dc.description.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. |
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