Two processor scheduling is in NC
SIAM Journal on Computing
Efficient parallel algorithms
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
The two-processor scheduling problem is in random NC
SIAM Journal on Computing
Job shop scheduling by simulated annealing
Operations Research
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
A Parallel Algorithm for Two Processors Precedence Constraint Scheduling
ICALP '91 Proceedings of the 18th International Colloquium on Automata, Languages and Programming
High-Level Data Parallel Programming in PROMOTER
HIPS '97 Proceedings of the 1997 Workshop on High-Level Programming Models and Supportive Environments (HIPS '97)
SASEPA: Simultaneous Allocation and Scheduling with Exclusion and Precedence Relations Algorithm
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
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In the paper, we investigate theoretical and practical aspects of distributed computing for simulated annealing algorithms applied to the problem of scheduling l jobs on m machines. Given n = l ċ m, the total number of tasks, O(n3) processors and an upper bound Λ = Λ(l, m) of the objective function, the expected run-times of parallelized versions of our heuristics [14] are O(n ċ log n ċ log Λ) for the exponential cooling schedule and O(n2 ċ log3/2 n c˙ m1/2 ċ log Λ) for the hyperbolic one. For Markov chains of constant length, the results imply a polylogarithmic run-time O(log n ċ log(l + m)) for the exponential schedule, where we employ Λ ≤ O(l + m), see Leighton et al. [10]. We implemented a distributed version of our sequential heuristics and first computational experiments on benchmark instances are presented.