Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty

Multi-objective optimisation of facility location decisions within integrated forward/reverse... Increasing interest to the environmental, social and economic aspects of the supply chains has motivated supply chain managers to optimise location-allocation decisions within closed-loop logistics networks. This paper presents a multi-objective model to optimise facility location decisions in integrated forward/reverse streams under uncertainty. The objectives of the model are to minimise total costs and simultaneously maximise customer satisfaction considering uncertainties in demand and return rate. The proposed model is solved by integrating genetic algorithm with sampling average method. The application of the model is examined in a real case study of car after sales network. The result of the model is compared to a deterministic model to identify how uncertainties affect the optimal configurations. The other experiment is carried out to study the effect of integrating forward and reverse logistics operations on the stakeholder's objectives. Finally, a post-analysis is applied to help in choosing one solution among many different solutions. Keywords: closed-loop supply chain; CLSC; facility location; stochastic optimisation; genetic algorithm. Reference to this paper should be made as follows: Afshari, H., Sharafi, M., ElMekkawy, T.Y. and Peng, Q. (2016) `Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty', Int. J. Business Performance and Supply http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Business Performance and Supply Chain Modelling Inderscience Publishers

Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty

Download PDF
27 pages

Loading next page...
 
/lp/inderscience-publishers/multi-objective-optimisation-of-facility-location-decisions-within-Y0ogq5gQdd

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1758-9401
eISSN
1758-941X
DOI
10.1504/IJBPSCM.2016.078565
Publisher site
See Article on Publisher Site

Abstract

Increasing interest to the environmental, social and economic aspects of the supply chains has motivated supply chain managers to optimise location-allocation decisions within closed-loop logistics networks. This paper presents a multi-objective model to optimise facility location decisions in integrated forward/reverse streams under uncertainty. The objectives of the model are to minimise total costs and simultaneously maximise customer satisfaction considering uncertainties in demand and return rate. The proposed model is solved by integrating genetic algorithm with sampling average method. The application of the model is examined in a real case study of car after sales network. The result of the model is compared to a deterministic model to identify how uncertainties affect the optimal configurations. The other experiment is carried out to study the effect of integrating forward and reverse logistics operations on the stakeholder's objectives. Finally, a post-analysis is applied to help in choosing one solution among many different solutions. Keywords: closed-loop supply chain; CLSC; facility location; stochastic optimisation; genetic algorithm. Reference to this paper should be made as follows: Afshari, H., Sharafi, M., ElMekkawy, T.Y. and Peng, Q. (2016) `Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty', Int. J. Business Performance and Supply

Journal

International Journal of Business Performance and Supply Chain Modelling – Inderscience Publishers

Published: Jan 1, 2016

There are no references for this article.