Model Of Production-inventory System Research Articles

Neural network for solving disturbance optimal control model for production inventory system with stochastic deterioration in two-level supply chain

Neural network for solving disturbance optimal control model for production inventory system with stochastic deterioration in two-level supply chain

A mathematical model on eco-friendly manufacturing system under probabilistic demand

The article deals with a mathematical model of production inventory system of green products in a green manufacturing industry. The main objective of this proposed model is to formulate a profit function for service level and random variable dependent demand implementing green technology in the manufacturing industry for reduction of green house gas emission. The production lotsize is considered here as an increasing function of green technology and capital invested for setup the manufacturing system which meets the market demand. As a result, green technology, capital invested for setup and service level are decision variable which are optimized to achieve maximum profit. Finally, numerical example for normal distribution and distribution free cases are illustrated to justify the proposed model.

Optimization of reliability based model for production inventory system

Traditional research studies on the Economic Production Quantity (EPQ) model propose that produced items have perfect quality. However, in real production systems the quality of the products depends on the production process reliability. EPQ models that consider reliability and the effect of imperfect items are much more complex, and in turn the objective function becomes much more complicated. It is challenging to solve this type of model analytically, and it is also time consuming. Hence, it is necessary to utilize non-traditional solution techniques, such as numerical methods and heuristic search algorithms, for solving this type of model. In this paper, the optimal solution of the EPQ based reliability model are obtained by analytical solution, a Generalized Reduced Gradient (GRG) algorithm, an Evolutionary Algorithm (EA), a Monte Carlo non-deterministic method and the LINGO™ commercial solver. The methodology of this work has been clearly presented, and the computational results are compared and discussed. The computational results show that the GRG, EA, and Monte Carlo methods result in feasible and similar solutions, while the analytical solution is not valid for the model studied. The model can be extended to solve more complicated inventory models considering rework, shortage and multiple products.