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Approximation algorithms for energy, reliability and makespan optimization problems
, 2012
"... ..."
EnvyFree Makespan Approximation
, 2009
"... We study envyfree mechanisms for scheduling tasks on unrelated machines (agents) that approximately minimize the makespan. For indivisible tasks, we put forward an envyfree polytime mechanism that approximates the minimal makespan to within a factor of O(log m), where m is the number of machines. ..."
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Cited by 2 (2 self)
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. We also show a lower bound of Ω(log m / log log m). This improves the recent result of Hartline et al. [15] who give an upper bound of (m+1)/2, and a lower bound of 2−1/m. For divisible tasks, we show that there always exists an envyfree polytime mechanism with optimal makespan.
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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approximation algorithm to minimize a weighted sum of the cost and the makespan, i.e., the maximum job completion time. We also consider the objective of minimizing the mean job completion time. We show that there is a polynomialtime algorithm that, given values M and 7", either proves
Using dual approximation algorithms for scheduling problems: theoretical and practical results
 Journal of the ACM
, 1987
"... Abstract. The problem of scheduling a set of n jobs on m identical machines so as to minimize the makespan time is perhaps the most wellstudied problem in the theory of approximation algorithms for NPhard optimization problems. In this paper the strongest possible type of result for this problem, ..."
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Cited by 215 (2 self)
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Abstract. The problem of scheduling a set of n jobs on m identical machines so as to minimize the makespan time is perhaps the most wellstudied problem in the theory of approximation algorithms for NPhard optimization problems. In this paper the strongest possible type of result for this problem
PopulationBased Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
, 1994
"... Genetic algorithms (GAs) are biologically motivated adaptive systems which have been used, with varying degrees of success, for function optimization. In this study, an abstraction of the basic genetic algorithm, the Equilibrium Genetic Algorithm (EGA), and the GA in turn, are reconsidered within th ..."
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Cited by 352 (12 self)
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. The combination of these two methods reveals a tool which is far simpler than a GA, and which outperforms a GA on large set of optimization problems in terms of both speed and accuracy. This paper presents an empirical analysis of where the proposed technique will outperform genetic algorithms, and describes a
Approximation Algorithms for Disjoint Paths Problems
, 1996
"... The construction of disjoint paths in a network is a basic issue in combinatorial optimization: given a network, and specified pairs of nodes in it, we are interested in finding disjoint paths between as many of these pairs as possible. This leads to a variety of classical NPcomplete problems for w ..."
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Cited by 166 (0 self)
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for which very little is known from the point of view of approximation algorithms. It has recently been brought into focus in work on problems such as VLSI layout and routing in highspeed networks; in these settings, the current lack of understanding of the disjoint paths problem is often an obstacle
OPlan: the Open Planning Architecture
, 1990
"... OPlan is an AI planner based on previous experience with the Nonlin planner and its derivatives. Nonlin and other similar planning systems had limited control architectures and were only partially successful at limiting their search spaces. OPlan is a design and implementation of a more flexible s ..."
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Cited by 371 (41 self)
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OPlan is an AI planner based on previous experience with the Nonlin planner and its derivatives. Nonlin and other similar planning systems had limited control architectures and were only partially successful at limiting their search spaces. OPlan is a design and implementation of a more flexible system aimed at supporting planning research and development, opening up new planning methods and supporting strong search control heuristics. OPlan takes an engineering approach to the construction of an efficient domain independent planning system which includes a mixture of AI and numerical techniques from Operations Research. The main contributions of the work are centred around the control of search within the OPlan planning framework, and this paper outlines the search control heuristics employed within the planner. These involve the use of condition typing, time and resource constraints and domain constraints to allow knowledge about an application domain to be used to prune the searc...
Static Scheduling Algorithms for Allocating Directed Task Graphs to Multiprocessors
, 1999
"... Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported ..."
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Cited by 312 (4 self)
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Devices]: Modes of ComputationParallelism and concurrency General Terms: Algorithms, Design, Performance, Theory Additional Key Words and Phrases: Automatic parallelization, DAG, multiprocessors, parallel processing, software tools, static scheduling, task graphs This research was supported
An Efficient Approximation Algorithm for Minimizing Makespan on Uniformly Related Machines
 Journal of Algorithms
, 1999
"... We give a new efficient approximation algorithm for scheduling precedence constrained jobs on machines with different speeds. The problem is as follows. We are given n jobs to be scheduled on a set of m machines. Jobs have processing times and machines have speeds. It takes p j =s i units of time ..."
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Cited by 34 (4 self)
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to minimize Cmax = max j C j , conventionally called the makespan of the schedule, where C j is the completion time of job j. Recently Chudak and Shmoys [2] gave an algorithm with an approximation ratio of O(log m) significantly improving the earlier ratio of O( m) due to Jaffe [6]. Their algorithm
Results 1  10
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