Optimization Models of Discrete-Event System Dynamics

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 simulatio...

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Bibliographic Details
Published inOperations research Vol. 56; no. 5; pp. 1218 - 1237
Main Authors Chan, Wai Kin (Victor), Schruben, Lee
Format Journal Article
LanguageEnglish
Published Hanover, MD INFORMS 01.09.2008
Institute for Operations Research and the Management Sciences
Subjects
Algebra
Algorithms
Applied sciences
Exact sciences and technology
Linear programming
Mathematical models
Mathematical optimization
Mathematical programming
methodology
Modeling
Network servers
Objective functions
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Optimization algorithms
Queueing networks
Queuing theory
Queuing theory. Traffic theory
Scheduling
Simulation
Simulations
Stochastic systems
Studies
system dynamics
Vertices
United States
Gradient
Event graph
simulation: methodology
Mixed integer programming
Scheduling
Systems theory
Dynamical system
Queueing network
Modeling
Optimization
Stochastic programming
Integer programming
system dynamics
Queueing system
Automatic generation
Discrete programming
Max plus algebra
Dynamic model
Discrete event system
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Summary: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.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0030-364X
1526-5463
DOI:10.1287/opre.1080.0559