Shelf Layout and Equipment Configuration Strategy of Four-Way Shuttle Stereoscopic Warehouse

doi:10.12141/j.issn.1000-565X.240327

Abstract

Abstract:

Four-way Shuttle Automated Storage and Retrieval System (FWS-AS/RS) is a storage form widely used in industries such as e-commerce, pharmaceuticals, and food in recent years. It has the characteristics of flexible system configuration, high storage density, high efficiency, and high automation. To efficiently design the shelves layout and equipment configuration of FWS-AS/RS, this paper firstly considered the possibility of arranging shelf rows, columns, and layers under different storage scales and discussed the impact of changes in horizontal aisle layout position and quantity as well as different shelf depths on operational efficiency. Then, taking the different configurations of shuttle cars and lifts as well as the different placement positions of lifts and input/output (I/O) ports as variables, it established the motion models of shuttle cars and lifts considering acceleration, deceleration, no-load, load energy consumption, and energy recovery during braking; it used the total cost, transport distance, energy consumption, and space utilization rate as evaluation indicators. Through simulation experiments, regular design strategies were obtained for constructing four-way shuttle style stereoscopic warehouse shelves, including row, column, layer, depth, tunnel position, number and position of I/O ports, the ratio of four-way shuttle cars (FWS) to lifts, and the relationship between lifts and longitudinal tunnels. Finally, taking a storage capacity of 5 000 as an example, the study applied these strategies to design simulation. The simulation results show that the three optimization schemes reduced the transport distance, energy consumption, total cost, and floor area by an average of 43.30%, 57.69%, 11.17%, and 8.60%, while increasing the space utilization rate by an average of 5.66%, thus verifying the correctness of the design strategy. The design strategy can provide reference for the construction and operation of such stereoscopic warehouses.

Key words: stereoscopic warehouse, four-way shuttle, shelf layout, equipment configuration

CLC Number:

TP391

LI Jianguo, GONG Xincheng. Shelf Layout and Equipment Configuration Strategy of Four-Way Shuttle Stereoscopic Warehouse[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(3): 68-79.

Figures/Tables 12

Fig.1

Fig.2

Table 1

Four types of operation"

序号	条件	描述
1	$z t i = z F W S j = z I / O k$	FWS、I/O口及任务同层
2	$z t i = z I / O k ≠ z F W S j$	I/O口与任务同层
3	$z t i = z F W S j ≠ z I / O k$	FWS与任务同层
4	$z t i ≠ z F W S j ≠ z I / O k$	FWS、I/O口及任务都不同层

Table 1

Fig.3

Table 2

Simulation results of operation efficiency and shelf parameters"

方案	$N R R$	$N R C$	$N R Z$	$N S K U$ /SKU	$η t h$ /（SKU‧h ^-1）	S/km	$S h$ /km	$S v$ /km	E/MJ	$E h$ /MJ	$E v$ /MJ
1	6	8	6	588	55.54	17.39	13.35	4.04	18.61	13.26	5.35
2	14	7	3	594	71.68	18.99	17.27	1.72	21.35	18.96	2.39
3	10	6	5	610	57.81	17.93	14.47	3.46	19.14	14.44	4.70
4	12	5	5	610	59.68	18.08	14.70	3.38	19.59	15.09	4.50
5	10	6	5	610	58.23	17.88	14.47	3.41	19.01	14.44	4.57
6	12	5	5	610	60.07	18.10	14.68	3.42	19.70	15.08	4.62
7	13	8	3	630	69.41	20.62	18.79	1.83	23.15	20.61	2.54
8	13	3	8	640	53.62	20.16	14.00	6.16	22.75	14.60	8.15
9	15	11	3	996	66.59	39.07	36.14	2.93	46.75	42.64	4.11
10	50	5	2	1 004	77.11	63.86	62.33	1.53	87.92	85.73	2.19
11	10	25	2	1 004	81.16	54.09	52.57	1.52	71.08	68.91	2.17
12	20	5	5	1 010	55.65	38.07	32.43	5.64	45.18	37.62	7.56
13	10	10	5	1 010	55.08	35.75	30.10	5.65	40.67	33.08	7.59
14	21	6	4	1 016	57.42	39.63	35.33	4.30	47.25	41.42	5.83
15	10	5	10	1 020	48.93	35.10	22.52	12.58	38.72	22.13	16.59
16	12	6	7	1 022	50.78	34.70	26.29	8.41	38.37	27.26	11.11
17	17	6	5	1 030	54.31	37.47	31.69	5.78	43.25	35.47	7.78
18	17	5	6	1 032	53.52	37.26	30.04	7.22	43.27	33.55	9.72
19	16	8	4	1 032	58.41	38.25	33.85	4.40	44.52	38.53	5.99
20	15	13	15	5 880	37.38	342.83	231.27	111.56	424.93	279.24	145.69
21	27	22	5	5 950	42.34	419.75	386.31	33.44	568.54	523.44	45.10
22	60	10	5	6 010	38.70	513.15	479.46	33.69	720.33	675.02	45.31
23	20	30	5	6 010	41.10	453.88	420.24	33.64	622.85	577.64	45.21
24	20	15	10	6 020	39.31	359.22	285.10	74.12	459.45	361.83	97.62
25	30	10	10	6 020	38.32	374.49	300.24	74.25	484.30	386.34	97.96
26	25	8	15	6 030	36.41	366.53	251.70	114.83	460.44	309.96	150.48
27	10	15	20	6 040	34.86	381.09	225.47	155.62	472.63	269.28	203.35
28	30	5	20	6 040	34.39	409.60	254.62	154.98	523.13	321.41	201.72
29	44	14	5	6 170	41.05	465.86	431.39	34.47	639.31	593.06	46.25
30	22	15	15	9 930	34.96	678.91	490.21	188.70	875.07	628.30	246.77
31	30	11	15	9 930	34.66	698.20	509.72	188.48	907.62	661.39	246.23
32	40	25	5	10 010	38.28	881.13	824.99	56.14	1 237.71	1 162.16	75.55
33	50	20	5	10 010	37.60	905.80	849.94	55.86	1 277.17	1 202.32	74.85
34	25	20	10	10 020	35.96	723.87	600.19	123.68	964.23	800.96	163.27
35	20	25	10	10 020	35.64	748.57	625.27	123.30	1 004.74	842.41	162.33
36	50	10	10	10 020	33.97	822.73	699.56	123.17	1 125.88	963.89	161.99
37	25	10	20	10 040	32.93	708.63	450.45	258.18	900.74	563.98	336.76
38	45	28	4	10 088	39.20	969.97	927.09	42.88	1 382.60	1 324.37	58.23
39	55	23	4	10 128	38.90	998.91	955.95	42.96	1 429.50	1 371.26	58.24
40	40	13	10	10 420	35.36	798.66	670.59	128.07	1 078.82	910.40	168.42
41	20	13	20	10 440	33.35	731.15	462.95	268.20	927.38	577.89	349.49
42	30	50	5	15 010	32.98	1 734.57	1 650.49	84.08	2 521.36	2 408.32	113.04
43	100	15	5	15 010	30.57	1 996.04	1 911.91	84.13	2 949.27	2 836.10	113.17
44	50	15	10	15 020	32.78	1 347.19	1 162.29	184.90	1 868.95	1 625.46	243.49
45	30	25	10	15 020	33.62	1 272.08	1 087.63	184.45	1 744.69	1 502.33	242.36
46	100	5	15	15 030	25.67	1 971.37	1 686.03	285.34	2 869.50	2 496.64	372.86
47	10	50	15	15 030	27.99	1 636.64	1 351.60	285.04	2 310.62	1 938.56	372.06
48	38	10	20	15 240	30.58	1 272.72	882.10	390.62	1 681.59	1 173.64	507.95
49	20	19	20	15 240	31.56	1 204.79	813.60	391.19	1 570.85	1 061.44	509.41

Table 2

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 3

Performance comparison of four schemes"

方案	N_RR	N_RC	N_RZ	N_d	$P N R Y$	$y N R R$	N_AC	N_FWS/台	N_I/O	I/O口坐标
方案	N_RR	N_RC	N_RZ	N_d	$P N R Y$	$y N R R$	N_AC	N_FWS/台	N_I/O	x_I/O	y_I/O	z_I/O
原方案	45	22	5	1	1	2	11	20	6	2，5，8，26，29，32	1	1
优化方案1	45	22	5	1	4	18，20，32，34	11	11	6	9，17，25	19，33	3
优化方案2	25	40	5	2	2	14，16	10	12	5	9，18，26，33，42	15	3
优化方案3	23	36	6	2	2	13，15	9	13	5	7，15，23，31，39	14	3
方案	N_L/台	提升机坐标		r_util/%	$η t h$ /（SKU‧h ^-1）	S/km	E/MJ	C_p/万元	C_m/万元	A/m ²	N_SKU/SKU
方案	N_L/台	x_L	y_L	r_util/%	$η t h$ /（SKU‧h ^-1）	S/km	E/MJ	C_p/万元	C_m/万元	A/m ²	N_SKU/SKU
原方案	6	3，6，9，27，30，33	1	63.96	214.91	297.98	404.37	1　581.0	580	1　551	4　960
优化方案1	6	10，18，26	19，33	59.30	215.42	179.26	196.11	1　407.0	400	1　683	4　990
优化方案2	5	10，19，27，34，43	15	72.00	222.50	169.15	182.96	1　385.5	370	1　400	5　040
优化方案3	5	8，16，24，32，40	13，15	71.45	222.60	158.40	134.14	1　420.7	410	1　170	5　016

Table 3

References 22

1	VAUGHAN T S， PETERAEN C G ． The effect of warehouse cross aisles on order picking efficiency［J］． International Journal of Production Research， 1999， 37（ 4）： 881- 897.
2	EKREN B Y， KAYA B， KÜÇÜKYAŞAR M ． Shuttle-based storage and retrieval systems designs from multi-objective perspectives：total investment cost，throughput rate and sustainability［J］． Sustainability， 2023， 15： 762/1- 16.
3	BATTARRA I， ACCORSI R， MANZINI R， et al ． Hybrid model for the design of a deep-lane multisatellite AVS/RS［J］． The International Journal of Advanced Manufacturing Technology， 2022， 121： 1191- 1217.
4	MAROLT J， KOSANIĆ N， LERHER T ． Relocation and storage assignment strategy evaluation in a multiple-deep tier captive automated vehicle storage and retrieval system with undetermined retrieval sequence［J］． The International Journal of Advanced Manufacturing Technology， 2022， 118： 3403- 3420.
5	ROODBERGEN K J， VIS I F A， TAYLOR G D ． Simultaneous determination of warehouse layout and control policies［J］． International Journal of Production Research， 2015， 53（ 11）： 3306- 3326.
6	MARCHET G， MELACINI M， PEROTTI S， et al ． Development of a framework for the design of autonomous vehicle storage and retrieval systems［J］． International Journal of Production Research， 2013， 51（ 14）： 4365- 4387.
7	BORTOLINI M， FACCIO M， FERRARI E， et al ． Design of diagonal cross-aisle warehouses with class-based storage assignment strategy［J］． International Journal of Advanced Manufacturing Technology， 2019， 100： 2521- 2536.
8	张志勇，王琴，梁艳．仓库内部布局的双叶Leaf方法及其通道角度优化［J］．系统工程， 2019， 37（ 2）： 70- 80.
	ZHANG Zhi-yong， WANG Qin， LIANG Yan ． Twin leaf method for warehouse internal layout and its aisles angle optimization［J］． Systems Engineering， 2019， 37（ 2）： 70- 80.
9	祝凌瑶，周丽．现代仓储中心存储布局的优化研究［J］．工程数学学报， 2022， 39（ 6）： 862- 874.
	ZHU Lingyao， ZHOU Li ． Study on storage layout optimization of modern warehouse center［J］． Chinese Journal of Engineering Mathematics， 2022， 39（ 6）： 862- 874.
10	YAN Q， LU J S， SHAO Y P， et al ． A scheduling optimization method for stacker path in double-ended compact storage system［J］． Advances in Mechanical Engineering， 2023， 15（ 2）： 1- 14.
11	ALNAHHAL M， SALAH B， AHMAD R ． Increasing throughput in warehouses：the effect of storage reallocation and the location of input/output station［J］． Sustainability， 2022， 14： 4611/1- 16.
12	SONG Y B， MU H B ． Integrated optimization of input/output point assignment and twin stackers scheduling in multi-input/output points automated storage and retrieval system by ant colony algorithm［J］． Mathematical Pro-blems in Engineering， 2022， 2022： 5997095/1- 18.
13	HAO J J， YU Y G， ZHANG L L ． Optimal design of a 3D compact storage system with the I/O port at the lower mid-point of the storage rack［J］． International Journal of Production Research， 2015， 53（ 17）： 5153- 5173.
14	HU M H， CHEN M C， KU M Y ． Storage location assignment and I/O location design on storage planning［J］． International Journal of Information Systems and Supply Chain Management， 2016， 9（ 4）： 43- 57.
15	YAN Q， LU J S， TANG H T， et al ． Travel time analysis and dimension optimisation design of double-ended compact storage system［J］． International Journal of Production Research， 2023， 61（ 20）： 6718- 6745.
16	王晓军，王博，晋民杰，等． AutoStore仓储系统资源配置仿真与优化［J］．计算机仿真， 2022， 39（ 5）： 480- 487，503.
	WANG Xiao-jun， WANG Bo， JIN Min-jie， et al ． Simulation and Optimization of Resource Allocation in Auto Store Storage System［J］． Computer Simulation， 2022， 39（ 5）： 480- 487，503.
17	姚道金，殷雄，罗真，等．复杂环境下AGVS路径规划算法［J］．华南理工大学学报（自然科学版）， 2023， 51（ 11）： 56- 62.
	YAO Daojin， YIN Xiong， LUO Zhen， et al ． AGVS path planning agorithm in complex environments［J］． Journal of South China University of Technology（Natural Science Edition）， 2023， 51（ 11）： 56- 62.
18	RAGHURAM P， ARJUNAN M K ． Design framework for a lean warehouse：a case study-based approach［J］． International Journal of Productivity and Performance Management， 2022， 71（ 6）： 2410- 2431.
19	WU Y， ZHOU C， MA W， et al ． Modelling and design for a shuttle-based storage and retrieval system［J］． International Journal of Production Research， 2020， 58（ 16）： 4808- 4828.
20	ZHAO N， LOU L， ZHANG S P， et al ． An efficient simulation model for rack design in multi-elevator shuttle-based storage and retrieval system［J］． Simulation Mo-delling Practice and Theory， 2016， 67： 100- 116.
21	MA Y F， HU Y N， YANG X J， et al ． Optimal lift scheduling in modular warehouse systems［J］． System Engineering Theory and Practice， 2023， 43（ 11）： 3307- 3319.
22	ZHAO Q X， ZHANG X D， WANG P ． Multi-type equipment selection and quantity decision optimization in intelligent warehouse［J］． IEEE Access， 2024， 12： 63515- 63527.

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