基于多目标根系生长算法的高精铜锭熔炼作业调度
Job scheduling of the smelting process for high-precision copper ingot using multi-objective root growth algorithm
查看参考文献18篇
|
文摘
|
本文提出一种基于植物根系生长行为的自适应多目标算法(multi-objective root growth algorithm, MORGA),用于求解高精度铜铸锭熔炼过程中的作业调度优化问题.首先,根据铜铸锭熔炼生产线现有的生产能力和熔炼工艺,以达到对客户承诺的交货期、降低生产成本的目的,建立以最小化生产总时间和订单未编入计划而受到的总惩罚值为目标的作业调度优化模型.然后,以植物根系分化式生长行为的数学仿真模型为基础,融入多目标优化策略,提出自适应多目标优化算法,设计编码规则,使其能够有效求解高精度铜铸锭熔炼作业调度模型.最后,利用实际生产数据对MORGA进行验证,并与经典多目标优化算法NSGAII和MOPSO比较, MORGA获得了更优的结果. |
|
其他语种文摘
|
This paper proposes a novel multi-objective root growth algorithm (MORGA) based on self-adaptive behavior of plant root growth. It can solve job scheduling optimization problem in the smelting process of high-precision copper ingot. At first, a job scheduling model for optimization is established on the existing production capacity and melting technology of smelting production line for copper ingot. The model is formulated with two objectives of minimizing production time and penalty value for the plans not containing some orders, which can meet clients' delivery date and reduce production cost. Then, MORGA is formulated based on mathematical simulation model for plant root growth behavior with multi-objective strategy. A new encoding rule for the algorithm is designed to solve the job scheduling model effectively. The experiment results using the actual data in production show that MORGA is robust and effective. MORGA can obtain better solutions compared to NSGAⅡand MOPSO when solving the model. |
|
来源
|
控制理论与应用
,2018,35(1):121-128 【核心库】
|
|
DOI
|
10.7641/CTA.2017.60615
|
|
关键词
|
并行机调度
;
多目标优化
;
根系生长
;
高精铜铸锭
;
熔炼过程
|
|
地址
|
中国科学院沈阳自动化研究所, 辽宁, 沈阳, 110016
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-8152 |
|
学科
|
自动化技术、计算机技术 |
|
基金
|
辽宁省自然科学基金
;
辽宁省科技计划项目
|
|
文献收藏号
|
CSCD:6221287
|
参考文献 共
18
共1页
|
|
1.
钟卫佳.
铜加工技术实用手册,2007
|
被引
24
次
|
|
|
|
|
2.
Holsapple C W. A genetics-based hybrid scheduler for generating static schedules in flexible manufacturing contexts.
IEEE Transactions on Systems, Mall and Cybernetics,1993,23(4):953-971
|
被引
3
次
|
|
|
|
|
3.
Pelin A. A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem.
Advances in Engineering Software,2012,45(1):272-280
|
被引
1
次
|
|
|
|
|
4.
Purushothaman D. A simulated annealing algorithm to minimize makespan of parallel batch processing machines with unequal job ready times.
Expert Systems with Applications,2012,39(1):1451-1458
|
被引
6
次
|
|
|
|
|
5.
Cheng B Y. An improved ant colony optimization for scheduling identical parallel batching machines with arbitrary job sizes.
Applied Soft Computing,2013,13(2):765-772
|
被引
1
次
|
|
|
|
|
6.
Torabi S A. A particle swarm optimization for a fuzzy multi-objective unrelated parallel machines scheduling problem.
Applied Soft Computing,2013,13(12):4750-4762
|
被引
5
次
|
|
|
|
|
7.
Xu R. A bi-objective scheduling problem on batch machines via a Pareto-based ant colony system.
International Journal of Production Economics,2013,145(1):371-386
|
被引
4
次
|
|
|
|
|
8.
Xu X L. Adaptive immune algorithm for solving Job-shop scheduling problem.
Advances In Natural Computation, Pt 2, Lecture Notes In Computer Science. 3611,2005:795-799
|
被引
1
次
|
|
|
|
|
9.
Das S. Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications.
Studies in Computational Intelligence,2009,203(3):23-55
|
被引
2
次
|
|
|
|
|
10.
郑晓龙. 求解置换流水线调度问题的混合离散果蝇算法.
控制理论与应用,2014,31(2):159-164
|
被引
23
次
|
|
|
|
|
11.
Sakawa M. An interactive fuzzy satisficing method for multiobjective linear programming problems and its application.
IEEE Transactions on Systems, Man, and Cybernetics,1987,17(4):654-661
|
被引
5
次
|
|
|
|
|
12.
Mainzer K.
Thinking in Complexity: the Complex Dynamics of Matter, Mind and Mankind. Third Edition,1997
|
被引
1
次
|
|
|
|
|
13.
王东生.
混沌、分形及其应用,1995
|
被引
178
次
|
|
|
|
|
14.
Deb K. A fast and elitist multiobjective genetic algorithm: NSGA-II.
IEEE Transactions on Evolutionary Computation,2002,6(2):182-197
|
被引
3013
次
|
|
|
|
|
15.
Qu B Y. Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection.
Information Sciences,2010,180(17):3170-3181
|
被引
7
次
|
|
|
|
|
16.
Qian B. An effective hybrid DEbased algorithm for multi-objective flow shop scheduling with limited buffers.
Computers & Operations Research,2009,36(1):209-233
|
被引
33
次
|
|
|
|
|
17.
Goldberg D E. Alleles, loci and the TSP.
Proceedings of the First International Conference on Genetic Algorithms and Their Applications,1985:154-159
|
被引
6
次
|
|
|
|
|
18.
Carlos A C C. Handling multiple objectives with particle swarm optimization.
IEEE Transactions on Evolutionary Computation,2004,8(2):256-279
|
被引
475
次
|
|
|
|
|
了解气象卫星更多信息，请访问