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		    A* Algorithm
 
 Acceptance-Rejection Method
 
 A heuristic search procedure that selects a node in its search tree for expansion such that the selected node has minimum value of the sum of the cost to reach the node plus a heuristic cost value for that node, where the heuristic cost underestimates the true minimum cost of completion.
 
 In stochastic or Monte Carlo simulation, a method for sampling from a given difficult target probability distribution by sampling from a distribution that is close to the target distribution and relatively easy to sample but possibly rejecting the generated output. Sometimes just called the rejection method.
 
 See
 
 See
 
 ▶ Artificial Intelligence
 
 ▶ Monte Carlo Methods ▶ Monte Carlo Simulation ▶ Simulation of Stochastic Discrete-Event Systems
 
 References Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4, 100–107. Pearl, J. (1984). Heuristics. Reading, MA: Addison-Wesley.
 
 Accounting Prices ▶ Shadow Prices
 
 Acceptance Sampling
 
 Accreditation
 
 ▶ Quality Control
 
 ▶ Model Accreditation
 
 S.I. Gass, M.C. Fu (eds.), Encyclopedia of Operations Research and Management Science, DOI 10.1007/978-1-4419-1153-7, # Springer Science+Business Media New York 2013
 
 A
 
 2
 
 Active Constraint
 
 Active Constraint
 
 Activity-Analysis Problem
 
 A constraint in an optimization problem that is satisfied exactly by a solution.
 
 A linear-programming problem of the form Maximize cx, subject to Ax  b, x  0. The variables xj of the vector x are quantities of products to be produced. The bi coefficients of the resource vector b represent the amount of resource i that is available for production, the cj coefficients represent the value (profit) of one unit of output xj, and the coefficients aij of the technological matrix A represent the amount of resource i required to produce one unit of product j. The aij are termed technological or input-output coefficients. The objective function cx represents some measure of value of the total production.
 
 See ▶ Inactive Constraint ▶ Slack Variable ▶ Surplus Variable
 
 Active Set Methods ▶ Quadratic Programming
 
 See ▶ Input–Output Analysis ▶ Input–Output Coefficients ▶ Linear Programming
 
 Activity (1) A structural variable whose value (level) is to be computed in a linear programming problem. (2) Project work items having specific beginning and completion points and durations.
 
 Acyclic Network A network that contains no cycles.
 
 See See ▶ Network Planning ▶ Project Management ▶ Structural Variables
 
 ▶ Graph Theory ▶ Network Optimization
 
 Adjacent Activity Level The value taken by a structural variable in an intermediate or final solution to a linear programming problem.
 
 Nodes of a graph or network are adjacent if they are joined by an edge; edges are adjacent if they share a common node.
 
 See See ▶ Structural Variables
 
 ▶ Graph Theory ▶ Network Optimization
 
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 Adjacent (Neighboring) Extreme Points Two extreme points of a polyhedron that are connecte		
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