Improved genetic algorithm for the permutation flowshop scheduling problem

Genetic algorithms (GAs) are search heuristics used to solve global optimization problems in complex search spaces. We wish to show that the e6ciency of GAs in solving a &owshop problem can be improved signi7cantly by tailoring the various GA operators to suit the structure of the problem. The &owshop problem is one of scheduling jobs in an assembly line with the objective of minimizing the completion time or makespan. We compare the performance of GA using the standard implementation and a modi7ed search strategy that tries to use problem speci7c information. We present empirical evidence via extensive simulation studies supported by statistical tests of improvement in e6ciency.