Abstract
NSGA-II is an evolutionary multi-objective optimization algorithm that has been applied to a wide variety of search and optimization problems since its publication in 2000. This study presents a review and bibliometric analysis of numerous NSGA-II adaptations in addressing scheduling problems. This paper is divided into two parts. The first part discusses the main ideas of scheduling and different evolutionary computation methods for scheduling and provides a review of different scheduling problems, such as production and personnel scheduling. Moreover, a brief comparison of different evolutionary multi-objective optimization algorithms is provided, followed by a summary of state-of-the-art works on the application of NSGA-II in scheduling. The next part presents a detailed bibliometric analysis focusing on NSGA-II for scheduling applications obtained from the Scopus and Web of Science (WoS) databases based on keyword and network analyses that were conducted to identify the most interesting subject fields. Additionally, several criteria are recognized which may advise scholars to find key gaps in the field and develop new approaches in future works. The final sections present a summary and aims for future studies, along with conclusions and a discussion.
Highlights
Non-Dominated Sorting Genetic Algorithm II (NSGA-II) [1] has been proposed as a powerful decision space exploration engine based on a genetic algorithm for solving multi-objective optimization problems
This paper presents a review of the application of NSGA-II in scheduling problems
This paper presents a comprehensive review of NSGA-II applied to different scheduling problems
Summary
Non-Dominated Sorting Genetic Algorithm II (NSGA-II) [1] has been proposed as a powerful decision space exploration engine based on a genetic algorithm for solving multi-objective optimization problems. The NSGA-II algorithm has been applied to a wide variety of search and optimization problems since its publication in 2000. Two major schools of thought in relation to schedule generation are algorithmic and knowledge-based approaches [2]. The first approach is based on a mathematical formulation that includes objective function(s) and constraints, while the second approach is not easy to explain in an analytical format and is often used in cases where a feasible solution is sufficient. Scheduling problems are generally known to be complex, large-scale, challenging, NP-hard, and involve several constraints [3,4]
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