Developing scenario-based robust optimisation approaches for the reverse logistics network design problem under uncertain environments
by Reza Babazadeh; Fariborz Jolai; Jafar Razmi
International Journal of Services and Operations Management (IJSOM), Vol. 20, No. 4, 2015

Abstract: In the last decade, economic benefits and environmental legislation have imposed reverse logistics activities, induced by various forms of return, to organisations. Reverse logistics network design is a major strategic issue. This paper discusses scenario-based stochastic programming method and robust optimisation approaches including minimisation conditional value-at-risk (CVaR), p-robust regret and min-max regret models to find the most appropriate method dealing with uncertain environment in designing reverse logistics network. Firstly, the reverse logistics network design model is developed by using two-stage stochastic programming approach integrating CVaR in its objective function as a robustness criterion ensuring that the amount of objective function is not worse than the CVaR value with specified probability (confidence level) under all realisations. Also, the advantages of stochastic programming method are investigated respect to deterministic model under all defined scenarios. Then, the scenario-based robust optimisation methods are compared with the stochastic and deterministic ones to disclose their advantages and disadvantages.

Online publication date: Wed, 22-Apr-2015

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Services and Operations Management (IJSOM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com