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CORE Lecture Series; This series was created in 1987 with the CORE Foundation, a privately financed international scientific association aiming to support research in econometrics, operations research and economics as well as scientific cooperation and training in these fields. The CORE Lecture Series is constituted of the presentations of ...
Operations research (British English: operational research) (U.S. Air Force Specialty Code: Operations Analysis), often shortened to the initialism OR, is a discipline that deals with the development and application of analytical methods to improve decision-making. [1]
The Journal of the Operational Research Society is a monthly peer-reviewed academic journal covering operations research.It is an official journal of the Operational Research Society and publishes full length case-oriented papers, full length theoretical papers, technical notes, discussions (viewpoints) and book reviews.
The field of system analysis relates closely to requirements analysis or to operations research. It is also "an explicit formal inquiry carried out to help a decision maker identify a better course of action and make a better decision than they might otherwise have made."
Operations research, operational research, or simply O.R., is the use of mathematical models, statistics and algorithms to aid in decision-making. It is most often used to analyze complex real-world systems, typically with the goal of improving or optimizing performance. It is one form of applied mathematics
In computer science and operations research, a memetic algorithm (MA) is an extension of an evolutionary algorithm (EA) that aims to accelerate the evolutionary search for the optimum. An EA is a metaheuristic that reproduces the basic principles of biological evolution as a computer algorithm in order to solve challenging optimization or ...
Operations research also uses stochastic modeling and simulation to support improved decision-making. Increasingly, operations research uses stochastic programming to model dynamic decisions that adapt to events; such problems can be solved with large-scale optimization and stochastic optimization methods.
A fully polynomial-time approximation scheme (FPTAS) is an algorithm for finding approximate solutions to function problems, especially optimization problems.An FPTAS takes as input an instance of the problem and a parameter ε > 0.