EPQ Model with Learning Effect For Imperfect Quality Items Under Trade-Credit Financing

  • Mahesh Kumar Jayaswal Department of Mathematics and Statistics, Banasthali Vidyapith, Banasthali, Rajasthan
  • Mandeep Mittal Department of Applied Science, Amity Institute of Applied Sciences, Amity University, Noida, U.P., India
  • Isha Sangal
  • Rita Yadav Department of Mathematics and Statistics, Banasthali Vidyapith, Banasthali, Rajasthan

Abstract

Although high and advanced technologies are used by the industry to produce high-quality items, few defective items have been produced due to some errors in
the technical operation or in maintenance. The defective cost is the expense involving rework, repair, and replacement of defective items and the cost incurred due to loss of goods. The learning function acts as a substantial function for cost diminution. Meanwhile, the impact of learning is an incident that occurs approximately everywhere and enables the workers to carry out new work with better performance after owing repetition over a course of time. Further, a retailer offers buyers an allowable setback time to arrange the money payable to him and no extra fine to a buyer if money is paid within the allowable financing time period. On the other hand, if the cash is not paid on trade-credit financing period of time, the retailer will charge on remaining cash provided by the buyer after the allowed period. Keeping these facts, an inventory model has been developed for imperfect quality items with a learning effect in which demand rate assumes as an exponential function of the trade credit period. The expected total prot function is maximized with respect to trade credit financing period under learning effect. A numerical example is illustrated, and comprehensive sensitivity analysis has also been depicted to understand the robustness of the model.

Published
Feb 11, 2021
How to Cite
JAYASWAL, Mahesh Kumar et al. EPQ Model with Learning Effect For Imperfect Quality Items Under Trade-Credit Financing. Yugoslav Journal of Operations Research, [S.l.], v. 31, n. (2), p. 235-247, feb. 2021. ISSN 2334-6043. Available at: <http://yujor.fon.bg.ac.rs/index.php/yujor/article/view/896>. Date accessed: 23 apr. 2024.
Section
Articles