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Optimization Models of Discrete-Event System Dynamics

Department of Decision Sciences and Engineering Systems, Rensselaer Polytechnic Institute, Troy, New York 12180
Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720
chanw{at}rpi.edu
schruben{at}ieor.berkeley.edu

A methodology is given for modeling the dynamics of discrete-event stochastic systems as optimization problems. The intent is to provide a means to utilize the rich mathematical theory and algorithms of optimization in the study of this important class of systems. A procedure for mapping a simulation event relationship graph into a mixed-integer program is presented, along with examples of queueing networks and manufacturing systems that illustrate the approach. Several potential applications are examined, including automatic constraint generation for optimal resource scheduling, representations of max-plus algebra models for queueing system dynamics, response gradient estimation, and an unconventional technique for simulating queueing systems using virtual resources that are identified from the optimization models for these systems.

Subject classifications: simulation; methodology; system dynamics.
History: Received February 2005; revision received March 2007; accepted May 2007.