Mathematics of Operations Research
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Dual Stochastic Dominance and Related Mean-Risk Models
SIAM Journal on Optimization
Applying the Minimum Risk Criterion in Stochastic Recourse Programs
Computational Optimization and Applications
Operations Research
Adjustable robust solutions of uncertain linear programs
Mathematical Programming: Series A and B
Computational complexity of stochastic programming problems
Mathematical Programming: Series A and B
Convexity and decomposition of mean-risk stochastic programs
Mathematical Programming: Series A and B
Optimization of Convex Risk Functions
Mathematics of Operations Research
A Robust Optimization Perspective on Stochastic Programming
Operations Research
A Linear Decision-Based Approximation Approach to Stochastic Programming
Operations Research
Satisficing Measures for Analysis of Risky Positions
Management Science
Robust Approximation to Multiperiod Inventory Management
Operations Research
Distributionally Robust Optimization and Its Tractable Approximations
Operations Research
Optimization Under Probabilistic Envelope Constraints
Operations Research
Robust Storage Assignment in Unit-Load Warehouses
Management Science
Multiple Objectives Satisficing Under Uncertainty
Operations Research
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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.