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浙江大学学报(工学版)  2019, Vol. 53 Issue (9): 1681-1688    DOI: 10.3785/j.issn.1008-973X.2019.09.006
机械工程     
考虑堆垛机加减速的节能自动立库设计
欧阳永强(),张新艳*()
同济大学 机械与能源工程学院,上海 201804
Design of energy-saving automated storage and retrieval system considering acceleration and deceleration of storage and retrieval machine
Yong-qiang OUYANG(),Xin-yan ZHANG*()
School of Mechanical and Power Engineering, Tongji University, Shanghai 201804, China
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摘要:

以料箱式自动立库为研究对象,考虑堆垛机实际作业中存在的加减速过程,以能耗、平均吞吐时间和成本的计算模型为目标函数,以货架数量、货格数量和堆垛机的运行参数为决策变量,建立自动立库存储区多目标优化模型. 采用2种方法求解该多目标的混合整数非线性规划模型问题:使用经典遗传算法进行求解,得到不同权重下的解集;使用非支配排序遗传算法求取pareto解,得到非劣解的解集. 这些解集是优化后决策变量的集合,根据这些参数组合可以确定存储区的大小,并辅助货架与堆垛机的选购. 依据该模型的优化结果,可以设计出规划更合理的自动立库存储区,可为节能自动立库的建造提供有效参考.

关键词: 自动立库(AS/RS)存储区设计多目标问题能耗参数优化加减速    
Abstract:

A multi-objective optimization model studying storage area in mini-load AS/RS (automated storage and retrieval system) was raised. Take mini-load AS/RS as the research object, calculation models of energy consumption, average throughput time and cost of mini-load AS/RS as objective functions, and number of channels, number of storage compartment and operating parameters of the S/R (storage and retrieval) machine as the decision variables. Two methods were applied to solve the multi-objective mixed integer nonlinear programming model problem. One method used the classical genetic algorithm to get the solution set under different weights. Another method used non-dominated sorting genetic algorithm to obtain the pareto solution set. The solution set was the collection of optimized parameters, which determined the dimensions of storage system and helped to choose shelves and S/R machines. According to the optimal results of the model, storage area in AS/RS can be designed, which can provide reference for the construction of energy-saving AS/RS.

Key words: automated storage and retrieval system (AS/RS)    design of storage area    multi-objective problem    energy consumption    parameter optimization    acceleration and deceleration
收稿日期: 2018-07-19 出版日期: 2019-09-12
CLC:  TH 165.1  
通讯作者: 张新艳     E-mail: tjjxoyyq@163.com;alicezxy@tongji.edu.cn
作者简介: 欧阳永强(1997—),男,硕士生,从事仓库节能研究. orcid.org/0000-0003-4836-7568. E-mail: tjjxoyyq@163.com
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引用本文:

欧阳永强,张新艳. 考虑堆垛机加减速的节能自动立库设计[J]. 浙江大学学报(工学版), 2019, 53(9): 1681-1688.

Yong-qiang OUYANG,Xin-yan ZHANG. Design of energy-saving automated storage and retrieval system considering acceleration and deceleration of storage and retrieval machine. Journal of ZheJiang University (Engineering Science), 2019, 53(9): 1681-1688.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.09.006        http://www.zjujournals.com/eng/CN/Y2019/V53/I9/1681

图 1  自动立库存储区俯视图示意图
图 2  堆垛机的复合作业过程示意图
参数 取值 单位 参数 取值 单位
? 0.9 ? TIt 15 year
tyear 4 800 h C1 120 000
m 1 000 kg C2 20
g 9.8 m/s2 C3 400
kr 0.01 ? C4 1.2
kir 1.15 ? W 2.2 m
lg 0.5 m M 20 000 ?
hg 0.5 m N 8 000 ?
T 10 s ? ? ?
表 1  多目标优化模型中常量的取值
变量 x1 x2 x3 x4/
(m·s?1)
x5/
(m·s?1)
x6/
(m·s?2)
x7/
(m·s?2)
对应参数 naisle n1 nh $v_{\rm{l}}^{{\rm{max}}}$ al $v_{\rm{y}}^{{\rm{max}}}$ ah
取值下限 1 10 4 1 1 1 1
取值上限 30 160 40 6.5 6.5 3.5 3.5
表 2  多目标优化模型中决策变量的取值范围
优化结果比较 E Tn C
$E$最优化 202 960 3.464 5 928 484
(?58.38%) (74.02%) (?128.88%)
${T_n}$最优化 1 931 153 0.338 48 345 948
(83.35%) (?166.27%) (71.93%)
$C$最优化 202 960 3.464 5 928 484
(22.45%) (?0.11%) (?11.36%)
多目标优化 321 450 0.900 13 569 000
表 3  单目标优化和多目标优化的结果比较
优化对象 决策变量 目标函数 最优化程度
x1 x2 x3 x4 x5 x6 x7 E Tn C y
GA求解结果 30 21 16 1.42 1.00 1.15 1.26 321 450 0.9 13 569 000 1.641 0
lingo求解结果 30 20 17 1.33 1.14 1.33 1.14 324 109 0.9 13 441 980 1.639 9
表 4  采用GA与lingo所求最优解的比较
图 3  使用非支配排序遗传算法(NSGA-II)得到的pareto解
图 4  pareto前沿中的成本和能耗的关系
优化结果比较 E Tn C
E最优化 202 960 3.464 5 928 484
(?138.40%) (80.66%) (?159.49%)
Tn最优化 1 931 153 0.338 48 345 948
(74.94%) (?98.22%) (68.18%)
C最优化 202 960 3.464 5 928 484
(?138.40%) (80.66%) (2.52%)
双目标优化 494 400 0.650 15 782 416
(2.13%) (?3.08%) (2.52%)
多目标优化 483 862 0.670 15 383 918
表 5  多目标优化与其他优化方法的结果比较
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