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Fast Approximation Algorithms for Fractional Packing and Covering Problems
, 1995
"... This paper presents fast algorithms that find approximate solutions for a general class of problems, which we call fractional packing and covering problems. The only previously known algorithms for solving these problems are based on general linear programming techniques. The techniques developed ..."
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Cited by 238 (13 self)
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This paper presents fast algorithms that find approximate solutions for a general class of problems, which we call fractional packing and covering problems. The only previously known algorithms for solving these problems are based on general linear programming techniques. The techniques developed in this paper greatly outperform the general methods in many applications, and are extensions of a method previously applied to find approximate solutions to multicommodity flow problems. Our algorithm is a Lagrangean relaxation technique; an important aspect of our results is that we obtain a theoretical analysis of the running time of a Lagrangean relaxationbased algorithm. We give several applications of our algorithms. The new approach yields several orders of magnitude of improvement over the best previously known running times for algorithms for the scheduling of unrelated parallel machines in both the preemptive and the nonpreemptive models, for the job shop problem, for th...
Improving Branch and Bound for Jobshop Scheduling with Constraint Propagation
, 1995
"... . Task intervals were defined in [CL94] for disjunctive scheduling so that, in a scheduling problem, one could derive much information by focusing on some key subsets of tasks. The advantage of this approach was to shorten the size of search trees for branch&bound algorithms because more propaga ..."
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Cited by 20 (4 self)
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. Task intervals were defined in [CL94] for disjunctive scheduling so that, in a scheduling problem, one could derive much information by focusing on some key subsets of tasks. The advantage of this approach was to shorten the size of search trees for branch&bound algorithms because more propagation was performed at each node. In this paper, we refine the propagation scheme and describe in detail the branch&bound algorithm with its heuristics and we compare constraint programming to integer programming. This algorithm is tested on the standard benchmarks from Muth & Thompson, Lawrence, Adams et al, Applegate & Cook and Nakano & Yamada. The achievements are the following: . Window reduction by propagation : for 23 of the 40 problems of Lawrence, the proof of optimality is found with no search, by sole propagation; for typically hard 10 × 10 problems, the search tree has less than a thousand nodes; hard problems with up to 400 tasks can be solved to optimality and among these, the open p...