基于IGSA的线性工程施工进度计划优化模型研究

doi:10.16381/j.cnki.issn1003-207x.2022.1627

摘要/Abstract

摘要：

在线状、条状及块状活动共存的线性工程中，针对线状、条状活动多的施工模式，以及线状活动施工方向可选择的复杂施工场景，利用线性计划方法，构建线性工程施工进度计划工期优化模型。同时，提出了一种改进引力搜索算法，该算法通过整数编码和约束规则使其符合离散型问题特点，利用劣质粒子增强局部搜索能力，采用粒子群算法记忆策略提高收敛速度，引入自适应交叉算子优化全局搜索能力，设计灾变算子改善种群多样性。案例仿真结果表明：工期优化模型和改进算法能够高效获得最短工期下复杂线性工程项目的施工进度计划，为管理者提供科学、合理的工期控制决策方法。

关键词: 线性工程, 施工进度计划, 线性计划方法, 工期优化, 改进引力搜索算法

Abstract:

In linear projects where linear， bar and block activities coexist， for complex construction scenarios with multiple construction modes of linear and bar activities， and the construction direction of linear activities can be selected， the linear planning method is used to construct the construction schedule of linear projects. At the same time， an improved gravitational search algorithm is proposed. The algorithm conforms to the characteristics of discrete problems through integer coding and constraint rules， uses inferior particles to increase local search ability， adopts particle swarm algorithm memory strategy to improve convergence speed， and introduces adaptive crossover operator. The case simulation results show that the construction period optimization model and the improved algorithm can efficiently obtain the construction schedule of the complex linear engineering project under the shortest construction period， and provide managers with a scientific and reasonable construction period control decision-making method.

Key words: linear engineering, construction schedule plan, linear programming method, schedule optimization, improved gravity search algorithm

中图分类号:

F272

潘泽龙,周国华,黄超然. 基于IGSA的线性工程施工进度计划优化模型研究[J]. 中国管理科学, 2025, 33(7): 210-221.

Zelong Pan,Guohua Zhou,Chaoran Huang. Research on Optimization Model of Linear Engineering Construction Schedule Planning Based on IGSA[J]. Chinese Journal of Management Science, 2025, 33(7): 210-221.

图/表 10

图1

图2

图3

图4

图5

图6

表1

最优施工方案"

活动序号	$X i j w (S T i j, E T i j)$				$P i$
活动序号	j=1	j=2	j=3	j=4	$P i$
1	1(32.42,168.56)	—	—	—	0
2	1(46.56,210.8)	—	—	—	0
3	—	1(10,123.15)	—	—	1
4	—	2(23,157.08)	—	—	1
5	—	1(29.58,120.63)	—	—	1
6	—	—	2(48.34,257.7)	—	1
7	—	—	2(51.78,157.03)	—	1
8	3(699.33,721.94)	—	—	—	0
9	2(627.74,666.42)	—	—	—	0
10	—	2(636.99,637.52)	1(581.88,636.99)	—	0
11	—	1(664.75,678.02)	—	—	1
12	—	—	3(678.05,703.02)	3(703.02,707.34)	1
13	—	—	—	3(701.7,721.31)	0
14	1(544.68,562.68)	—	—	—	0
15	1(537.68,544.68)	—	—	—	0
16	—	1(442.97,503.64)	—	—	0
17	—	1(572.68,583.35)	1(583.35,613.02)	—	1
18	2(751.6,753.85)	2(749.67,751.6)	2(748.69,749.67)	2(747.75,748.69)	0
29	1(0,49.14)	1(49.14,60)	—	—	1
20	—	1(0,38.34)	1(38.34,60)	—	1
21	—	—	1(50.96,60)	1(0，50.96)	0
22	1(30.36,157.27)	—	—	—	—
23	—	1(60.54,437.54)	—	—	—
24	—	(16.8,428.8)	—	—	—
25	—	(27.92,417.92)	—	—	—
26	(43.14,383.14)	—	—	—	—
27	(29.33,609.33)	—	—	—	—
28	(40.66,469.66)	—	—	—	—
29	(47.75,248.75)	—	—	—	—
30	(52.54,457.54)	—	—	—	—
31	(58.74,507.74)	—	—	—	—
32	—	—	—	(60.89,360.89)	—
33	—	—	—	(54.34,234.34)	—
34	—	—	—	(51.7,611.7)	—
35	—	(48.34,358.34)	—	—	—
36	—	—	(70,527)	—	—

表1

表2

图7

图8

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活动数	算法类型	最优解	最差解	标准差	运行时间CPU/s
20	IGSA	122.40	122.40	0.00	137.7
	Agrama	125.67	128.23	0.69	116.8
	SA	126.74	142.96	5.21	53.13
	GSA	135.30	138.95	1.32	125.4
	IGA	124.50	127.69	1.10	199
30	IGSA	278.00	280.25	0.64	566.2
	Agrama	288.68	300.05	3.46	560.2
	SA	290.71	320.95	7.59	281.3
	GSA	322.38	380.00	15.05	596.3
	IGA	283.08	290.88	2.11	913.4
40	IGSA	317.15	318	0.28	1356.4
	Agrama	321.8	327.41	1.82	1225.5
	SA	318.61	324.19	1.56	671.9
	GSA	355.65	376.43	6.69	1222
	IGA	317.91	320.09	0.57	1961