Abstract
Many evolutionary algorithms have been used to solve multi-objective scheduling problems. NSGA-II is one of them that is based on the Pareto optimality concept and generally obtains good results. However, it is possible to improve its performance with some modifications. In this paper, two modified NSGA-II algorithms have been suggested for solving the multi-objective flexible job shop scheduling problem. The neighborhood structures defined for the problem are integrated into the algorithms to create better generations during the iterations. Also, their initial populations are created with an effective heuristic. In the first modified NSGA-II, after the creation of the offspring population, a neighbor of each individual in the parent population is constructed, and then one of them is selected according to the domination state of the solutions. Then the populations are merged to create a new population. In the second modified NSGA-II, only the solutions on the first and second fronts of the parent population and also their neighbors are merged with the offspring population. Other operators of the algorithms like the non-dominated sorting and calculating the crowding distances are as the classic NSGA-II. A comparison is done with a classic NSGA-II based on two metrics. The results show that as it is in the first modified NSGA-II, including neighbors of more individuals of the population provides better results because it increases diversity and intensity of the search. The performance of the second modified NSGA-II is almost similar to the NSGA-II. So, it can be concluded that although integrating the neighborhood structures can improve the performance of search, it is better to define that the structures should be applied to how many and which solutions, in otherwise the quality of search may not increase.
Highlights
Flexible job shop scheduling problem (FJSSP) is an NP-hard problem that has been investigated by many researchers in the recent decades
The algorithms are compared with a classic non-dominated sorting genetic algorithm-II (NSGA-II), whose initial population is created randomly, based on different metrics
The neighborhood structures defined for the FJSSP are integrated into the non-dominated sorting genetic algorithm (NSGA)-II algorithm to increase the diversity and intensity of the search
Summary
Flexible job shop scheduling problem (FJSSP) is an NP-hard problem that has been investigated by many researchers in the recent decades. The algorithms for the multi-objective optimization are widely researched in the scheduling literature. In these algorithms, the diversity and intensity of the search are increased to create better generations. If the parent dominates the child, a new child is created by the Solving the Multi-objective Flexible Job Shop Scheduling Problem mutation operator. The aim of this study is to propose two hybrid algorithms based on the non-dominated sorting genetic algorithm-II (NSGA-II) for solving the multi-objective FJSSP (MO-FJSSP). A neighbor is generated for each individual in the parent population and one of them is selected according to the domination state of them. The algorithms are compared with a classic NSGA-II, whose initial population is created randomly, based on different metrics
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
