During a project, the period and the cost are often what the customer pays closo attertion to. For a multitask one, the ultimate completion time depends the slowest task. As a result, there is certain flexibility for those tasks that could be finished before the last one. We can make good use of the flexible time to reduce the overtime pay and equipment upgrade fee etc. so that the overall expense can be reduced. Based on the previous research, there is a certain relationship between time and expense. For multiple dependent projects, better time schedule could not only slow down the whole project but also reduce the overall expense. In this paper, this problem is addressed by GERT network. To be more specific, in the GERT network, the relationship between the progresses of each task is traced via Z-tags. The fixed time and flexible time are also defined, and the experiments of the expense are conduced involved in flexible time and fixed time respectively. When the flowing money in task network becomes a function of time, we can optimize the overall project fee can be optimized while avoiding delaying the whole project by adjusting the time schedule. Moreover, the customer content maximization (CCM) method is used to optimize the project fee. The CCM is defined as the weighted sum of expense content and risk content. At last, the project of one large passenger cabin environmental control system and work on the flexible time and expense of each task are investigated. It is found that our method is able to make full use of the flexible time of each task so as to reduce the overall project fee, which is full of practical values.
GENG Rui, ZHU Jian-jun, WANG He-hua, LIU Xiao-di
. Optimization of the Costs in Multi-tasking Grey GERT Based on z Tags[J]. Chinese Journal of Management Science, 2017
, 25(4)
: 133
-142
.
DOI: 10.16381/j.cnki.issn1003-207x.2017.04.016
[1] Abdi R, Ghasemzadeh H R, Abdollahpour S, et al. Modeling and analysis of mechanization projects of wheat production by GERT networks[J]. Agricultural Sciences in China, 2010, 9(7):1078-1083.
[2] El-Sherbeny M. GERT analysis for a dissimilar two-engine aeroplane model with CCF, human error and PM[J]. The International Journal of Quality & Reliability Management, 2010, 27(3):378-390.
[3] León H C M, Farris J A, Letens G et al. An analytical management framework for new product development processes featuring uncertain iterations[J]. Journal of Engineering and Technology Management, 2013, 30(1):45-71.
[4] Xu Ruiting, Fang Zhigeng, Sun Jinyu. A grey STA-GERT quality evaluation model for complex products based on manufacture-service dual-network[J].Grey Systems:Theory and Application, 2014, 4(2):195-206.
[5] 刘远,方志耕,刘思峰, 等. 基于供应商图示评审网络的复杂产品关键质量源诊断与探测问题研究[J]. 管理工程学报, 2011,25(2):212-219.
[6] Lin Kuoping, Wu Mingjia, Hung Kuochen, et al. Developing a Tω (the weakest t-norm) fuzzy GERT for evaluating uncertain process reliability in semiconductor manufacturing[J]. Applied Soft Computing, 2011, 11(8):5165-5180.
[7] Wang C N, Yang G K, Hung K C, et al. Evaluating the manufacturing capability of a lithographic area by using a novel vague GERT[J]. Expert Systems with Applications, 2011, 38(1):923-932.
[8] 方志耕, 杨保华, 陆志鹏, 等. 基于Bayes推理的灾害演化GERT网络模型研究[J]. 中国管理科学, 2009, 17(2):102-107.
[9] 金振鑫,陈洪转,胡海东.区域创新型科技人才培养及政策设计的GERT网络模型[J]. 科学学与科学技术管理, 2011, 32(12):144-152.
[10] 阮爱清,刘思峰. 灰色GERT网络及基于顾客需求的灰数估计精度[J]. 系统工程, 2007,25(12):100-104.
[11] 肖先刚, 方志耕, 赵云龙. 基于GERT网络改进算法的某型船舶制造周期问题研究[J]. 物流科技, 2009,(4):25-28.
[12] 杨保华,方志耕,张娜,等. 基于多种不确定性参数分布的U_GERT网络模型及其应用研究[J]. 中国管理科学, 2010, 18(2):96-101.
[13] 刘思峰,俞斌,方志耕,等. 灰色价值流动G-G-GERT网络模型及其应用研究[J]. 中国管理科学, 2009, 17(S1):28-33.
[14] Xu Ruiting, Fang Zhigeng,Sun Jinyu. A grey STA-GERT quality evaluation model for complex products based on manufacture-service dual-network[J]. Grey Systems:Theory and Application, 2014, 4(2):195-206.
[15] Hajiagha S H R, Mahdiraji H A, Hashemi S S. A hybrid model of fuzzy goal programming and grey numbers in continuous project time, cost, and quality tradeoff[J]. The International Journal of Advanced Manufacturing Technology, 2014, 71(1):117-126.
[16] Haque K M A, Hasin M A A. Fuzzy based project time-cost optimization using simulated annealing search technique[J]. International Journal of Information Technology Project Management, 2014, 5(1):90-103.
[17] Salmasnia A, Mokhtari H, Abadi I N K. A robust scheduling of projects with time, cost, and quality considerations[J]. The International Journal of Advanced Manufacturing Technology, 2012, 60(5):631-642.
[18] Cai Jinling, Zhu W, Ding Haijun, et al. An improved artificial bee colony algorithm for minimal time cost reduction[J]. International Journal of Machine Learning and Cybernetics, 2014, 5(5):743-752.
[19] Afruzi E N, Najafi A A, Roghanian E, et al. A multi-objective imperialist competitive algorithm for solving discrete time, cost and quality trade-off problems with mode-identity and resource-constrained situations[J]. Computers & Operations Research, 2014,50:80-96.
[20] Ghasemzsdeh F, Archer N,Iyogun P. A zero-one model for project portfolio selection and scheduling[J]. Journal of Operational Research Society, 1999, 50(7):745-755.
[21] Hegazy T. Optimization of construction time-cost trade-off analysis using genetic algorithms[J]. Canadian Journal of Civil Engineering, 2011, 26(6):685-697.
[22] GoldrattE M. Critical chain[M].Great Barrington:The North River Press,1997.
