Resource allocation optimization of equipment development task based on MOPSO algorithm

doi:10.21629/JSEE.2019.06.09

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (6): 1132-1143.doi: 10.21629/JSEE.2019.06.09

收稿日期:2019-04-10 出版日期:2019-12-20 发布日期:2019-12-25

Resource allocation optimization of equipment development task based on MOPSO algorithm

Xilin ZHANG^1,²(), Yuejin TAN¹(), Zhiwei YANG^1,*()

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
² Business School, Jiangsu Normal University, Xuzhou 221116, China

Received:2019-04-10 Online:2019-12-20 Published:2019-12-25
Contact: Zhiwei YANG E-mail:zhangxilin16@nudt.edu.cn;yjtan@nudt.edu.cn;zhwyang88@hotmail.com
About author:ZHANG Xilin was born in 1984. He received his B.S. degree in logistics engineering from Shandong University in 2007 and M.S. degree in logistics engineering from Jilin University in 2009. He is currently a Ph.D. candidate in College of Systems Engineering, National University of Defense Technology (NUDT). His main research interests include complex system engineering management.E-mail: zhangxilin16@nudt.edu.cn|TAN Yuejin was born in 1958. He is currently a professor and Ph.D. student supervisor in College of Systems Engineering, NUDT. His main research interests include complex system engineering management, and armament system-of-systems technology. E-mail: yjtan@nudt.edu.cn|YANG Zhiwei was born in 1988. He received his Ph.D. degree in computer science from Leiden University in 2016. He is currently a lecturer in College of Systems Engineering, NUDT. His main research interests include systems engineering, systems optimization and systems simulation. E-mail: zhwyang88@hotmail.com
Supported by:
the National Natural Science Foundation of China(71690233);This work was supported by the National Natural Science Foundation of China (71690233)

摘要/Abstract

Abstract:

Resource allocation for an equipment development task is a complex process owing to the inherent characteristics, such as large amounts of input resources, numerous sub-tasks, complex network structures, and high degrees of uncertainty. This paper presents an investigation into the influence of resource allocation on the duration and cost of sub-tasks. Mathematical models are constructed for the relationships of the resource allocation quantity with the duration and cost of the sub-tasks. By considering the uncertainties, such as fluctuations in the sub-task duration and cost, rework iterations, and random overlaps, the tasks are simulated for various resource allocation schemes. The shortest duration and the minimum cost of the development task are first formulated as the objective function. Based on a multi-objective particle swarm optimization (MOPSO) algorithm, a multi-objective evolutionary algorithm is constructed to optimize the resource allocation scheme for the development task. Finally, an uninhabited aerial vehicle (UAV) is considered as an example of a development task to test the algorithm, and the optimization results of this method are compared with those based on non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ), non-dominated sorting differential evolution (NSDE) and strength pareto evolutionary algorithm-Ⅱ (SPEA-Ⅱ). The proposed method is verified for its scientific approach and effectiveness. The case study shows that the optimization of the resource allocation can greatly aid in shortening the duration of the development task and reducing its cost effectively.

Key words: resource allocation, equipment development task, multi-objective particle swarm optimization (MOPSO), development task simulation

. [J]. Journal of Systems Engineering and Electronics, 2019, 30(6): 1132-1143.

Xilin ZHANG, Yuejin TAN, Zhiwei YANG. Resource allocation optimization of equipment development task based on MOPSO algorithm[J]. Journal of Systems Engineering and Electronics, 2019, 30(6): 1132-1143.

图/表 6

参考文献 41

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ID	Sub-task name	Minimum duration/d			Maximum cost/US＄K			MQRD
ID	Sub-task name	$DM_o$	$DM_m$	$DM_p$	$CM_o$	$CM_m$	$CM_p$	MQRD
1	Prepare UAV preliminary design requirements and objectives	1.9	2	3	8.6	9	13.5	400
2	Create UAV preliminary design configuration	4.75	5	8.75	5.3	5.63	9.84	600
3	Prepare surfaced models & internal drawings	2.66	2.8	4.2	3	3.15	4.73	600
4	Perform aerodynamics analyses & evaluation	9	10	12.5	6.8	7.5	9.38	400
5	Create initial structural geometry	14.3	15	26.3	128	135	236	600
6	Prepare structural & notes for finite element model	9	10	11	10	11.3	12.4	500
7	Develop freebody diagrams & applied loads	7.2	8	10	11	12	15	500
8	Perform weights & inertia analysis	4.75	5	8.75	8.9	9.38	16.4	300
9	Perform stability and control analyses & evaluation	18	20	22	20	22.5	24.8	500
10	Develop freebody diagram & applied loads	9.5	10	17.5	21	22.5	39.4	400
11	Establish internal load distributions	14.3	15	26.3	21	22.5	39.4	500
12	Evaluate structural strength, stiffness & life	13.5	15	18.8	41	45	56.3	400
13	Preliminary manufacturing planning & analyses	30	32.5	36	214	232	257	600
14	Prepare UAV proposal	4.5	5	6.25	20	22.5	28.1	500

Resource allocation scheme	Duration/d	Cost/US＄K	Average occurrence of resource conflicts	Average number of reworks
400 600 600 400 600 500 500 300 500 400 500 400 600 500	118.1	690.1	29.1	11.2
240 361 365 240 372 341 300 182 301 243 308 400 371 300	135.1	622.7	10.1	10.5
353 360 368 242 360 306 304 180 300 245 329 400 366 427	121.2	626.9	10.7	10.6
354 363 360 297 368 316 302 181 300 244 330 400 367 500	118.4	627.9	11.7	10.9
400 369 377 313 432 318 302 190 308 241 353 240 402 500	111.8	650.3	18.0	13.5
369 364 385 290 437 308 301 180 312 240 374 241 365 494	113.2	644.5	16.5	13.4

Resource allocation optimization of equipment development task based on MOPSO algorithm

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