Multi-objective Flexible Job-shop Scheduling Problems with Limited Resource Constraints Using Nondominated Sorting Genetic Algorithm II
The unbalance of the machine flexibility and resource limitation for the flexible job-shop scheduling problem(FJSP) has proven it is of utmost importance in the real- world applications. In this paper, to solve the FJSP with limited resource constraints, we propose a method based on the nondominated sorting genetic algorithm II (NSGA-II). In our algorithm, all kinds of strategies, used in population initialization, selection, crossover and mutation, are adopted. When using genetic operators, we always divide chromosomes into two parts: operations sequencing (OS) part and the machine selection (MS) part It can generate better individuals by encoding and decoding, and we find a strong tuning for the biend of two regulations of population initialization through experiments. To test the typical benchmark problems in a targeted manner, the experimental results show the feasibility and effectiveness of the improved NSGA-II, which is aimed at minimizing the makespan, critical machine workload, and total workload of the machines, and it is proved that the algorithm proposed by this paper can And the better solutions.
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Multi-objective Flexible Job-shop Scheduling Problems with Limited Resource Constraints Using Nondominated Sorting Genetic Algorithm II
Semantic Scholar · Engineering · 2018
Abstract
The unbalance of the machine flexibility and resource limitation for the flexible job-shop scheduling problem(FJSP) has proven it is of utmost importance in the real- world applications. In this paper, to solve the FJSP with limited resource constraints, we propose a method based on the nondominated sorting genetic algorithm II (NSGA-II). In our algorithm, all kinds of strategies, used in population initialization, selection, crossover and mutation, are adopted. When using genetic operators, we always divide chromosomes into two parts: operations sequencing (OS) part and the machine selection (MS) part It can generate better individuals by encoding and decoding, and we find a strong tuning for the biend of two regulations of population initialization through experiments. To test the typical benchmark problems in a targeted manner, the experimental results show the feasibility and effectiveness of the improved NSGA-II, which is aimed at minimizing the makespan, critical machine workload, and total workload of the machines, and it is proved that the algorithm proposed by this paper can And the better solutions.