A mixed-integer nonlinear programming approach to analog circuit synthesis
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Expert Systems: Principles and Programming
Expert Systems: Principles and Programming
Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems
INFORMS Journal on Computing
Compile-time dynamic voltage scaling settings: opportunities and limits
PLDI '03 Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation
Multi-objective pump scheduling optimisation using evolutionary strategies
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
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Compressor selection is one of the primary functions in operation of natural gas pipelines, and a major concern of the task is to minimize operating costs. This study presents a comparison of three automation techniques for compressor selection: mixed integer linear programming, genetic algorithms, and expert systems. In compressor selection, dispatchers often turn on/off compressor units based on the status of the pipeline and the anticipated customer demand. Since a novice dispatcher often performs this task on a trial-and-error basis without any guarantee of optimal operations, it is desirable to develop a decision support system that can select compressors based on the available data. This study presents a comparison of three automation techniques for incorporation into a decision support system. Based on parameter values for one section of the gas pipeline at the St. Louis East area in Saskatchewan, Canada, a comparison of the strengths and weaknesses of the three automation techniques as well as the recommendations they gave are discussed.