Improved Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem
Улучшенный генетический алгоритм для решения задачи составления расписания в гибком цехе
2020-01-01
SCID: 54.1/2wz76c56
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convergence speedflexible job shop scheduling problemimproved genetic algorithminitial population generationsingle-point mutation
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Abstract (AI)
The Genetic algorithm is one of the effective methods to solve flexible job shop scheduling problems. An improved genetic algorithm is proposed to overcome the shortcomings of traditional genetic algorithm, such as weak searching ability and long running time when solving FJSP. There are two main improvements. First, the algorithm adopted a new generation mechanism to produce the initial population, which could accelerate the convergence speed of the algorithm. Second, a new single-point mutation operation is designed to avoid the occurrence of illegal solutions, thus reducing the running time of the algorithm. The simulation results proved that the improved algorithm has better performance than some other algorithms.
Key Findings
1
A new initial-population generation mechanism is introduced to accelerate convergence.
2
A redesigned single-point mutation operation prevents illegal solutions and reduces algorithm runtime.
3
An improved genetic algorithm is proposed for flexible job shop scheduling to address traditional algorithms’ weak search capability and long runtime.
4
Simulation results show that the improved genetic algorithm outperforms some other compared algorithms.
Research Object
flexible job shop scheduling problem
Research Subject
the search effectiveness, convergence speed, solution validity, and running time of genetic algorithms for solving the flexible job shop scheduling problem
Publication Details
Publication Date
2020-01-01
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