be minimized. A meta-heuristic Particle Swarm Optimization (PSO) algorithm is used to solve the unconstrained non integer non linear form of objective function as it can generate accurate result with a shorter computational time with stable convergence. Some numerical examples have been presented to explain the model. The results obtained from PSO algorithm is compared with the results of Genetic Algorithm (GA) applying on the same inventory model and found satisfactory.