A Trajectory-Based Immigration Strategy Genetic Algorithm to Solve a Single-Machine Scheduling Problem with Job Release Times and Flexible Preventive Maintenance

Abstract

This paper considers the single-machine problem with job release times and flexible preventive maintenance activities to minimize total weighted tardiness, a complicated scheduling problem for which many algorithms have been proposed in the literature. However, the considered problems are rarely solved by genetic algorithms (GAs), even though it has successfully solved various complicated combinatorial optimization problems. For the problem, we propose a trajectory-based immigration strategy, where immigrant generation is based on the given information of solution extraction knowledge matrices. We embed the immigration strategy into the GA method to improve the population’s diversification process. To examine the performance of the proposed GA method, two versions of GA methods (the GA without immigration and the GA method with random immigration) and a mixed integer programming (MIP) model are also developed. Comprehensive experiments demonstrate the effectiveness of the proposed GA method by comparing the MIP model with two versions of GA methods. Overall, the proposed GA method significantly outperforms the other GA methods regarding solution quality due to the trajectory-based immigration strategy.

A Mayfly-Based Approach for CMOS Inverter Design with Symmetrical Switching

“…There are various optimization algorithms available in the literature. Gradient descent [15,16], genetic algorithms [17][18][19], RMSProp [20], and many other optimization algorithms have been used to improve the efficiency and accuracy of solving complex problems. In the published literature, different evolutionary optimization methods have been used to design inverters with optimal switching characteristics.…”