1- Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Iran , mokhtari_ie@yahoo.com
2- 2. Associate professor, Department of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, Iran.
3- MSc student, Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Abstract: (3035 Views)
One of the basic assumptions of classical production-inventory models is that all products are of perfect quality. However, in real manufacturing situations, the production of defective items is inevitable, and a fraction of the items produced may be naturally imperfect. In fact, items may be damaged due to production and/or transportation conditions in the manufacturing process. On the other hand, some reworkable items exist among imperfect items that can be made perfect by additional processing. In addition, the classical production-inventory models assume that there is only one product in the system and that there is an unlimited amount of resources. However, in many practical situations, several products are produced and there are some constraints related to various factors such as machine capacity, storage space, available budget, number of allowable setups, etc. Therefore, we propose new constrained production-inventory models for multiple products where the manufacturing process is defective and produces a fraction of imperfect items. A percentage of defective items can be reworked, and these products go through the rework process to become perfect and return to the consumption cycle. The goal is to determine economic production quantities to minimize the total cost of the system. The analytical solutions are each derived separately by Lagrangian relaxation method, and a numerical example is presented to illustrate and discuss the procedure. A sensitivity analysis is performed to investigate how the variation in the inputs of the models affects the total cost of the inventory system. Finally, some research directions for future works are discussed.
Type of Study:
Research |
Subject:
Manufacturing Process & Systems Received: 2019/09/1 | Accepted: 2021/06/19 | Published: 2021/06/19