A Genetic Algorithm Approach for Modelling and Optimisation of MAJSP- Part II: GA operators and results

This paper, as a continuation of A Genetic Algorithm Approach for Modelling and Optimisation of MAJSP-Part1: Representation, will focus on Multi-Attribute Job-Shop Scheduling Problem (MAJSP). MAJSP is an extension of classical JSP. It represents more realistic scheduling problems since more attributes for jobs are included. The objective is to investigate how the changes in GA operators may affect the optimal fitness value (profit) for algorithms 7011 presented in the previous part. The GA operators presented here include selection and crossover. Since every machine is capable of performing a predefined set of jobs, it is critical to keep in mind that the operators should be designed in a way that feasibility of schedules never becomes violated. The rest of the algorithms are designed according to these assumptions and the results are compared.