George Bernard Dantzig (/ˈdæntsɪɡ/; November 8, 1914 – May 13, 2005) was an American mathematical scientist who made contributions to industrial engineering, operations research, computer science, economics, and statistics.

Early in the 1920s the Dantzig family moved from Baltimore to Washington, D.C. His mother became a linguist at the Library of Congress, and his father became a math tutor at the University of Maryland, College Park.

Over time, some facts were altered, but the basic story persisted in the form of an urban legend and as an introductory scene in the movie Good Will Hunting.

"[8] Years later, another researcher, Abraham Wald, was preparing to publish a paper where he had arrived at a conclusion for the second problem when he learned of Dantzig's earlier solution.

[4][8][9] With the outbreak of World War II, Dantzig took a leave of absence from the doctoral program at Berkeley to work as a civilian for the United States Army Air Forces.

By 1960, he became a professor in the Department of Industrial Engineering at UC Berkeley, where he founded and directed the Operations Research Center.

On a sabbatical leave that year, he managed the Methodology Group at the International Institute for Applied Systems Analysis (IIASA) in Laxenburg, Austria.

Dantzig was the recipient of many honors, including the first John von Neumann Theory Prize in 1974, the National Medal of Science in 1975,[10] and an honorary doctorate from the University of Maryland, College Park in 1976.

Based on his work, tools are developed "that shipping companies use to determine how many planes they need and where their delivery trucks should be deployed.

It is used in manufacturing, revenue management, telecommunications, advertising, architecture, circuit design and countless other areas".

Linear programming arose as a mathematical model developed during World War II to plan expenditures and returns in order to reduce costs to the army and increase losses to the enemy.

However, it takes only a moment to find the optimum solution by posing the problem as a linear program and applying the Simplex algorithm.