Sample average approximation method for the chance-constrained stochastic programming in the transportation model of emergency management Online publication date: Thu, 30-Apr-2015
by Chunlin Deng; Liu Yang
International Journal of Simulation and Process Modelling (IJSPM), Vol. 9, No. 4, 2014
Abstract: This study proposes a stochastic programming model for the transportation of emergency resource during the emergency response. Since it is difficult to predict the timing and magnitude of any disaster and its impact on the urban system, resource mobilisation is treated in a random manner, and the resource requirements are represented as random variables. Randomness is represented by the chance constraints in this paper. To deal with the difficulty in calculating the chance constraint function, we use conditional value at risk (CVaR) to approximate the chance constraint, and solve the approximation problem of the chance-constrained stochastic programming by using the sample average approximation (SAA) method. For a given sample, the SAA problem is a deterministic nonlinear programming (NLP) and any appropriate NLP code can be applied to solve the problem. The model and method provide a new way for the emergency logistics management engineering.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Simulation and Process Modelling (IJSPM):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook