Abstract
For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.
Highlights
Along with the rapid development of modern manufacturing, science, and technology, consumers’demands are becoming more and more personalized and customized
A hybrid non-dominated sorting simulated annealing algorithm for flexible job-shop scheduling problem (FJSP) was proposed by Shivasankaran et al [4], in which critical or incapable machines were eliminated by non-dominated sorting for all operations and simulated annealing was used to search for an optimal solution
All the genes with a value of 1 corresponding to R in the parent chromosome P2 are assigned to the offspring C2, and the genes copied to C2 are removed in P1, and the remaining genes are sequentially assigned to C2
Summary
Along with the rapid development of modern manufacturing, science, and technology, consumers’. This paper proposes an improved genetic algorithm with an adaptive variable neighborhood search (IGA-AVNS) for solving FJSP. Three neighborhood structures are designed to generate the neighborhood solution and the three neighborhood structures are selected adaptively, that is, the neighborhood with the best search effect is adaptively selected for searching, which greatly reduces the search time and improves search efficiency This makes full use of the characteristics of GA and VNS search, which increases the diversity of the population and prevents the loss of the optimal solution, and accelerates the convergence speed and improves the efficiency of the algorithm.
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