Volume 53, Number 5, November-December 2019
Operations Research Applications in Industry
|1489 - 1512
|08 October 2019
A new robust possibilistic programming model for reliable supply chain network design: A case study of lead-acid battery supply chain
Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
2 Department of Industrial Engineering, College of Engineering, Tarbiat Modares University, Tehran, Iran
3 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
* Corresponding author: email@example.com
Accepted: 4 September 2018
Nowadays, the importance of caring about tremendous undesirable economical and technological effects of disruptions has impelled many researchers to design reliable supply chain networks. Moreover, the issue of intrinsic imprecision of input parameters should be gingerly regarded in the design of supply chain networks because it could have inverse impact on the quality of long-term planning decisions. Consequently, to handle the noted problems, in this paper, a reliable closed-loop supply chain network is formulated in which a new reliability method is introduced. The proposed formulation can effectively enable the design of a reliable network under different kinds of disruptions besides seeking for minimum overall costs of network design. On the one hand, a new effectual robust possibilistic programming (RPP) model is developed to confront with business-as-usual uncertainty in input parameters. Lastly, a real industrial case study is employed to validate the utility and practicability of the rendered model as well as presenting the efficiency and felicity of the developed RPP model.
Mathematics Subject Classification: 90c70
Key words: Robust possibilistic programming / reliability / supply chain network design / closed-loop supply chain
© EDP Sciences, ROADEF, SMAI 2019
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.