Multi-objective simulation optimization through search heuristics and relational database analysis

The SimMOp framework for generating solutions to simulation optimization problems containing multiple objectives is presented. The complexity within this subject is the conflicting multiple stochastic outputs whose estimates are only available through simulation. The framework combines a simulation...

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Bibliographic Details
Published in:Decision Support Systems 2008-12, Vol.46 (1), p.277-286
Main Authors: Willis, K.O., Jones, D.F.
Format: Article
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Computer simulation
Decision analysis
Decision theory. Utility theory
Exact sciences and technology
Heuristic
Information systems. Data bases
Inventory control, production control. Distribution
Memory organisation. Data processing
Multi-objective optimization
Operational research and scientific management
Operational research. Management science
Optimization algorithms
Optimization techniques
Simulation
Software
Stochastic models
Studies
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Summary:The SimMOp framework for generating solutions to simulation optimization problems containing multiple objectives is presented. The complexity within this subject is the conflicting multiple stochastic outputs whose estimates are only available through simulation. The framework combines a simulation model, a non-exhaustive heuristic search algorithm with an embedded multi-objective optimization technique, and database technologies to generate a set of good quality solutions. The goodness of solutions is measured from a multi-objective and stochastic perspective through analysis after the search phase of the methodology. The methodology has been tested using a discrete-event simulation model based on inventory theory.
ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2008.06.012