Impact of random defective rate on lot size focusing work-in-process inventory in manufacturing system

Chang Wook Kang, Misbah Ullah, Biswajit Sarkar, Iftikhar Hussain, Rehman Akhtar

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


Literature has focused inventory models with intensive emphasis on imperfect production processes in recent past. However, the work-in-process-based inventory models have been ignored, relatively, in general and the impact of random defects in the form of reworkable and non-reworkable defect rate on lot size and total cost function in particular. This paper develops mathematical models for work-in-process-based inventory by incorporating the effect of random defects rate on lot size and expected total cost function. Our proposed models assume that defective products produced during the production process follow random distributions. Defective products, either in the form of reworkable or rejected production units, follow four types of distribution density functions: uniform, triangular, double triangular and beta distribution. Mathematical models are derived for optimum lot size based on minimization of expected total cost function through the analytical optimization approach. Numerical examples and detailed sensitivity analysis are carried to illustrate and compare the proposed models at different levels of distribution functions’ parameters.

Original languageEnglish
Pages (from-to)1748-1766
Number of pages19
JournalInternational Journal of Production Research
Issue number6
Publication statusPublished - 2017 Mar 19

Bibliographical note

Publisher Copyright:
© 2016 Informa UK Limited, trading as Taylor & Francis Group.

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Impact of random defective rate on lot size focusing work-in-process inventory in manufacturing system'. Together they form a unique fingerprint.

Cite this