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A dynamic load balancing approach to the control of multiserver polling systems with applications to elevator system dispatching

James Alan Lewis, University of Massachusetts - Amherst

Abstract

This dissertation presents a new technique for the control of multi-server polled queueing systems. The new technique is referred to as dynamic load balancing (DLB).^ Using a simple cyclic service model, evidence is provided indicating that waiting time will be minimized if the servers of the polled queueing system remain maximally separated via a 'skip-ahead' control policy. Approximations are derived for average job waiting time in two polled queueing system 'modes'--the maximum server separation mode and the minimum server separation mode. These approximations further suggest the desirability of a 'skip-ahead' control policy to maintain maximum server separation.^ A discrete-event model and corresponding discrete-event simulation of the polled queueing system is developed. The DLB algorithm is developed to achieve the maximum server separation objective in the polled queueing system. Simulation results substantiate the approximations developed for the polled queueing system model over a wide range of system parameters and load levels.^ DLB is adapted for elevator system control. Changes to DLB were required to account for the presence of car calls and direction switching in the elevator system. Despite the added complexity of the elevator system over the multi-server polled queueing system, DLB is shown via simulation to provide improvement over a state of the art elevator system control algorithm in six of six performance measures (e.g. average waiting time). ^

Subject Area

Engineering, Civil|Engineering, Electronics and Electrical|Computer Science

Recommended Citation

James Alan Lewis, "A dynamic load balancing approach to the control of multiserver polling systems with applications to elevator system dispatching" (January 1, 1991). Doctoral Dissertations Available from Proquest. Paper AAI9207424.
http://scholarworks.umass.edu/dissertations/AAI9207424

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