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In-Depth Information
Table 4.
(
Continued
)
reC31(50x10)
0.1
118850/
127140
/137360
174640/
197070
/212550
0.3
127040/
135530
/154350
163770/
180080
/191750
0.5
129470/
141750
/155910
148820/
172950
/183590
0.7
132280/
145110
/154160
135600/
159930
/178970
0.9
141350/
153370
/166680
147570/
160000
/178390
hel1(100x10)
0.1
54972/
59251
/62630
81188/
83072
/84658
0.3
60079/
61488
/63151
78055/
80143
/83015
0.5
61398/
62902
/65205
73912/
76444
/78183
0.7
63148/
65391
/66380
70617/
73361
/77638
0.9
63853/
68679
/70562
66468/
70733
/73478
From the tables, we can see that the best solution found by ISFLA is better than
that of SFLA. Even the worst value is better than that of SFLA. For different the
fuzzy
α
-cuts, the average value of the objective obtained by the proposed method
ISFLA in the optimal programming model is smaller than that of the SFLA method,
which is also appeared in the worst programming model. Even for the large-scale
scheduling problems, it also performed well than SFLA. From those above
comparison results, the proposed ISFLA had superior performance than SFLA with
respect to convergence and stability.
5
Conclusion
This paper proposes an improved evolutionary algorithm based on the general SFLA
method to solve the fuzzy zero-wait scheduling problem with due dates in multi-
product batch processes. In the ISFLA, a new frog leaping rule is considered for
modification of the general SFLA. And the strategy of Forced Moving of the worst
frog in each sub-memeplex is proposed to increase the diversity of memeplexes. The
fuzzy mathematical model is proposed to denote the uncertain zero-wait scheduling
problems based on the fuzzy cut-set theory. According to simulation results, the
proposed algorithm reaches a much better optimal solution in comparison with the
SFLA, and proved the feasibility. Furthermore, the proposed method can also be used
to solve other complex problems.
Acknowledgments.
This work was supported by National Natural Science
Foundation of China (Grant No. 61174040, 61104178), Fundamental Research Funds
for the Central Universities.
References
1.
Mokeddem, D., Khellaf, A.: Multicriteria Optimization of Multiproduct Batch Chemical
Process Using Genetic Algorithm. J. Food Process Eng. 33, 979-991 (2010)
2.
Kim, M., Jung, J.H., Lee, I.B.: Optimal Scheduling of Multiproduct Batch Processes for
Various Intermediate Storage Policies. Ind. Eng. Chem. Res. 35, 4058-4066 (1996)
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