A robust fuzzy approach for constrained multi-product economic production quantity with imperfect items and rework process

Khalilpourazari S., Mirzazadeh A., Weber G., Pasandideh S. H. R.

OPTIMIZATION, vol.69, no.1, pp.63-90, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 69 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.1080/02331934.2019.1630625
  • Journal Name: OPTIMIZATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, Computer & Applied Sciences, MathSciNet, zbMATH
  • Page Numbers: pp.63-90
  • Keywords: Economic production quantity, imperfect items, uncertainty, multi-objective optimization, robust fuzzy chance constraint programming, STAGE MANUFACTURING SYSTEM, WATER CYCLE ALGORITHM, RANDOM DEFECTIVE RATE, GREY WOLF OPTIMIZER, MULTIOBJECTIVE OPTIMIZATION, EPQ MODEL, EOQ MODEL, ORDER QUANTITY, INVENTORY MODEL, BATCH SIZE
  • Middle East Technical University Affiliated: No


This paper aims to develop a robust multi-item EPQ formulation considering rework process and imperfect items. The mathematical model consists of two objective functions targeting minimization of total inventory costs and the total required warehouse space, respectively. Since, in real-world applications, parameters of the mathematical formulation are due to uncertainty, in this paper, Basic Chance Constraint Programming and Robust Fuzzy Chance Constraint Programming models are developed to handle uncertainties in both objective function and constraints. By solving examples, the superiority of the RFCCP over the BCCP model is showed and its ability to provide risk-averse solutions is ensured. Due to the nonlinearity of the RFCCP model and to provide the decision maker with the Pareto front, two algorithms, namely, Multi-Objective Grey Wolf Optimizer and Multi-Objective Water Cycle Algorithm, are used to optimize the decision variables. The performance of the algorithms is evaluated within different large-size test problems, using diversity, Spacing, number of non-dominated solutions and CPU-time. In the end, to investigate significant differences and to determine the best algorithm ANOVA test is implemented. In the end, we also offer new research directions, e.g. in terms of further ways of uncertainty modelling and risk management.