跳到主要內容

臺灣博碩士論文加值系統

(44.222.218.145) 您好!臺灣時間:2024/03/03 23:32
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:褚文明
研究生(外文):Weng Ming Chu
論文名稱:隨機性專案網路在完成時間機率分佈及最佳化資源分配之研究
論文名稱(外文):On the Probability Distribution Function of the Project Completion Time and the Optimal Resource Allocation Problem in Stochastic Activity Networks
指導教授:姚銘忠姚銘忠引用關係曾宗瑶
指導教授(外文):Ming-Jong YaoTsueng-Yao Tseng
學位類別:博士
校院名稱:東海大學
系所名稱:工業工程與經營資訊學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:195
中文關鍵詞:隨機性專案網路專案資源分配專案網路完成時間關鍵路徑計畫評核術
外文關鍵詞:Stochastic ProjectResource AllocationProject Completion TimeCritical PathPERT
相關次數:
  • 被引用被引用:4
  • 點閱點閱:661
  • 評分評分:
  • 下載下載:209
  • 收藏至我的研究室書目清單書目收藏:1
本論文主要研究的主題有兩部分,第一個部份是針對隨機性專案網路,在專案內所有的作業時間均為隨機變數之情況下,發展出一有效率之演算法,估算專案網路完成時間之機率密度函數(Probability Density Function, pdf)。第二個部份同上述的條件及環境下,針對大型專案網路,執行專案作業之最佳資源分配,以期得到專案最小的總成本。
在確定性的專案網路環境中,每一個專案作業時間均為固定值,因此利用關鍵路徑法(Critical Path Method, CPM),可以直接且容易的將專案完成時間計算出來。然而在現實的環境中,專案作業時間會因內、外在因素之影響而產生變化,故應視為隨機變數,因此專案的完成時間亦必然為一隨機變數,其機率分配值並不容易估算。對於專案者管理而言,掌握專案的完成時限是非常重要的決策項目之ㄧ,因為與專案相關之其它問題,包括專案排程問題、最佳化資源投入問題、專案成本分析及專案風險評估等問題,均須以專案完成時間作為參考之基礎。
本論文對於專案執行環境及條件之要求,除考慮其隨機性外,另要求專案作業之機率分配不得侷限於某特定的機率型態;所發展出來的演算法,必須能有效率的在大型的專案網路中執行。一般的文獻針對於此條件要求,所提出之估算方法均會承受大量的計算負擔,並無實際應用之效果。故本研究擬以離散式技術(Discretization technique)取代一般數理分析方式,執行專案完成時間之估算。然而利用該技術的同時,須克服下述兩個問題:首先要發展出一演算機制,使專案完成時間之估算能夠有系統且精確的進行,並要求在合理的時間內完成;另一須克服之問題,就是將專案網路各路徑間相依的問題納入考慮,因路徑間相依之因素,會造成專案完成時間估算之誤差。基於此,本論文提出兩種估算專案完成時間機率分配之演算法:
1. 標籤修訂追蹤演算法(Label-Correction Tracing Algorithm, LCTA)。
2. 區域積分演算法(Marginal Integration Algorithm, MIA)。
本論文主題的第二個部份是針對隨機性專案網路,如何決定每一專案作業之最佳資源投入(Optimal Resource Allocation)量。專案作業之資源投入會減少專案的完成時間,減少專案因超過時限所產生的懲罰成本之機率,但同時亦會增加專案資源使用成本。因此專案管理者必須在這兩種成本間做權衡,對專案作業資源投入量作最佳的決策,以期求出最低的專案總成本。本論文擬以前述所發展出之LCTA為基礎,並以啟發式演算法(磁場機制演算法及基因演算法)為決策之工具,求解專案作業最佳化資源投入量。該問題亦可透過專案路徑關鍵指標(Path Critical Index, PCI)及作業關鍵指標(Activity Critical Index, ACI)之計算,來做為資源投入分析之參考,亦是本論文後續之研究目標。
In this study, I focus myself on two major topics. First, I propose efficient approaches to determine the probability density function (pdf) of the completion time of a project when the durations of the activities are random variables. Second, I would like to solve the resource allocation problem for large-size stochastic projects (i.e., those projects with the durations of its activities being random variables).
When the durations of all the activities are constants, project managers may easily calculate the project completion time by the well-known Critical Path Method (CPM). However, the duration of an activity is a random variable for most of the cases in the real world, and obviously, the project completion time turns to be another random variable. Facing such a challenge, project managers should pay serious attention to monitor the uncertainty involved in stochastic projects. Project managers are highly interested in obtaining the pdf of the project completion time because it provides full insights into the randomness of the completion of the project so that project managers would have the basis for many subsequent decisions such as planning, budgeting, risk analysis and scheduling, etc.
For large-size stochastic projects with general types of pdf for the duration of activities, the project managers must turn to the techniques of discretization since the other approaches in the literature become too demanding in computational loading. In this study, we find that there are two problems when applying the techniques of discretization to obtain an approximated pdf of the project completion time. Namely, first, there exists neither exact data structure nor systematic scheme for the computer programming when applying the techniques of discretization; and second, error may arise from assuming independency between sub-paths in the activity network. Therefore, we are motivated to propose the following two approaches to improve the techniques of discretization.
1. The Label-Correction Tracing Algorithm(LCTA)
2. The Marginal Integration Algorithm(MIA)
The major concern of my second topic, i.e., the Resource Allocation Problem in Stochastic Projects, is to determine the amount of the resource allocated to each activity. Project managers may allocate more resources to shorten the durations of the activities to expedite the project to avoid possible tardiness penalty cost from delay. However, such a move increases the total resources usage costs. Therefore, project managers need efficient decision-support tools to deal with the trade-off relation between the total costs incurred from the resource usage and the tardiness penalty of the project. We are motivated to propose new approaches based on meta-heuristics (e.g., electromagnetism algorithm and genetic algorithms) and/or the Path Criticality Index and the Activity Criticality Index in the stochastic project for the futhur researches.
摘要 III
ABSTRACT III
目錄 V
表目錄 IX
圖目錄 XI
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 5
1.4 研究方法與步驟 6
1.5 論文架構 11
第二章 文獻探討 13
2.1 PERT網路模式的介紹 13
2.2 PERT的不適性 14
2.3隨機性專案網路完成時間之估算 16
2.3.1分析推導法 17
2.3.2蒙地卡羅模擬法 19
2.3.3近似解法 19
2.4隨機性專案網路最佳化資源分配之處理 22
2.4.1數理分析解法(Analytical approach) 23
2.4.1.1整數規劃法(Integer programming, IP) 23
2.4.1.2分枝界限法(Branch and Bound algorithm) 24
2.4.1.3動態規劃法(Dynamic Programming, DP) 24
2.4.2模擬解法(Simulation approach) 24
2.4.3啟發式解法(Heuristic approach) 25
第三章 標籤修訂演算法 27
3.1 DODIN ALGORITHM之介紹 28
3.2離散化技術之介紹 30
3.2.1離散化技術的種類及其優缺點 30
3.2.2柴比契夫取樣點法 32
3.2.3離散化max及convolution運算原理 34
3.2.4離散化重新取樣技術 35
3.3路徑相依對網路完成時間求解的影響 37
3.3.1路徑相依性造成專案網路估算之偏差 37
3.3.2最長路徑偏差(Longest Path Bias, LPB) 39
3.3.3路徑相依性造成偏差之原因 40
3.3.4路徑相依因素納入估算之方法 40
3.4標籤修訂追蹤演算法 43
3.4.1擴張樹結構 43
3.4.2擴張樹節點資料結構 44
3.4.3擴張樹之追蹤程序 45
3.4.4 LCTA處理路徑相依性所造成估算之誤差 52
3.4.5 LCTA演算程序之綜整 54
3.4.6 LCTA複雜度分析 55
3.5 LCTA之實驗數據 56
3.5.1 LCTA第一組實驗 58
3.5.2 LCTA第二組實驗 60
3.6結論 62
第四章 區域積分演算法 64
4.1 MAX運算複雜度之分析 65
4.2區域積分計算之原理 65
4.2.1 MIA計算公式推導 66
4.2.2 MIA執行效率之分析 70
4.3運用MIA估算隨機性專案網路完成時間 71
4.3.1 MIA對專案網路路徑相依性問題的處理能力 71
4.3.2 LCTA_MIA執行專案網路完成時間估算實例 72
4.4 LCTA_MIA之專案網路實驗數據 73
4.4.1 LCTA_MIA第一組實驗 75
4.4.2 LCTA_MIA第二組實驗 76
4.5結論 78
第五章 隨機性專案網路最佳化資源分配問題之處理 80
5.1運用磁場機制演算法解單種類資源SRAP問題 80
5.1.1專案單種類資源分配問題之定義 82
5.1.1.1作業資源的投入 82
5.1.1.2專案成本的計算 83
5.1.2磁場機制演算法對SRAP之應用 84
5.1.2.1磁場機制演算法之架構 85
5.1.2.2磁場機制演算法之改進措施 90
5.1.3關鍵路徑叢集之概念 91
5.1.4關鍵路徑叢集演算法 94
5.1.4.1 CPCA相關的符號及參數定義 94
5.1.4.2 CPCA對專案作業資源投入之策略 96
5.1.4.3關鍵路徑叢集演算法之演算程序 99
5.1.5叢集區域搜尋演算法 105
5.1.5.1叢集區域搜尋演算法之概念 105
5.1.5.2叢集區域搜尋演算法之演算程序 106
5.1.6 EM解SRAP之數據實驗 107
5.1.6.1第一組實驗 108
5.1.6.2第二組實驗 110
5.1.7結論 113
5.2運用基因演算法解多種類資源SRAP問題 114
5.2.1專案多種類資源分配問題之定義 114
5.2.1.1多種類資源的投入 115
5.2.1.2多種類資源投入之範例 119
5.2.2基因演算法介紹 120
5.2.2.1基因演算法求解架構 120
5.2.2.2參數設定 121
5.2.2.3編碼表示法及解碼 122
5.2.2.4初始群組 123
5.2.2.5適應函數 123
5.2.2.6輪盤選擇機制 124
5.2.2.7交配機制 124
5.2.2.8突變機制 125
5.2.3多種類資源SRAP問題運用CPCA產生GA初始解 125
5.2.4數據實驗 128
5.2.5結論 130
第六章 論文未來發展方向 132
第七章 結論 134
參考文獻 136
附錄A PERT使用限制之推導 143
附錄B 離散式MAX及CONVOLUTION運算實例 145
附錄C LCTA演算實例 149
附錄D 路徑相依LCTA演算實例 152
附錄E LCTA執行KOLISH範例實驗 153
附錄F LCTA執行大型專網路範例實驗 156
附錄G MCS取樣精確度驗證及K-S 檢驗 162
附錄H LCTA_MIA執行大型專網路範例實驗 166
附錄I LCTA_MIA之K-S 檢驗 172
附錄J CPCA演算法的流程圖 175
附錄K LCTA演算法的流程圖 179
附錄L 14項專案網路範例 181
附錄M 隨機性專案網路KSP, PCI 及ACI相關問題探討 189
1.Abel, A.F., Robert, L.A., Julia, J.P., “Understanding simulation solutions to resource constrained Project scheduling problems with stochastic task durations”, Engineering Management Journal, 10/4, 5-13, 1998.
2.Adlakha, V.G., “A Monte Carlo technique with quasi random points for the stochastic shortest path problem”, American Journal of Mathematic Management Society, 7, 325-358, 1987.
3.Adlakha, V.G. & Kulkarni, V.G., “A classified bibliography of research on stochastic PERT networks: 1966-1987”, Information Systems and Operational Research, 27/3, 272-296, 1989.
4.Agrawal, M.K., Elmaghraby, S.E., “On computing the distribution function of the sum of independent random variables”, Computers & Operations Research, 28, 473-483, 2001.
5.Anklesaria, K.P., Drezner, Z., “A multivariate approach to estimating the completion times for PERT networks”, Journal of the Operational Research, 37/8, 811-815, 1986.
6.Antonella, B., Lorenzo, A.P., “Optimal resource allocation with minimum activation levels and fixed costs”, European Journal of Operational Research, 131, 536-549, 2001.
7.Basso, A., Peccati, L.A., “Optimal resource allocation with minimum activation levels and fixed costs”, European Journal of Operational Research, 131, 536-549, 2001.
8.Bellman, R., “On a routing problem”, Quarterly of Applied Mathematics, 16, 88-90, 1958.
9.Bertsekas, D.P., “A simple and fast label correcting algorithm for shortest paths”, Networks, 23, 703-709, 1993.
10.Birbil, S.I., Fang, S.C., “An electromagnetism-like mechanism for global optimization”, Journal of Global Optimization, 25, 263-282, 2003.
11.Birbil S.I., Fang S.C., Sheu R.L.. On the Convergence of a Population-Based Global Optimization Algorithm. Journal of Global Optimization, 30, 301~318, 2004.
12.Birge, J.R. and F. Louveaux, Introduction to Stochastic Programming, Springer Verlag, New York, 1997.
13.Boctor, F. F., “Some efficient multi-heuristic procedures for resource-constraint project scheduling”, European Journal of Operational Research, 49, 3-13, 1990.
14.Boctor, F. F., “Resource-constrained project scheduling by simulated annealing”, International Journal of Production Research, 34, 2335-2351, 1996
15.Bowers, J. A., “Criticality in resource constrained networks”, Operational Research Society, Journal, 46, 80-91, 1995.
16.Bowers, J. A., “Identifying critical activities in stochastic resource constrained networks”, Omega, International Journal Management Science, 24, 37-46, 1996.
17.Bowman, R.A., “Efficient estimation of arc criticalities in stochastic activity networks”, Management Science, 41/1, 1995.
18.Burt, J.M., Garman, M.B., “Conditional Monte Carlo: A simulation technique for stochastic network analysis”, Management Science, 18/3, 1971.
19.Chatzoglou, P., Macaulay, L., “A review of existing models for project planning and estimation and the need for a new approach”, International Journal of Project Management, 14, 173-183, 1996.
20.Cho, J.G., Yum, B.J., “Functional estimation of activity criticality indices and sensitivity analysis of expected project completion time”, Journal of the Operational Research Society, 55, 850-859, 2004.
21.Chu, W.M., Yao, M.J., “A simulation-based genetic algorithm for the optimal resource allocation in probability activity networks”, The fifth Metaheuristics International Conference, 2003.
22.Chu, W.M., Yao, M.J, “On Improving the Electromagnetism Algorithm for Solving the Resource Allocation Problem in Probabilistic Activity Networks'”, The 36th International Conference on Computers and Industrial Engineering, 20~23 Jun, 2006.
23.Clark, C.E., “The greatest of a finite set of random variables”, Operations Research, 9, 146-162, 1961.
24.Colby, A.H., Elmaghraby S.E., “On the complete reduction of directed acyclic graphs”, OR Report No. 197, N.C. State Univ., Raleigh, NC., 1984.
25.Coppendate, J., “Manage risk in product and process development and avoid unpleasant surprises”, Engineering Management Journal, February, 35-38, 1995.
26.Cox, A., “Simple normal approximation to the completion time distribution for a PERT network”, International Journal of Project Management, 13/4, 265-270, 1995.
27.Dawson, R.J., Dawson, C.W., “Practical proposals for managing uncertainty and risk in project planning”, International Journal of Project Management, 16, 299-310, 1998.
28.Demeulemeester, E.L., Herroelen, W.S. & Elmaghraby, S.E., “Optimal procedures for the discrete time/cost trade-off problem”, European Journal of Operation Research, 88, 50-68, 1996.
29.Demeulemeester, E.L., Herroelen, W.S., “New benchmark results for the resource-constrained project scheduling problem”, Management Science, 43/11, 1485-1492, 1997.
30.Devroye, L.P., “Inequalities for the completion times of stochastic PERT networks”, Mathematic of Operations Research, 4/4, 441-447, 1979.
31.Dimitri, G.G., & Aharon, G., “Stochastic network project scheduling with non-consumable limited resources”, International Journal of Production Economics, 48, 29-37, 1997.
32.Dimitri, G.G. & Aharon, G., “A heuristic for network project scheduling with random activity durations depending on the resource allocation”, International Journal of Production Economics, 55, 149-162,1998.
33.Dimitri, G.G. & Aharon, G., Zohar, L., “Resource constrained scheduling simulation model for alternative stochastic network projects”, Mathematics and Computers in Simulation, 63, 105-117, 2003.
34.Dodin, B.M., “Determining the K most critical paths in PERT networks”, Operations Research, 32/4, 859-877, 1984.
35.Dodin B.M., “Bounding the project completion time distribution in PERT networks”, Operations Research, 33,/4, 862-881, 1985a
36.Dodin, B.M., “Approximating the distribution function in stochastic networks”, Computers and Operations Research, 12/3, 251-264,1985b.
37.Dodin, B.M., Elmaghraby, S.E., “Approximating the criticality indices of the activities in PERT networks”, Management Science, 31, 207-223, 1985c.
38.Dodin, B.M., Sirvanci, M., “Stochastic networks and the extreme value distribution”, Computers and Operations Research, 17/4, 397-409, 1990.
39.Dorp J.R., Duffey M.R., “Statistical dependence in risk analysis for project networks using Monte Carlo methods”, International Journal of Production Economics, 58, 17-29, 1999.
40.Elmaghraby, S.E., “On the expected duration of PERT type networks”, Management Science, 13, 299-306, 1967.
41.Elmaghraby, S.E., Activity networks: Project planning and control by network models, Wiley, New York, 1977.
42.Elmaghraby, S.E., Pulat, P.S., “Optimal project compression with dual-dated events”, Naval Research Logistics Quarterly, 26/2, 331-348, 1979.
43.Elmaghraby, S.E., Herroellen, W.S., “On the measurement of complexity in activity networks”, European Journal of Operational Research, 5, 223-234, 1980.
44.Elmaghraby, S.E., “Resource allocation via dynamic programming in activity networks”, European Journal of Operational Research, 88, 50-86, 1992.
45.Elmaghraby, S.E., “Activity nets: a guided tour through some recent developments”, European Journal of Operational Research, 82/3, 383-408, 1995.
46.Elmaghraby, S.E., “On criticality and sensitivity in activity networks”, European Journal of Operational Research, 127, 220-238, 2000.
47.Fatemi, G., Teimouri, E., “Path critical index and activity critical index in PERT networks”, European Journal of Operational Research, 141/1, 147-152, 2002.
48.Fatemi, G., Rabbani, M., “A new structural mechanism for reducibility of stochastic PERT networks”, European Journal of Operational Research, 145/2, 394-402, 2003.
49.Feng, C.W., Liu, L. and Burns, S.A., “Stochastic construction time-cost trade-off analysis”, Journal of Computing in Civil Engineering, 14, 117-126, 2000.
50.Fisher, D.L., Saisi, D., Goldstein, W.M., “Stochastic PERT networks: OP diagrams critical paths and the project completion time”, Computers and Operations Research, 12/5, 471-482, 1985.
51.Fishman, G.S., “Estimating network characteristics in stochastic activity networks”, Management Science, 31/5, 579-593, 1985.
52.Fulkerson, D.R., “Expected critical path lengths in PERT networks”, Operations Research, 10/6, 808-817, 1962.
53.Gallagher, C., “A note on PERT assumption”, Management Science, 33, 1360-1362, 1987.
54.Gallo, G. and S. Pallottino, “Shortest path algorithms”, Annals of Operations Research, 7, 3-79, 1988.
55.Glover, F., D. Klingman, and N. Philips, “A new polynomial bounded shortest path algorithm”, Operations Research, 33, 65-73, 1986.
56.Godfrey, C.O., Michael, M., “Optimizing the multiple constrained resources product mix problem using genetic algorithms”, International Journal of Production Research, 39/9, 1897-1910, 2001.
57.Hagstrom, J.N., “Computational complexity of PERT problems”, Networks, 18, 139-147, 1988.
58.Hagstrom, J.N., “Computing the probability distribution of project duration in a PERT network”, Networks, 20, 231-244, 1990.
59.Hardie, N., “The prediction and control of project duration: a recursive model”, International Journal of Project Management, 19, 401-409, 2001.
60.Harley, H.O., Wortham, A.W., “A statistical theory for PERT critical path analysis”, Management Science, 12/10, 469-481, 1966.
61.Hinddelang, T. J., Muth, J, F., “A dynamic programming algorithm for Decision CPM networks”, Operations Research, 27, 225-241, 1979.
62.Ho, Y.C., Eyler, M.A., Chien, T.T., “A gradient technique for general buffer storage buffer storage design in a serial production line”, International Journal of Production Research, 17/6, 557-580, 1979.
63.Holland, J. H., “Adaptation in natural and artificial systems”, Univ. of Michigan Press, Ann Arbot, Mich., 1975.
64.Kolisch, R. and A. Sprecher, “PSPLIB-A project scheduling problem library”, European Journal of Operational Research, 96, 205-216, 1996.
65.Kolisch, R., Drexl, A., “local search for nonpreemptive multi-mode resource-constrained project scheduling”, IIE transactions, 29, 987-999, 1997.
66.Kamburowski, J., “An overview of the computational complexity of the PERT shortest route and Maximum flow problems in stochastic networks”, Stochastic in Combinatorial Optimization, World Scientific Publishing, Singapore, 1987.
67.Kamburowski, J., “Upper bound on the expected completion time of PERT networks”, European Journal of Operational Research, 21/2, 206-212, 1985a.
68.Kamburowski, J., “Normally distributed activity durations in PERT networks”, European Journal of Operational Research, 36/11, 1051-1057, 1985b.
69.Kleindorfer, G.B., “Bounding distributions for stochastic logic”, Operations Research, 19/7, 1586-1601, 1971.
70.Klingel, A.R., Jr., “Bias in PERT project completion time calculations for a real network”, Management Science, 13/4, 476-489, 1966.
71.Kotiah, T.C.T., Wallace, N.D., “Another look at the PERT assumptions”, Management Science, 20/1, 44-49, 1973.
72.Kulkarni, V.G. & Adlakha V.G., “Markov and Markov-Regenerative PERT nwtworks”, Operations Research, 34/5, 769-781, 1986.
73.Leu, S.S., Yang, C.H., “GA-based multicriteria optimal model for construction scheduling”, Journal of Construction Engineering and Management, 125, 420-427, 1999.
74.Luis, C, Antonio, G., “Distribution network optimization: Finding the most economic solution by using genetic algorithms”, European Journal of Operational Research, 108, 527-537, 1998.
75.Martin, J.J., “Distribution of the time through a directed acyclic network”, Operational Research, 13, 46-66, 1965.
76.Malcom, D.G., Roseboom, J.H., Clark C.E. & Fazar W., “Application of technique for research and development program evaluation (PERT)”, Operations Research, 7/5, 646-669, 1959.
77.MacCrimmon, K.R., Ryacec, C.A., “An analytical study of the PERT assumptions”, Operations Research, 12, 16-37, 1964.
78.McBride, W.J., McCelland, C.W., “PERT and the beta distribution”, IEEE Transactions on Engineering Management, 14/4, 166-169, 1967.
79.Mehrotra, K., Chai, J. & Pillutal, S., “A study of approximating the moments of job completion time in PERT networks”, Journal of Operations Management, 14/3, 277-289, 1996.
80.Malcolm, D.G., J.H. Roseboom, C.E. Clark and W. Fazar, “Application of a technique for research and development program evaluation”, Operations Research, 7, 646-669, 1959.
81.Michel, C., Francesco, L.P., Salvatore, T., “A hierarchical approach for bounding the completion time distribution of stochastic task graphs”, Performance Evaluation, 41,1-22, 2000.
82.Molcolm, D. G., Roseboom, J.H., Clark, C.E., “Application of a technique for research and development program evaluation”, Operations Research, 7, 646-669, 1959.
83.Pape, U., “Implementation and efficiency of Moore-algorithms for the shortest route problem”, Mathematical Programming, 7, 212-222, 1974.
84.Premachandra, I.M., “An approximation of the activity duration distribution in PERT”, International Journal of Project Management, 18, 215-222, 2000.
85.Pontrandolfo, P., “Project duration in stochastic networks by the PERT-path technique”, International Journal of Project Management, 18, 215-222, 2000.
86.Ragsdale, C., “The current state of the network simulation in project management theory and practice”, OMEGA, 17/1, 21-25, 1989.
87.Ringer, L.J., “Numerical operators for statistical PERT critical path analysis”, Management Science, 16, 136-143, 1969.
88.Robillard, P., Trahan, M., “The completion time of PERT networks”, Operations Research. 25, 15-29, 1977.
89.Sasieni, W., “A note on PERT times”, Management Science, 32, 1652-1653.
90.Schmidt, C.W., Grossmann I.E., “The exact overall time distribution of a project with uncertain task durations”, European Journal of Operational Research, 126, 614-636, 2000.
91.Sculli, D., “The completion time of PERT networks”, Journal of the Operational Research Society, 25/1, 155-158, 1983.
92.Shogan, A.W., “Bounding distributions for a stochastic PERT network”, Networks, 7, 359-381, 1977.
93.Sigal, C.E., Pritsker, A.A.B., Solberg, J.J., “The use of cutsets in Monte Carlo analysis of stochastic networks”, Mathematics and Computers in Simulation, 21, 376-384, 1979.
94.Solis, F. J. and Wets, R. J-B., Minimization by random search techniques, Mathematics of Operations Research, 6, 19–30, 1981.
95.Splede, J.G., “Bounds for the distribution function of network variables”, First Symposium of Operations Research, 3, 113-123, 1977.
96.Sullivan, R.S., Hayya, J.C., “A comparison of the method of bounding distributions and Monte Carlo simulation for analyzing stochastic acyclic networks”, Operations Research, 28/3,614-617, 1980.
97.Tereso A.P., Araujo M.T., Elmaghraby S.E., “Adaptive resource allocation in multimodal activity networks”, International Journal of Production Economics, 92, 1~10, 2004a.
98.Tereso A.P., Araujo M.T., Elmaghraby S.E., “The optimal resource allocation in stochastic activity networks via the Electromagnetism approach”, Research Report, Universidade so Minho, Guimaraes, Portugal. Submitted for publication, 2004b.
99.Van, Slyke, R.M., “Monte Carlo methods and the PERT problem”, Operations Research, 11, 839-861, 1963.
100.Ward, S., Chapman, C., “Transforming project risk management into project uncertainty management”, International Journal of Project Management, 21/2, 97-105, 2003.
101.Wan, Y.W., “Resource allocation for a stochastic CPM-type network through perturbation analysis”, European Journal of Operational Research, 79, 239-248, 1994.
102.Wiest, J.D., Levy, F.K., “A Management Guide to PERT/CPM, Pretice-Hall”, Englewood Cliffs, NJ, 1977.
103.William, T.M., “Practical use of distributions in network analysis”, Journal of the Operational Society, 43/3, 265-270, 1962.
104.William, T.M., “Criticality in stochastic networks”, Operational Research Society Journal, 43, 353-357, 1992.
105.Yao M.J., Chu W.M., “A Label-Correcting Tracing Algorithm for the Approximation of the Probability Distribution Function of the Project Completion Time”, Journal of Chinese Institute of Industrial Engineers, Accepted, 2005.
106.Yao, M.J., Huang, J.X., ”Solving the Economic Lot Scheduling Problem with Deteriorating Items Using Genetic Algorithms,” Journal of Food Engineering, 2004 to appear.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top