基于分布式资源配置的现金流动态均衡多项目调度优化

doi:10.16381/j.cnki.issn1003-207x.2024.1589

Abstract

Abstract:

In reality， a contractor often implements several projects concurrently while during this course， how to maintain a dynamic balance between cash outflows and cash inflows is a key issue for the contractor to deal with. It should be noted that over the course of the project， the distribution of cash flows is closely related to the arrangement of the project schedule. As a result， through reasonable project scheduling， the contractor can effectively coordinate cash outflows and inflows and thus best achieve a positive cash flow balance. Based on the realistic background aforementioned， this paper studies the decentralized resource-constrained multi-project scheduling problem with the objective of cash flow dynamic balance. In the problem， the schedule of each project is arranged under its local resource constraints whereas the enterprise headquarter exerts its impact on project scheduling through the allocation of global resources among different projects.First， based on the discussion of realistic and theoretical backgrounds of the research problem， a multi-project scheduling optimization model is constructed under the objective of minimizing the maximum cash flow gap of the contractor. The model consists of two submodels， namely local project scheduling submodel and global resource allocation submodel. The former optimally arranges schedule for each single project under the constraint of its local resources and based on the results obtained， the latter optimally allocates and coordinates global resources among multiple projects so as to realize the best dynamic balance between the contractor’s cash outflows and inflows. Through the analysis of the constructed model， three basic properties of the studied problem are proposed， thus providing supports for the effective solution of the problem.Second， due to the characteristic of the problem， which is NP-hard and includes two interrelated subproblems， a simulated annealing - tabu search algorithm is developed. In the algorithm， the local project scheduling subproblem is solved by a simulated annealing - tabu search algorithm， where a tabu list is employed to prevent the algorithm from visiting identical feasible solutions repeatedly at high temperature stage， while the global resource allocation subproblem is tackled using a sequential game based algorithm， which deals with global resource conflicts effectively by postponing the relevant activities. To enhance the searching efficiency of the developed algorithm， an improvement measure is designed for the algorithm for the local project scheduling subproblem based on the properties of the studied problem.Finally， in order to evaluate the performance of the algorithm and the effect of its improvement measure， a large-scale computational experiment is conducted on a data set generated randomly. In the experiment， taking the multi-start iteration improvement algorithm as the comparison algorithm， the two versions of the developed algorithm， i.e.， the simulated annealing - tabu search algorithm equipped with and without the improvement measure， are tested and the results are also compared with those obtained by the simulated annealing， tabu search， and multistart iteration improvement algorithms. Based on the computational results， the advantage of the simulated annealing - tabu search algorithm including the improvement measure over other algorithms is verified and through the sensitivity analysis of the impact of key parameters on the objective function， the trends of the maximum cash flow gap of individual projects and multiple projects varying with the parameters are discussed.The conclusions of the research in this paper are as follows： The simulated annealing - tabu search algorithm with the improvement measure is the most promising algorithm for the studied problem. The advantage of this algorithm over other algorithms grows with the increase in the activity number of project， local resource strength， and project deadline or the decrease in the local resource factor. The maximal cash flow gap in individual projects descends as the local resource strength， advance payment proportion， milestone activity number， and project deadline go up， while ascends as the local resource factor and discount rate climb. When the global resource factor drops or the global resource strength and project deadline grow， the increment of the maximal cash flow gap in multi-project， which is caused by handling global resource conflicts， decreases.

Key words: Decentralized multi-project scheduling, Dynamic balance of cash flows, Optimization model, Simulated annealing, Tabu search

CLC Number:

C935

Yukang He,Tao Jia,Weibo Zheng. Decentralized Resource Allocation Based Multi-project Scheduling Optimization to Dynamically Balance Cash Flows[J]. Chinese Journal of Management Science, 2026, 34(2): 144-155.

Figures/Tables 7

参数	设置	参数	设置
项目数 $H$	3	全局资源强度 $G R F$	0.5，0.75，1
项目非虚活动数 $n h$	10，20，40，60	全局资源因子 $G R F$	0.5，0.75，1
项目的开始和结束活动数	从2、3、4中按等概率随机选取	活动工期 $d i h$	从 $U [1,10]$ 中随机选取
项目的最大紧前紧后活动数	4	活动成本 $c i h$	从 $U [1,10]$ 中随机选取
局部资源种类数 $K$	4	项目预付款比例 $γ h$	0.05，0.1，0.15
局部资源因子 $L R F$	0.5，0.75，1	项目支付比例 $θ h$	0.8，0.85，0.9
局部资源强度 $L R S$	0.5，0.75，1	项目开始时间 $B T h$	从 $U [0,5]$ 中随机选取
全局资源种类数 $Q$	2	单位时期折现率 $α$	0.006，0.008，0.01
活动挣值 $v i h$	$ρ v ∙ c i h$ 。其中， $ρ v$ 是生成 $v i h$ 的专用参数，该参数的取值从 $U [1.3,1.5]$ 中随机其选取	项目里程碑活动	里程碑活动数 $M h$ 被设置为3、4、5，里程碑活动从项目的非虚活动中随机选取
活动对局部资源的需求量 $l r i h k$	从 $U [1,10]$ 中随机选取	项目截止时间 $D T h$	$A T h$ + $ρ D ∙ C P L$ 。其中， $ρ D$ 是生成 $D T h$ 的专用参数，该参数被设置为1.2、1.4、1.6； $C P L$ 为不考虑资源约束下的项目关键路径长度
活动对全局资源的需求量 $g r i h q$	从 $U [1,10]$ 中随机选取	项目截止时间 $D T h$

参数	取值	SA-TS-IM			SA-TS			SA			TS			MSII			$∆ A R D 1$	$∆ A R D 2$	$∆ A R D 3$
参数	取值	$N B S$	$A R D$	$M R D$	$N B S$	$A R D$	$M R D$	$N B S$	$A R D$	$M R D$	$N B S$	$A R D$	$M R D$	$N B S$	$A R D$	$M R D$	$∆ A R D 1$	$∆ A R D 2$	$∆ A R D 3$
$n h$	10	5,334	1.66	2.81	3,063	5.10	9.65	2,979	6.44	9.92	3,561	6.26	12.09	1,854	13.37	26.20	3.44	4.78	4.60
	20	5,766	1.27	2.86	3,519	5.69	8.87	3,288	7.58	12.20	3,111	7.35	12.14	1,089	22.56	32.20	4.42	6.31	6.08
	40	6,174	1.15	2.34	3,900	6.72	10.73	3,207	8.36	14.26	2,697	9.26	17.64	258	37.18	52.07	5.57	7.21	8.11
	60	6,495	0.53	1.00	3,429	7.84	13.22	3,369	9.70	18.55	2,763	10.07	17.73	0	46.60	69.88	7.31	9.17	9.54
$L R F$	0.5	8,331	0.92	2.01	4,914	7.16	13.17	4,566	9.12	18.55	3,903	9.59	17.73	810	32.67	65.07	6.24	8.20	8.67
	0.75	7,995	1.16	2.27	4,722	6.75	13.22	4,182	8.26	13.50	3,933	7.85	12.59	1,137	29.60	69.88	5.59	7.10	6.69
	1	7,443	1.38	2.86	4,275	5.12	9.61	4,095	6.68	10.05	4,296	7.28	15.10	1,254	27.54	53.14	3.74	5.30	5.90
$L R S$	0.5	7,266	1.41	2.86	4,143	5.09	10.32	3,993	6.31	9.52	4,362	5.64	9.87	1,437	26.76	45.40	3.68	4.90	4.23
	0.75	8,055	1.22	2.26	4,755	6.55	13.22	4,215	8.38	15.18	3,978	8.54	15,72	1,113	28.78	56.95	5.33	7.16	7.32
	1	8,448	0.80	1.60	5,013	7.38	12.74	4,635	9.36	18.55	3,792	10.55	17.73	651	34.25	69.88	6.58	8.56	9.75
$ρ D$	1.2	7,383	1.33	2.86	4,278	5.64	11.13	4,080	6.80	12.58	4,419	6.33	11.66	1,368	25.74	48.14	4.31	5.47	5.00
	1.4	7,995	1.18	2.34	4,557	6.16	11.59	4,248	8.22	14.38	4,134	8.45	16.82	1,044	28.80	69.88	4.98	7.04	7.27
	1.6	8,391	0.95	1.92	5,076	7.25	13.22	4,515	9.08	18.55	3,579	9.96	17.73	789	35.25	66.03	6.30	8.13	9.01
全部算例		23,769	1.15	2.86	13,911	6.35	13.22	12,843	8.03	18.55	12,132	8.25	17.73	3,201	29.93	69.88	5.20	6.88	7.10

参数取值	$G m a x h$	参数取值	$G m a x h$	参数取值	$G m a x h$	参数取值	$G m a x h$	参数取值	$G m a x h$	参数取值	$G m a x h$	参数取值	$G m a x h$
$L R F$ =0.5	13.96	$L R S$ =0.5	18.59	$γ h$ =0.05	27.48	$M h$ =3	26.34	$θ h$ =0.8	26.02	$α$ =0.006	13.67	$ρ D$ =1.2	19.23
$L R F$ =0.75	14.93	$L R S$ =0.75	14.73	$γ h$ =0.1	13.62	$M h$ =4	14.70	$θ h$ =0.85	16.16	$α$ =0.008	15.43	$ρ D$ =1.4	14.72
$L R F$ =0.75	17.11	$L R S$ =1	12.70	$γ h$ =0.15	4.88	$M h$ =5	4.98	$θ h$ =0.9	3.80	$α$ =0.01	16.90	$ρ D$ =1.6	12.01

参数取值	$∆ G m a x$	参数取值	$∆ G m a x$	参数取值	$∆ G m a x$
$G R F$ =0.5	1.75	$G R S$ =0.5	6.26	$ρ D$ =1.2	4.58
$G R F$ =0.75	3.61	$G R S$ =0.75	3.23	$ρ D$ =1.4	3.16
$G R F$ =0.75	4.81	$G R S$ =1	0.67	$ρ D$ =1.6	2.42

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Decentralized Resource Allocation Based Multi-project Scheduling Optimization to Dynamically Balance Cash Flows

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