Publisher Summary
The chapter focuses on the working of Genetic algorithms (GAs) by illustrating a complex crew scheduling system. The core technologies and methods used in the crew scheduler are found throughout the modern enterprise, government agencies, and the military. The Crew Scheduler is built on multi-objective, multi-constraint resource allocation system. It solves very large problems with very high numbers of constraints under multiple objective conditions by using advanced optimization services that is implemented through genetic algorithms and evolutionary programming. A genetic crew scheduler model consists of a master plan, job sequencer, crew scheduler, genetic optimizer, resource constrained scheduler, precedence analyzer, auditing facility, and the crew schedules. The chapter discusses the implementation and execution of the genetic crew scheduler by using a specific java implementation, which is less robust and extensive than the architecture. The chapter also describes formulas to measure fitness, convergence and the effect of constraints on the problem as the genetic search engine works toward a solution. The chapter discusses graph theory and algorithms for implementing precedence and other topology constraints. There are several ways to represent precedence graph: dependency matrix, sparse matrix representations, graph traversal algorithms, and topological sorts.
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