Abstract
Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost and quality as optimization objectives. Currently, energy- efficiency is also taking into consideration in these problems. However, this problem is NP-Hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version
of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates. This problem represents an extension of the classical job-shop scheduling problem, where each operation has
to be executed by one machine and this machine can work at different speeds. The evaluation section shows that the powerful commercial tools for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile
our genetic algorithm was able to solve all instances with a good solution quality.
