Solving the Dynamic Job Shop Scheduling Problem using Bottleneck and Intelligent Agents based on Genetic Algorithm

The Dynamic Job Shop (DJS) scheduling problem is one of the most complex forms of machine scheduling. This problem is one of NP-Hard problems for solving which numerous heuristic and metaheuristic methods have so far been presented. Genetic Algorithms (GA) are one of these methods successfully applied to these problems. In these approaches, of course, avoiding premature convergence, better quality and robustness of solutions is still among the challenging arguments. The adapting of GA operators in amount and range of coverage can operate as an efficient approach in improving its effectiveness. In the proposed GA (GAIA), (1) the adapting in the amount of operators’ algorithm based on the solutions’ tangent rate for premature convergence is done. Then, (2) the adapting in the range of coverage of operators’ algorithm, in first step, happens by operators convergence on Bottleneck Recourses (BR) (which was detected initially) and, in the next step, occurs by operators convergence on the elite solutions so that the search process focuses on more probable areas than the whole space of solution. Comparing the problem results in the static state with the results of other available methods in the literature indicated high efficiency of the proposed method.