Many optimization techniques have been adopted for efficient job scheduling in grid computing, such as: genetic algorithms, simulated annealing and stochastic methods. Such techni...
Renato Porfirio Ishii, Rodrigo Fernandes de Mello,...
Approximate dynamic programming is emerging as a powerful tool for certain classes of multistage stochastic, dynamic problems that arise in operations research. It has been applie...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
We present a new class of randomized approximation algorithms for unrelated parallel machine scheduling problems with the average weighted completion time objective. The key idea i...
Train Routing is a problem that arises in the early phase of the passenger railway planning process, usually several months before operating the trains. The main goal is to assign...
Alberto Caprara, Laura Galli, Leo G. Kroon, G&aacu...