Scheduling by NSGA-II: Review and Bibliometric Analysis

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.

An Integrated Flow Shop Scheduling Problem of Preventive Maintenance and Degradation With an Improved NSGA-II Algorithm

“…In order to evaluate the optimization effect of algorithms, the spacing distribution SD and the scattering range index SS of three algorithms were calculated according to Equation (32) to Equation (34). sim T is simulation time.…”

“…The present review of the above-mentioned literatures reveals that despite the increasing complexity of scheduling problems, NSGA-II is more suitable for finding efficient solutions or near-optimal solutions. Moreover, the NSGAII algorithm has been widely used to solve the job-shop scheduling problems because of its advantages, such as high efficiency to optimize the complex problems and the ability to gain widespread Pareto-optimal solutions [39]. Therefore, this paper chooses NSGA-II to solve the job-shop scheduling problem.…”

An intelligent energy management system for optimum design and real-time operation

“…part of the most dominant evolutionary algorithms in the field of multi-objective optimization. It is the most studied algorithm in scheduling since 2014 [22]. NSGA-II is based on the notion of non-dominance, which makes it possible to assign the solutions of a population to the different Pareto fronts and is characterized by a high diversity of results, thanks to the congestion distance and elitism.…”

Examining the developments in scheduling algorithms research: A bibliometric approach

“…The use of bibliometrics in different disciplines has continued to increase. Studies similar to this study ( Dao et al., 2017 ; Rahimi et al., 2022 ; Shishido and Estrella, 2018 ; Yu et al., 2018 ) have carried out a bibliometric analysis of the genetic algorithm, grids, and clouds, Cloud Computing Technology, and Non-dominated Sorting Genetic Algorithm II (NSGA II). The systematic review helps to gain new insight and uncover meaningful knowledge from cumulative data of a research field ( Radhakrishnan et al., 2017 ).…”