Evolutionary Computation in Scheduling

Gandomi, Amir H.; Emrouznejad, Ali; Rahimi, Iman

doi:10.1002/9781119574293.ch1

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Although numerous techniques have been proposed to solve the optimization problem mentioned above, there is still a crucial need for more suitable techniques. A viable method to manage these issues is to employ global optimization algorithms, including exact optimization methods (e.g., branch-and-bound and branch-and-cut) and, in some cases, evolutionary computation (EC) techniques [5][6][7][8][9]. EC techniques have been employed for large, complex real-world problems that cannot be solved using classical methods [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Scheduling by NSGA-II: Review and Bibliometric Analysis

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%