50+ Years of Innovation
Cornell’s leadership in advancing the field of Operations Research is highly acclaimed within both academia and industry. The School of Operations Research and Information Engineering is considered to be one of the world’s preeminent graduate and undergraduate engineering programs.
The history of Operations Research and Information Engineering (ORIE) at Cornell University is deeply rooted in the engineering traditions of Cornell. While the first courses in Industrial Engineering were taught in 1895, the University’s engineering history spans a period of 170 years beginning in 1830. In 2009, Cornell’s College of Engineering published a history of the College that includes a chapter on ORIE.
Cornell University awarded the nation’s first doctorates in industrial engineering. Industrial engineering courses were first taught in 1895, through the Sibley School of Mechanical Engineering. The modern College of Engineering was founded in 1921 and consisted of the School of Civil Engineering, the School of Mechanical Engineering, and the School of Electrical Engineering. Courses in Operations Research were introduced in 1955. In 1961 the Department of Industrial Engineering and Administration and the graduate field of Industrial Engineering and Operations Research were established. In 1965 the Master of Engineering program was established for Cornell students desiring a more comprehensive and advanced professional education beyond the four-year Bachelor of Science degree. Since that time, the School has continued to expand its curriculum, research activity has flourished, the Master of Engineering program has expanded to attract students from all over the world, faculty has doubled in size, and many new courses and programs have been added.
The School of Operations Research and Information Engineering, on the forefront of Operations Research science, continues to influence and impact organizational and managerial process improvement, productivity, and performance through sophisticated mathematical and analytical techniques—quantitative methodologies applied to solving complex problems and reducing business risk.