求解改进布谷鸟算法的置换流水车间调度问题

doi:10.16180/j.cnki.issn1007-7820.2019.10.012

Abstract

Abstract:

Aiming at the problem of displacement flow shop scheduling under actual working conditions, the standard cuckoo algorithm was improved with the goal of minimizing the completion time. In order to improve the stability of the optimized solution and the computational accuracy of the algorithm, the proposed algorithm introduced the elimination probability into the dynamic adaptive mechanism. Besides, the proposed algorithm also introduced the local search into the differential evolution mechanism, and applied the NEH algorithm in the initial population generation. The improved cuckoo search was applied to solve the permutation flow shop scheduling problem under actual working conditions. The results showed that compared with the simulation optimization results of the standard cuckoo search, the improved cuckoo search had better stability and higher precision.

Key words: PFSP, cuckoo search, elimination probability, local search, differential evolution mechanism, makespan.

CLC Number:

TP301

BING Xiaofeng,TAO Yifei,DONG Yuanyuan,SUN Sihan. Solving the Problem of Replacement Flow Shop Scheduling Based on Improved Cuckoo Algorithm[J].Electronic Science and Technology, 2019, 32(10): 60-64.

Figures/Tables 8

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Figure 6.

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Job	M₁	M₂	M₃	M₄	M₅	M₆
1	1,0.5²	2,0.5²	1,0.3²	1,0.5²	8,0.2²	7,0.5²
2	1,0.2²	1,0.2²	1,0.5²	3,0.5²	10,0.5²	8,0.2²
3	2,0.5²	2,0.5²	2,0.3²	2,0.2²	5,0.2²	6,0.5²
4	3,0.5²	3,0.3²	1,0.2²	1,0.5²	7,0.5²	6,0.3²
5	5,0.8²	2,0.2²	1,0.3²	2,0.5²	5,0.2²	7,0.5²
6	3,0.5²	1,0.5²	2,0.5²	4,0.3²	3,0.5²	8,0.5²
7	1,0.3²	2,0.5²	2,0.3²	2,0.5²	5,0.2²	9,0.3²
8	1,0.5²	1,0.3²	1,0.5²	5,0.3²	10,0.5²	7,0.5²
9	1,0.3²	4,0.5²	1,0.3²	5,0.5²	5,0.2²	7,0.5²
10	3,0.5²	1,0.5²	2,0.5²	4,0.5²	7,0.5²	9,0.3²

性能指标	布谷鸟算法	改进后的布谷鸟算法
Min	23 798	22 600
Avg	24 159.6	23 469.2
Avg-FI	28.15	21.5
SD	317.69	304.80
Cousume	599.05	579.75

Solving the Problem of Replacement Flow Shop Scheduling Based on Improved Cuckoo Algorithm

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4	20	15	10	5	40	30
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Job	M₁	M₂	M₃	M₄	M₅	M₆
1	10	20	15	5	5	30
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3	30	60	90	10	20	50
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Job	M₁	M₂	M₃	M₄	M₅	M₆
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3	30	60	90	10	20	50
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