Time-path routing and scheduling optimization algorithm based on max-flow theoretic

In this paper we proposed a Max-Flow Oriented Algorithm (MFOA) to achieve the shortest finish time in Time-Path Scheduling Problem (TPSP). The signification of our algorithm is that it combines both benefits of the Max-Flow algorithm and heuristic algorithms, so we can achieve a better result with lower time cost. Another feature of our algorithm is that all the data on the same node in the network can be considered as a single merged data, so the time cost is mainly depended on the network topology and the task numbers will not affect the time cost much. This feature makes our algorithm suitable for large scale applications. In this paper we figured out the relationship of Max-Flow routing and our objective: shortest finish time, and how the Max-Flow theoretic helps to achieve it. We also build the mathematical model of our MFOA with Max-Flow Oriented Scheduling (MFOS) rerouting strategy. To investigate the performance of our algorithm, we compare it with existing optimization algorithm and algorithms with 4 other heuristic rerouting strategies. Based on the results of simulations on different topologies, our algorithm is best in most of the situations. Besides the method used in this paper also helps to develop more efficient algorithms.