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

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 applica...

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Vydáno v:Optimization Ročník 69; číslo 1; s. 63 - 90
Hlavní autoři: Khalilpourazari, Soheyl, Mirzazadeh, Abolfazl, Weber, Gerhard-Wilhelm, Pasandideh, Seyed Hamid Reza
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 02.01.2020
Taylor & Francis LLC
Témata:
Algorithms
Constraint modelling
Decision making
Economic production quantity
imperfect items
multi-objective optimization
Multiple objective analysis
Parameter uncertainty
Pareto optimization
Risk management
robust fuzzy chance constraint programming
Robustness (mathematics)
uncertainty
Warehouses
ISSN:0233-1934, 1029-4945
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Shrnutí: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.
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ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2019.1630625