Name
Title | ||
|---|---|---|
An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers |
Abstract | ||
|---|---|---|
In this paper, an effective hybrid discrete differential evolution (HDDE) algorithm is proposed to minimize the maximum completion time (makespan) for a flow shop scheduling problem with intermediate buffers located between two consecutive machines. Different from traditional differential evolution algorithms, the proposed HDDE algorithm adopted job permutation to represent individuals and applies job-permutation-based mutation and crossover operations to generate new candidate solutions. Moreover, a one-to-one selection scheme with probabilistic jumping is used to determine whether the candidates will become members of the target population in next generation. In addition, an efficient local search algorithm based on both insert and swap neighborhood structures is presented and embedded in the HDDE algorithm to enhance the algorithm's local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It shows that the proposed HDDE algorithm is not only capable to generate better results than the existing hybrid genetic algorithm and hybrid particle swarm optimization algorithm, but outperforms two recently proposed discrete differential evolution (DDE) algorithms as well. Especially, the HDDE algorithm is able to achieve excellent results for large-scale problems with up to 500 jobs and 20 machines. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.ins.2010.10.009 | Inf. Sci. |
Keywords | Field | DocType |
intermediate buffer,efficient local search algorithm,discrete differential evolution,flow shop scheduling,better result,traditional differential evolution algorithm,existing hybrid genetic algorithm,computational simulation,effective hybrid discrete differential,hdde algorithm,hybrid particle swarm optimization,proposed hdde algorithm,local search algorithm,computer simulation,hybrid algorithm,local search,makespan,differential evolution | Hybrid algorithm,Artificial intelligence,Population-based incremental learning,Genetic algorithm,Particle swarm optimization,Mathematical optimization,Job shop scheduling,Flow shop scheduling,Algorithm,Differential evolution,Local search (optimization),Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
181 | 3 | 0020-0255 |
Citations | PageRank | References |
40 | 1.05 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quan-Ke Pan | 1 | 1134 | 35.46 |
| Ling Wang | 2 | 2745 | 165.98 |
| Liang Gao | 3 | 1493 | 128.41 |
| W. D. Li | 4 | 112 | 5.59 |