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 machinescheduling. This problem is one of NP-Hard problems for solving which numerous heuristic andmetaheuristic methods have so far been presented. Genetic Algorithms (GA) are one of these methodssuccessfully applied to these problems. In these approaches, of course, avoiding prematureconvergence, better quality and robustness of solutions is still among the challenging arguments. Theadapting of GA operators in amount and range of coverage can operate as an efficient approach inimproving 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) theadapting in the range of coverage of operators’ algorithm, in first step, happens by operatorsconvergence on Bottleneck Recourses (BR) (which was detected initially) and, in the next step, occursby operators convergence on the elite solutions so that the search process focuses on more probableareas than the whole space of solution. Comparing the problem results in the static state with theresults of other available methods in the literature indicated high efficiency of the proposed method.