Development of a fuzzy economic order quantity model for imperfect quality items using the learning effect on fuzzy parameters

Abstract

This paper develops an inventory model for items with imperfect quality in a fuzzy environment by assuming that learning occurs in setting the fuzzy parameters. This implies that inventory planners collect information about the inventory system and build up knowledge from previous shipments, and thus learning process occurs in estimating the fuzzy parameters. So, it is hypothesized that the fuzziness associated with all fuzzy inventory parameters is reduced with the help of the knowledge acquired by the inventory planners. In doing so, the study developed a total profit function with fuzzy parameter, where triangular fuzzy number is used to quantify the fuzziness of the parameters. Next, the learning curve is incorporated into the fuzzy model to account for the learning in fuzziness. Subsequently, the optimal policy, including the batch size and the total profit are derived using the classical approach. Finally, numerical examples and a comparison among the fuzzy learning, fuzzy and crisp cases are provided to highlight the importance of using learning in fuzzy model.