Please use this identifier to cite or link to this item: https://doi.org/10.1287/opre.1080.0570
Title: Goal-driven optimization
Authors: Chen, W.
Sim, M. 
Issue Date: 2009
Source: Chen, W., Sim, M. (2009). Goal-driven optimization. Operations Research 57 (2) : 342-357. ScholarBank@NUS Repository. https://doi.org/10.1287/opre.1080.0570
Abstract: We develop a goal-driven stochastic optimization model that considers a random objective function in achieving an aspiration level, target, or goal. Our model maximizes the shortfall-aware aspiration-level criterion, which encompasses the probability of success in achieving the aspiration level and an expected level of underperformance or shortfall. The key advantage of the proposed model is its tractability. We can obtain its solution by solving a small collection of stochastic linear optimization problems with objectives evaluated under the popular conditional-value-at-risk(CVaR) measure. Using techniques in robust optimization, we propose a decision-rule-based deterministic approximation of the goal-driven optimization problem by solving subproblems whose number is a polynomial with respect to the accuracy, with each subproblem being a second-order cone optimization problem(SOCP). We compare the numerical performance of the deterministic approximation with sampling-based approximation and report the computational insights on a multiproduct newsvendor problem. © 2009 INFORMS.
Source Title: Operations Research
URI: http://scholarbank.nus.edu.sg/handle/10635/43946
ISSN: 0030364X
DOI: 10.1287/opre.1080.0570
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

32
checked on Dec 14, 2017

WEB OF SCIENCETM
Citations

25
checked on Nov 18, 2017

Page view(s)

50
checked on Dec 10, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.