Author_Institution :
Div. of Eng., Brown Univ., Providence, RI, USA
Abstract :
The state of the art in scheduling “point-to-point” trains in a railway network utilizes the principles of centralized decision-making. The major difficulty of this approach is that the execution time and the memory requirements increase nonlinearly as the system grows in size. The present paper introduces a new approach, “DARYN”, wherein the overall decision process is analyzed and distributed onto every natural entity of the system. In DARYN, the decision process for every train is executed by an on-board processor that negotiates, dynamically and progressively, for temporary ownership of the tracks with the respective station controlling the tracks, through explicit processor to processor communication primitives. This processor then computes its own route utilizing the results of its negotiation, its knowledge of the track layout of the entire system, and its evaluation of the cost function. Every station´s decision process is also executed by a dedicated processor that, in addition, maintains absolute control over a given set of tracks and participates in the negotiation with the trains. Presently, DARYN utilizes a simple cost function. However, if one chooses to increase the complexity of the cost function, DARYN´s advantage over the traditional approach increases due to its enormous available computational power. Given that the current microprocessors such as MC68030, MC88000, Intel 486, and Intel 860 are powerful yet relatively inexpensive, a network of concurrently executing processors may offer superior price-performance quotient over a single high performance computer
Keywords :
distributed control; distributed decision making; rail traffic; scheduling; traffic control; DARYN; cost function; decision process; distributed decision-making algorithm; onboard processor; point-to-point trains; processor to processor communication primitives; railway networks; route; scheduling; station; track layout; train; Communication system control; Cost function; Data analysis; Decision making; Distributed computing; Distributed decision making; Power engineering computing; Processor scheduling; Rail transportation; Scheduling algorithm;