Multi-item EPQ model with learning effect on imperfect production over fuzzy-random planning horizon

Abstract

Uncertainty is certain in the world of uncertainty. This study revisits an economic production quantity (EPQ) model with shortages for stock-dependent demand of the items with reworking and disposing of the imperfect ones over a random planning horizon under the joint effect of inflation and time value of money, where the expected time length is imprecise in nature. Transmission of learning effect has been incorporated to reduce the defective production. The total expected profit over the random planning horizon is maximized subject to the imprecise space constraint. The possibility, necessity and credibility measures have been introduced to defuzzify the model. The simulation-based genetic algorithm is used to make decision for the above EPQ model in different measures of uncertainty. The model is illustrated through an example. Sensitivity analysis shows the impacts of different parameters on the objective function in the model.