The AFSA-GA Algorithm for the Quay Crane Scheduling Problem of the Loading and Unloading Operations

Liu, Yi; Shahbazzade, Sabina; Wang, Jian

doi:10.4018/ijssci.2017070104

Cited by 1 publication

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“…So according to this view, these scheduling systems do not thoroughly help the scheduling staff if the amount of scheduling data is very large 2 Complexity [12]. As mentioned earlier, many researchers have already laid the research foundation for the scheduling problem, and through their extensive research on the scheduling problem, several heuristic algorithms have been used to solve the scheduling problem in recent years [13]. e mainstream algorithms for solving the scheduling problem are simulated annealing algorithm, expert system, ant colony algorithm, etc.…”

Section: Related Workmentioning

confidence: 99%

Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

2021

Complexity

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In this paper, an improved genetic algorithm is designed to solve the above multiobjective optimization problem for the scheduling problem of college English courses. Firstly, a variable-length decimal coding scheme satisfying the same course that can be scheduled at different times, different classrooms, and different teaching weeks per week is proposed, which fully considers the flexibility of classrooms and time arrangements of the course and makes the scheduling problem more reasonable. Secondly, a problem-specific local search operator is designed to accelerate the convergence speed of the algorithm. Finally, under the framework of optimal individual retention, the selection operator, crossover operator, and variation operator are improved. It is experimentally demonstrated that the designed algorithm not only has a faster convergence speed but also improves the diversity of individuals to a certain extent to enhance the search space and jump out of the local optimum. Research shows that the improved genetic algorithm has improved average fitness value and time compared with traditional genetic algorithm. At the same time, the use of the largest fuzzy pattern algorithm effectively solves the conflict problem of college English lesson scheduling, thereby improving the solution of college English lesson scheduling. Through the research of this article, the management system of college English course scheduling has been made more intelligent, and the rational allocation of teaching resources and the completion of education and teaching plans have been improved.

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Section: Related Workmentioning

confidence: 99%

Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

2021

Complexity

View full text Add to dashboard Cite

show abstract

The AFSA-GA Algorithm for the Quay Crane Scheduling Problem of the Loading and Unloading Operations

Cited by 1 publication

References 9 publications

Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

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