Goal-Driven Optimization

作     者：Chen, Wenqing Sim, Melvyn 

作者机构：Natl Univ Singapore NUS Business Sch NUS Risk Management Inst Singapore 117548 Singapore Singapore MIT Alliance SMA Singapore Singapore 

出 版 物：《OPERATIONS RESEARCH》 (运筹学)

年 卷 期：2009年第57卷第2期

页      面：342-357页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：SMA NUS Risk Management Institute NUS academic research [R-314-000-066-122, R-314-000-068-122] 

主　　题：programming: stochastic 

摘      要：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.