An adaptive learning approach for no-wait flowshop scheduling problems to minimize makespan

被引:0
|
作者
Engin, Orhan [1 ]
Gunaydin, Cengiz [1 ]
机构
[1] Selcuk Univ, Dept Ind Engn, Fac Engn & Architecture, TR-42079 Konya, Turkey
关键词
No-wait flowshop; Adaptive learning approach; Genetic algorithm; Makespan; NEURAL-NETWORKS; HEURISTIC ALGORITHM; M-MACHINE; OPTIMIZATION; SEARCH; TIME; JOBS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
No-wait flowshop scheduling problem (NW-FSSP) with the objective to minimize the makespan is an important sequencing problem in the production plans and applications of no-wait flowshops can be found in several industries. In a NW-FSSP, jobs are not allowed to wait between two successive machines. The NW-FSSPs are addressed to minimize makespan and the NW-FSSP is known as a NP- Hard problem. In this study, Agarwal et al.'s(1) adaptive learning approach (ALA) is improvement for NW-FSSPs. Improvements in adaptive learning approach is similar to neural-network training. The improvement adaptive learning approach (IALA) is applied to all of the 192 problems. The proposed IALA method for NW-FSSP is compared with Aldowaisan and Allahverdi's(2) results by using Genetic heuristic. The results of computational experiments on randomly generated NW-FSSPs are show that the proposed adaptive learning approach performs quite well.
引用
收藏
页码:521 / 529
页数:9
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