Novel Evolutionary Computation Approaches to Scheduling and Timetabling

Dear Colleagues,

Scheduling and Timetabling problems are complex combinatorial optimization problems of enormous practical and economic importance. This issue focuses on the application of novel evolutionary computation methods to solve scheduling and timetabling problems. Evolutionary Computation, a machine learning technique, is based on Darwin's theory of evolution and as such iteratively refines an initial population of potential solutions using the processes of evaluation, selection and regeneration in an attempt to find solutions to (complex) optimization problems. The use of Evolutionary Computation techniques to solve scheduling and timetabling problems is a growing field and EC heuristics have been successfully applied to various subdomains including educational timetabling, transportation scheduling, personnel scheduling, job shop scheduling and sports scheduling amongst others.

Given the high processing times typical of evolutionary computing techniques in solving these problems, parallel EC methods and implementations are a recent trend of great importance. Authors are invited to submit manuscripts reporting original research on the application of EC to scheduling and timetabling problems and its efficient implementation.

Professor Wolfgang Banzhaf
Dr. Nelishia Pillay
Guest Editors

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