Benefit of preservation technology with promotion and time-dependent deterioration under fuzzy learning

This paper studies three continuous review economic order quantity models for time-dependent deterioration using preservation technology. First, a crisp model is developed and the model is extended into a fuzzy model to include the imprecise nature of demand. It is further extended to analyze the impact of the learning effect under the fuzzy environment. All models are developed for a finite time horizon, incorporating promotional effort and full backorder. The optimal solutions are derived for the number of orders, preservation technology cost, and the fraction of a cycle with positive stock. Three algorithms are developed to find the optimal solution for three models. Numerical analysis is performed to demonstrate the application, followed by a sensitivity analysis of the important parameters. The crisp model leads to the lowest total cost followed by fuzzy learning and fuzzy model. Even though the optimal number of orders is found to be the same for the three models, order quantity is more for the fuzzy model and less for the crisp model. The order quantity increases step-wise with an increase in preservation factor.