基于机器学习的场地受限型项目调度与物料订购集成优化

doi:10.16381/j.cnki.issn1003-207x.2024.2109

Abstract

Abstract:

The implementation of construction projects involves multiple spaces such as construction sites and prefabricated component manufacturers. How to realize the coordination between on-site activity scheduling and off-site material supply is critical to ensure the smooth execution of these projects. Meanwhile， the cramped site space furtherly renders the parallel execution of activities and limits the materials ordering plan through warehouse capacity. In order to solve the above practical dilemmas， the integrated optimization problem of project scheduling and material procurement under the limited space is explored is investigated in this paper.According to practical scenarios of construction projects with limited site space， the considered project scheduling and material ordering problem is defined. On this basis， considering the precedence relationships between activities， resource availability， inventory dynamic balance equations and site space constraints， the project scheduling and material ordering model with site space constraints is formulated. Through concurrently optimizing activity timetables and material ordering plan， the integrated model aims to minimize the total project cost.In order to solve the proposed problem， the complexity of the problem is analyzed. Based on the NP-hard characteristics of the problem， an algorithm automatic recommendation approach embedded with four meta-heuristics is developed using machine learning. Firstly， an improved serial scheduling generation scheme is proposed based on the problem structure， and four meta-heuristic algorithms are accordingly designed. Secondly， based on the extracted 91 instance features， the performance of five machine learning algorithms is tested， and the decision tree algorithms is selected to build the algorithm recommendation model due to its highest accuracy. Finally， the model parameters that make the approach perform best are determined by grid search method.To verify the effectiveness of the proposed method， large-scale numerical experiments are carried out based on the instances generated by ProGen and PSPLIB. The effectiveness of the proposed integrated model is verified by comparing with the independent model. The superiority the of developed automatic algorithm recommendation approach is also proved. Besides， comparative experiments among CPLEX， the proposed recommendation approach and other algorithms are set up， and experimental results demonstrate that the algorithm recommendation method based on random forest behaves best on accuracy and solution quality.The solution framework of the PSMOP is constructed by constructing an automatic algorithm recommendation approach embedded with multiple meta-heuristics. Meanwhile， the integrated decision making on both project scheduling and material ordering can provide more comprehensive decision supports for project managers. Moreover， reference values for the research on construction project management is provided.

Key words: project scheduling, material ordering, integrated optimization model, machine learning, algorithm recommendation

CLC Number:

C935

Baofeng Tian,Jingwen Zhang,Zhi Chen. An Automatic Algorithm Recommendation Approach for the Project Scheduling and Material Ordering Problem with Site Space Constraints Through Machine Learning[J]. Chinese Journal of Management Science, 2026, 34(10): 175-185.

Figures/Tables 15

集合	描述
$V$	活动集合， $V = 1, 2, ⋯, J$
$E$	优先关系集合， $E ⊂ V × V$
$R$	可更新资源集合， $R = 1, ⋯, k, ⋯, K$
$N R$	物料（不可更新资源）集合， $N R = 1, ⋯, m, ⋯, M$
$P r e j$	活动 $j$ 的紧前活动集合
$S u c j$	活动 $j$ 的紧后活动集合
参数
$d j$	活动j的工期
$R k$	可更新资源k的供应限量
$r j k$	活动j在每个阶段对资源k的需求量
$u j m$	活动j在每个阶段对物料m的消耗量
$U j m$	活动j对物料m的总消耗量， $U j m = d j ∙ u j m$
$W m$	物料m的仓库容量
$L m$	物料m的订购提前期
$O m$	物料m的单次订购成本
$H m$	物料m的单位存储成本
$D$	项目合同工期
$R e$	项目提前完工的单位工期奖励
$e f j / l f j$	活动j的最早结束时间/最晚结束时间
中间变量
$I m t$	第t阶段末期物料m的库存量
$y m t$	0-1辅助变量，若第t阶段发布物料m的订购订单， $y m t = 1$ ，否则 $y m t = 0$
决策变量
$x j t$	若活动j在第t阶段执行结束，则 $x j t = 1,$ 否则 $x j t = 0$
$P j m t$	若活动j所需物料m在第t阶段订购，则 $P j m t = 1$ ，否则 $P j m t = 0$

O_m

H_m

m=1

J10、J20： $D = 0.7 × ∑ j ∈ V d j$

J30、J60、J90： $D = 0.4 × ∑ j ∈ V d j$

m=2

m=3

M	分散决策	集成决策	$I r a t e$ (%)	$I c o s t$
M=1	3589.75	3328.04	7.86	261.71
M=2	5599.42	5299.18	5.67	300.23
M=3	8487.19	8116.68	4.56	370.52
平均	5892.12	5581.30	6.03	310.82

References 33

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算法	预测值
算法	GA	SA	PSO	DE
GA	267	1	1	36
SA	32	3	0	0
PSO	20	0	361	40
DE	31	0	33	255

备选算法	TP Rate	FR Rate	Precision
GA	0.875	0.107	0.763
SA	0.086	0.001	0.750
PSO	0.857	0.052	0.914
DE	0.799	0.100	0.770
平均	0.820	0.080	0.824

算法	比例(%)
GA	27.43
SA	3.30
PSO	41.17
DE	28.11

算法	Accuracy（%）	Cost	Gap（%）	AvgTime
BA	100	23930.41	0	20.529
AARA（本文）	82.04	24179.29	1.04	19.592
AARA-LMT	79.44	24734.47	3.36	17.607
AARA-RT	78.15	24796.21	3.62	18.615
GA	27.43	26849.44	12.20	17.218
SA	3.30	28678.68	19.84	19.782
PSO	41.17	26188.47	9.44	22.232
DE	28.11	26695.63	11.56	16.885
Rand	24.17	27139.48	13.41	19.565

算例	BA	CPLEX		AACA（本文）		Rand
算例	Cost	Cost	Gap（%）	Cost	Gap（%）	Cost	Gap（%）
J10	5586.51	5581.30	-0.09	5645.22	1.05	6292.94	12.65
J20	11518.42	-	-	11734.97	1.88	13029.24	13.12
J30	17180.33	-	-	17374.53	1.13	19532.44	13.69
J60	35097.07	-	-	35443.78	0.99	39937.45	13.79
J90	50269.73	-	-	50356.16	0.17	57198.17	13.78

An Automatic Algorithm Recommendation Approach for the Project Scheduling and Material Ordering Problem with Site Space Constraints Through Machine Learning

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