Online algorithms are central to solving resource allocation and matching challenges in dynamic environments where decisions must be made without complete knowledge of future events. Research in this ...

A forecasting-driven framework integrates ARIMA, LSTM, and ensemble learning to optimize cloud resource scheduling. By predicting CPU, memory, ...

Part I of this paper presented a method for primal decomposition of large convex separable programs into a sequence of smaller subproblems. In this part, additional theory and development of a ...

State and local governments are learning to do more with less by treating resource allocation as a strategic tool for ...

This is a preview. Log in through your library . Abstract In this paper we consider a multiperiod resource allocation model in which the resources are storable and substitutable. A specific ...