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Introduction Operations research is a well-recognized formal field of study. It is a branch of applied mathematics, which basically includes all mathematical methods that are used to model complex problems in science, engineering, business, social sciences and other fields. As the name suggests, it also includes the application of these methods to find optimal solutions for complex problems by use of computers. Operation research uses analytical models that simulate the dynamic system being studied with the help of computer analysis to predict trends and probable future outcomes. Operational research can be classified into two broad categories: "linear programming" and "nonlinear programming". Linear programs are those in which solving for an optimal solution can be done using deterministic or linear algebraic techniques. Nonlinear programming (also called dynamic or non-deterministic) problems involve the use of stochastic or nonlinear models for solutions. The term "operations research" was coined by John von Neumann in 1952 to describe the application of the mathematical theory of probability and statistics to management problems. The first book that defined operations research as a distinct field of study was written by Wassily Leontief, who depicted it as a new science that treated operations as a higher level phenomenon. In his book "Economic Theory and Operations Research", he investigated the connections between economic theory and mathematical probabilistic models, which were used to explore how decision makers make decisions under uncertainty. The rise of operations research was fueled by growing interest in the application of mathematics to real world problems arising from World War II. During this period, operations research became heavily reliant upon mathematical programming techniques developed for the Manhattan Project, which used linear models to estimate the likely nuclear fallout from exploding atomic bombs, thereby reducing the risk to personnel in later missions. Operations research is now widely recognized as a specialty within applied mathematics, although it has grown much larger than any one particular field. The role of operations research in business can be seen in firms' use of supply chain management systems. These are often governed by linear programming models that work with decision support systems. Non-linear models are now common in operations research. A vital part of this evolution is the understanding that the large-scale behavior of dynamic problems differs fundamentally from the behavior of static problems. One non-linear model that has gained widespread use in recent years is the queuing network, which describes systems modeled by differential equations; it is estimated that eighty percent of operations researchers regularly use this mathematical technique. Queuing networks are often deployed to optimize business processes, such as routing tasks to appropriate servers, prioritizing different types of tasks, and allocating resources optimally. Operations research encompasses problem solving using analytical techniques to decide how to accomplish an objective most effectively. Supply chain management is a field that uses operations research to outperform competitors and enhance profits. Supply chain operations research includes supply chain modeling, supply chain analytics and supply chain optimization. By using the analytic techniques, one can use mathematical models to solve real world problems and increase profits. Supply chains are not static entities; they evolve as the strategies of the companies in question change and as their underlying technologies improve. There are different analytical techniques that can be applied to optimize any given supply chain; some of these include:Game theory is widely used in operations research for modeling various problems, such as coordination games, bargaining games, reputation building games and issues with common resources (this is related to non-cooperative game theory). cfa1e77820
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