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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
An approximation algorithm for the generalized assignment problem
, 1993
"... The generalized assignment problem can be viewed as the following problem of scheduling parallel machines with costs. Each job is to be processed by exactly one machine; processing job j on machine i requires time pif and incurs a cost of c,f, each machine / is available for 7", time units, ..."
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Cited by 201 (5 self)
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The generalized assignment problem can be viewed as the following problem of scheduling parallel machines with costs. Each job is to be processed by exactly one machine; processing job j on machine i requires time pif and incurs a cost of c,f, each machine / is available for 7", time units
An efficient approximation for the generalized assignment problem
 Information Processing Letters
, 2006
"... We present a simple family of algorithms for solving the Generalized Assignment Problem (GAP). Our technique is based on a novel combinatorial translation of any algorithm for the knapsack problem into an approximation algorithm for GAP. If the approximation ratio of the knapsack algorithm is α and ..."
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Cited by 33 (6 self)
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We present a simple family of algorithms for solving the Generalized Assignment Problem (GAP). Our technique is based on a novel combinatorial translation of any algorithm for the knapsack problem into an approximation algorithm for GAP. If the approximation ratio of the knapsack algorithm is α
The Generalized Assignment Problem and Its Generalizations
"... The generalized assignment problem is a classical combinatorial optimization problem that models a variety of real world applications including flexible manufacturing systems [6], facility location [11] and vehicle routing problems [2]. Given n jobs J = {1, 2,..., n} and m agents I = {1, 2,..., m}, ..."
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The generalized assignment problem is a classical combinatorial optimization problem that models a variety of real world applications including flexible manufacturing systems [6], facility location [11] and vehicle routing problems [2]. Given n jobs J = {1, 2,..., n} and m agents I = {1, 2,..., m
An efficient approximation for the Generalized Assignment Problem
, 2006
"... We present a simple family of algorithms for solving the Generalized Assignment Problem (GAP). Our technique is based on a novel combinatorial translation of any algorithm for the knapsack problem into an approximation algorithm for GAP. If the approximation ratio of the knapsack algorithm is α and ..."
Abstract
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We present a simple family of algorithms for solving the Generalized Assignment Problem (GAP). Our technique is based on a novel combinatorial translation of any algorithm for the knapsack problem into an approximation algorithm for GAP. If the approximation ratio of the knapsack algorithm is α
A stochastic generalized assignment problem
 Naval Postgraduate School, Working Paper
, 2003
"... We develop a stochastic version of the Elastic Generalized Assignment Problem (EGAP) that incorporates independent, normally distributed resourceconsumption coefficients and other random parameters. The Stochastic EGAP (SEGAP) is a stochastic integer program with simple recourse. We construct two d ..."
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Cited by 5 (0 self)
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We develop a stochastic version of the Elastic Generalized Assignment Problem (EGAP) that incorporates independent, normally distributed resourceconsumption coefficients and other random parameters. The Stochastic EGAP (SEGAP) is a stochastic integer program with simple recourse. We construct two
Generalized Assignment Problem A Brief Introduction
"... The generalized assignment problem is one of the representative combinational optimization problems known to be NPhard. Generally, the objective is to minimize the total cost when assigning J jobs to M agents with constrained capacity such that each job is assigned to exactly one agent. Some agents ..."
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The generalized assignment problem is one of the representative combinational optimization problems known to be NPhard. Generally, the objective is to minimize the total cost when assigning J jobs to M agents with constrained capacity such that each job is assigned to exactly one agent. Some
A Linear Relaxation Heuristic For The Generalized Assignment Problem
 Naval Research Logistics
, 1992
"... We examine the basis structure of the linear relaxation of the generalized assignment problem. The basis gives a surprising amount of information. This leads to a very simple heuristic that uses only generalized network optimization codes. Lower bounds can be generated by cut generation, where t ..."
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Cited by 17 (1 self)
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We examine the basis structure of the linear relaxation of the generalized assignment problem. The basis gives a surprising amount of information. This leads to a very simple heuristic that uses only generalized network optimization codes. Lower bounds can be generated by cut generation, where
An Improved Hybrid Genetic Algorithm for the Generalized Assignment Problem
 Proceedings of the 2003 ACM Symposium on Applied Computing
, 2004
"... We consider the generalized assignment problem in which the objective is to find a minimum cost assignment of a set of jobs to a set of agents subject to resource constraints. The presented new approach is based on a previously published, successful hybrid genetic algorithm and includes as new featu ..."
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Cited by 14 (2 self)
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We consider the generalized assignment problem in which the objective is to find a minimum cost assignment of a set of jobs to a set of agents subject to resource constraints. The presented new approach is based on a previously published, successful hybrid genetic algorithm and includes as new
Results 1  10
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4,532,065