  The paper investigates stochastic resource allocation problems with scarce,
reusable resources and non-preemtive, time-dependent, interconnected tasks.
This approach is a natural generalization of several standard resource
management problems, such as scheduling and transportation problems. First,
reactive solutions are considered and defined as control policies of suitably
reformulated Markov decision processes (MDPs). We argue that this reformulation
has several favorable properties, such as it has finite state and action
spaces, it is aperiodic, hence all policies are proper and the space of control
policies can be safely restricted. Next, approximate dynamic programming (ADP)
methods, such as fitted Q-learning, are suggested for computing an efficient
control policy. In order to compactly maintain the cost-to-go function, two
representations are studied: hash tables and support vector regression (SVR),
particularly, nu-SVRs. Several additional improvements, such as the application
of limited-lookahead rollout algorithms in the initial phases, action space
decomposition, task clustering and distributed sampling are investigated, too.
Finally, experimental results on both benchmark and industry-related data are
presented.
