We propose a heuristic to solve the server placement problem and we design a generic greedy framework for the online scheduling problem.
A realistic case study is presented in the following section in order to validate the effectiveness of the proposed application framework for scheduling the spool fabrication based on the project material-supply-driven project planning and control optimization algorithm.
In this case study, three sample spools from the project are selected to build a test case, so to verify the feasibility of the proposed framework for scheduling the spool fabrication operations (i.e., mainly fitting and welding) under the constraints of limited laborer resources, preset deadlines of particular spools, technological relationships, and time-dependent material supply patterns.
The simulation results demonstrate that the proposed framework for scheduling the storage space heating load provides a method to selectively hedge against the price and load uncertainty risk.
By using a stochastic system analysis approach and average dwell time technique, a framework for the transmission power scheduling and control co-design is established.
In this study, a generic approach for material-supply-driven project planning and control optimization is adapted into an application framework for scheduling the spool fabrication operations.
In this paper, we present a systematic framework for the design of gain scheduled observer based controllers for affine linear parameter-varying (LPV) systems.
A framework for the joint design of sensor scheduling and fault isolation is proposed.
For these reasons, we propose in this paper an innovative scheduling framework for the downlink of LTE.
The objective of our investigation is the design of a scheduling framework for the downlink of the LTE system which satisfies the QoS requirements defined by the QoS architecture of 3GPP specifications [1, 2].
In this work, we provide a complete mathematical framework for the energy optimal packet-based scheduling and analyze the proposed scheme using Markov chain in the many-user limit.
