Proposition of New Genetic Operator for Solving Joint Production and Maintenance Scheduling: Application to the Flow Shop Problem

Genetic algorithms are used in scheduling leading to efficient heuristic methods for large sized problems. The efficiency of a GA based heuristic is closely related to the quality of the used GA scheme and the GA operators: mutation, selection and crossover. In this paper, we propose a joint genetic algorithm (JGA), for joint production and maintenance scheduling problem in permutation flowshop, in which different genetic joint operators are used. We also proposed a joint structure to represent an individual in with two fields: the first one for production data and the second one for maintenance data. We used different Taillard benchmarks to compare the performances of JGA with each proposed operator