Modelling of job-shop scheduling with multiple quantitative and qualitative objectives and an approach which mixes genetic algorithms and tabu search

In this research, an integrated approach to modelling the job shop scheduling problems, along with a genetic algorithm/tabu search mixture solution approach, is proposed. The multiple objective functions modelled include both multiple quantitative (time and production) and multiple qualitative (marketing) objectives. In addition, realistic issues, such as the uncertainty aspect, rescheduling relative importance of criteria, and alternative process plans with the GA/TS approach, are also modelled within the framework of the multi-objective functions, with the aids of fuzzy set theory, the analytic hierarchy process, and dynamic probability distribution. The implementation of the genetic algorithm/tabu search solution approach and the rescheduling scheme is supported and demonstrated by illustrative examples and computational results by this approach.