TARU PUBLICATIONS
Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
Powered by:Powered by

The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Statistics and Management Systems
Open Access Research Article

Optimization of multi-echelon reverse supply chain network using genetic algorithm

, *

* Corresponding author · click or hover a name for details

pp. 1353–1364Vol. 26Issue 6September 2023DOI: 10.47974/JSMS-1072XML
Received:
03 Feb 2023
Published Online:
12 Sep 2023
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1072
Pages:
1353–1364

Abstract

Remanufacturing products has been more popular in recent years among businesses as a result of environmental concerns, and the close loop supply chain (CLSC) network has been used to optimise the reverse logistic system. The objective of the current study is to identify the ideal CLSC network, which consists of several producers, remanufacturers, intermediary centres, and customer centres. The consideration of a multi-product, multi-echelon, closed loop supply chain network (CLSC) model for returns products, in which choices about the procurement of materials and their production, distribution, recycling, and disposal play a significant part, is necessary to meet the work’s objectives. In order to resolve the issue more cheaply, a mixed-integer linear programming (MILP) approach is used. Genetic algorithm (GA) is applied as a solution approach in this.

Keywords

OptimizationSCMReverse logisticGenetic algorithmEchelon

Subject Classifications

90B06 Logistics90C11 Mixed integer programming

Acknowledgements

DG 2407

References

[1] Amin, S. H., & Zhang, G. A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), pp. 4165-4176, (2013).
[2] Anand, R., Aggarwal, D., & Kumar, V. A comparative analysis of optimization solvers. Journal of Statistics and Management Systems, 20(4), pp. 623-635, (2017). 
[3] Cui, Y. Y., Guan, Z., Saif, U., Zhang, L., Zhang, F., & Mirza, J. Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach. Journal of Cleaner Production, 162, pp.717-742, (2017).
[4] Das, D., & Dutta, P. Maximizing profit and responsiveness of a closed-loop supply chain in presence of product exchange offer. Journal of Statistics and Management Systems, 22(3), pp.495-534, (2019).
[5] Diabat, A., Kannan, D., Kaliyan, M., & Svetinovic, D. An optimization model for product returns using genetic algorithms and artificial immune system. Resources, conservation, and recycling, 74, pp.156-169, (2013).
[6] Goli, A., & Golmohammadi, A. M. Multi-objective optimization of location and distribution in a closed-loop supply chain by considering market share in competitive conditions. International Journal of Supply and Operations Management, pp.1-13, (2022).
[7] Khan, S. H., Chouhan, V. K. A multi-objective optimization of closed-loop supply chain problem with vehicle routing. Global Journal of Engineering and Technology Advances, 6(2), pp.121-130, (2021).
[8] Ozdogru, U. Impact of exponential technologies on global supply chain management. Technology in Supply Chain Management and Logistics: Current Practice and Future Applications, pp. 37-55, (2019). 
[9] Paduloh, P., Djatna, T., Sukardi, S., & Muslich, M. Uncertainty models in reverse supply chain: A review. Int. J. Supply Chain Management, 9(2), pp.139-149, (2020).
[10] Rasi, R. E., & Sohanian, M. A multi-objective optimization model for sustainable supply chain network with using genetic algorithm. Journal of Modelling in Management, pp,1-14, (2020).
[11] Safaei, S., Ghasemi, P., Goodarzian, F. Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm. Environmental Science and Pollution Research, pp.1-15, (2022). 
[12] Xin, L., Xu, P., & Manyi, G. Logistics distribution route optimization based on genetic algorithm. Computational Intelligence and Neuroscience, pp.1-9, (2022).

Views: 249Downloads: 2Citations: 0