Linear Programming Under Uncertainty

By G. Dantzig. Management Sciences, 2004

1,449 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/4eda893eb020a58c3c71e6bd75bc9ee9252967bf

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In Control and Systems Engineering

In Management Information Systems

In Management Science and Operations Research

Citations in the corpus

Uncertainty Theory (2004, neutral): “Stochastic programming was first studied by Dantzig [22] in 1965, while uncertain programming was first proposed by Liu [79] in 2009.”
Models for production planning under uncertainty: A review ☆ (2006, background): “%) and published PhD Thesis ARTICLE IN PRESS Table 3 Summary of citations on models for production planning under uncertainty Source Number of citations % Total Annals of Operations Research 2 2.30 Books 9 10.34 Computer and Chemical Engineering 1 1.15 Computers and Industrial Engineering 2 2.30…”
Managing demand uncertainty in supply chain planning (2003, background): “One of the most popular frameworks for planning under uncertainty is two-stage stochastic programming (Birge & Louveaux, 1997; Dantzig, 1955).”
Lectures on Stochastic Programming: Modeling and Theory (2009, background): “Chapters 2 and 3 Stochastic programming with recourse originated in the works of Beale [14], Dantzig [48] and Tintner [260].”
Optimization with Stochastic Dominance Constraints (2003, methodology): “The first stochastic optimization models with expected values were introduced in [1] [6].”
A review on simulation-based optimization methods applied to building performance analysis (2014, background): “Pioneer studies on optimization under uncertainty can be traced back to the 1950s [102].”
Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty (2011, background): “Stochastic programming (Dantzig, 1955) is particular from the point of view of approximation and numerical optimization in that it involves a representation of the objective F by an integral (as soon as F stands for an expected cost under a continuous probability distribution), a large, possibly inf”
Uncertain convex programs: randomized solutions and confidence levels (2005, background): “We finally remark that the proposed randomized approach to uncertain convex problems is different in spirit from the classical stochastic approximation methods used in stochastic programming, [11, 29], though even in the latter context a scenario-like approach is often used.”
Robust and Data-Driven Optimization: Modern Decision-Making Under Uncertainty (2006, background): “The field of decision-making under uncertainty was pioneered in the 1950s by Dantzig [25] and Charnes and Cooper [23], who set the foundation for, respectively, stochastic programming and optimization under probabilistic constraints.”
Robust Linear Optimization With Recourse (2009, methodology): “The focus of this paper is on two-stage linear optimization with right-hand side uncertainty, which was first described by Dantzig in [17].”
Decision making under uncertainty in energy systems: state of the art (2013, background): “These models are described as follows: • Probabilistic approach: one of the earliest works in stochastic programming was done by Dantzig in 1955 [3].”
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization (2010, background): “A class of adaptive optimization problems studied extensively in operations research (since Dantzig (1955)) is the area of stochastic optimization with recourse.”
Pyomo — Optimization Modeling in Python (2012, background): “From the earliest days of using computers to optimization problems, it was recognized that input data is uncertain in most real-world decision problems [20].”