Optimal lot-sizing under learning effect in inspection errors with different types of imperfect quality items

This paper develops four economic order quantity models with different types of imperfect quality items including salvage, repairable, scrap, and reject items. The fraction of imperfect items is assumed to be a random variable. In order to recognize these items, system conducts a full inspection process involving type I and II errors. Recently, many researchers have dealt with human factors in the context of lot-sizing problems due to close them to real manufacturing situations. The inspector's error has considered in many previous studies as a detrimental human factor while ignoring the ability of the inspector to reduce the inspection errors through learning as a constructive human factor. As a result, we also consider learning in inspection errors in our models. We determine the optimal policy for each case separately in order to maximize the total profit. A numerical example is presented to study the impact of learning in inspection errors. Moreover, we investigate the sensitivity of proposed models with respect to the major parameters. Results indicate that learning in inspection error has a significant effect on the profitability. Therefore, it should be regarded to avoid the serious underestimation of profit.

Automated summary

This paper develops four economic order quantity models for different types of imperfect quality items and considers the impact of inspector's ability to reduce inspection errors through learning on profitability. Results from numerical examples and sensitivity analysis show that learning in inspection errors has a significant effect on profitability.