Distribution-dependent robust linear optimization with applications to inventory control
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Summary
By exploiting distributional information, stronger upper bounds are established on the constraint violation probability of a solution, which enables us to “inject” less conservatism into the formulation, which in turn yields a more cost-effective solution.
- Type
- article
- Published
- 2013-11-05
- Cited by
- 21
- References
- 31
- OpenAlex
- https://openalex.org/W26347579
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1696804
Keywords
Computer science, Knowledge management, Context (archaeology), Process management, Business
References
- On Robust Optimization
- Introduction to Stochastic Programming
- Stochastic Programming
- Theory and Applications of Robust Optimization
- Distributionally Robust Chance-Constrained Linear Programs with Applications
- Large deviations-based asymptotics for inventory control in supply chains
- On Distributionally Robust Chance-Constrained Linear Programs
- Control of Uncertain Systems: A Linear Programming Approach
- Technical Note - Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming
- Robust solutions of uncertain linear programs
- Inventory Control for Supply Chains with Service Level Constraints: A Synergy between Large Deviations and Perturbation Analysis
- Probability Inequalities for the Sum of Independent Random Variables
- Probabilistic Service Level Guarantees in Make-to-Stock Manufacturing Systems
- Adjustable robust solutions of uncertain linear programs
- A Robust Optimization Approach to Inventory Theory
- Large Deviations Techniques and Applications
- A Hierarchy of Near-Optimal Policies for Multistage Adaptive Optimization
- Distributionally robust joint chance constraints with second-order moment information
- From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization
- Convex Approximations of Chance Constrained Programs
Cited by
- A State‐Feedback Approach to Inventory Control: Analytical and Empirical Studies
- New a priori and a posteriori probabilistic bounds for robust counterpart optimization: I. Unknown probability distributions
- New a priori and a posteriori probabilistic bounds for robust counterpart optimization: II. A priori bounds for known symmetric and asymmetric probability distributions
- Conditions under which adjustability lowers the cost of a robust linear program
- Theoretical Advances in Robust Optimization, Feature Selection, and Biomarker Discovery
- Optimizing (s, S) policies for multi-period inventory models with demand distribution uncertainty: Robust dynamic programing approaches
- New a priori and a posteriori probabilistic bounds for robust counterpart optimization: III. Exact and near-exact a posteriori expressions for known probability distributions
- Generalized robust counterparts for constraints with bounded and unbounded uncertain parameters
- Online and Distributed Robust Regressions Under Adversarial Data Corruption
- Advancing Robust Optimization for Process Systems Engineering Applications
- Scalable Robust Models Under Adversarial Data Corruption
- A robust optimization approach for the vehicle routing problem with selective backhauls
- Robustness-based approach for fuzzy multi-objective problems
- Online and Distributed Robust Regressions with Extremely Noisy Labels
- Robust optimization approaches in inventory management: Part A—the survey
- Flexible robust optimal bidding of renewable virtual power plants in sequential markets under asymmetric uncertainties
- Adjustable Target‐Oriented Robust Optimization for Inventory Management
- Robust optimization of pharmaceutical inventory under supply and demand uncertainty: a multi-period CVaR model based on the SPD framework
- A new robust capacitated hub interdiction problem under ambiguous demand and its benders decomposition
- The Evolution of Knowledge Management Systems Needs to be Managed
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