工程最佳化模式不確定性參數值分析 -以專案排程模式為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：45

、訪客IP：18.191.27.78

姓名

王信翔(Sin-Siang Wang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

工程最佳化模式不確定性參數值分析 -以專案排程模式為例
(Analysis of Uncertain Parameter Values included in Engineering Optimization Models: A Case Study of a Project Scheduling Model)

相關論文

★ 橋梁檢測人力機具排班最佳化之研究	★ 勤業務專責分工下消防人員每日勤務排班最佳模式之研究
★ 司機員排班作業最佳化模式之研究	★ 科學園區廢水場實驗室檢驗員任務指派最佳化模式之研究
★ 倉儲地坪粉光工程之最佳化模式研究	★ 生下水道工程工作井佈設作業機組指派最佳化之研究
★ 急診室臨時性短期護理人力指派最佳化之探討	★ 專案監造人力調派最佳化模式研究
★ 地質鑽探工程人機作業管理最佳化研究	★ 職業棒球球隊球員組合最佳化之研究
★ 鑽堡於卵礫石層施作機具調派最佳化模式之研究	★ 職業安全衛生查核人員人力指派最佳化研究
★ 救災機具預置最佳化之探討	★ 水電工程出工數最佳化之研究
★ 石門水庫服務台及票站人員排班最佳化之研究	★ 空調附屬設備機組維護保養排程最佳化之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

決策者為了有效解決各類型的工程最佳化問題，並獲得最佳的決策方案，已將工程最佳化模式廣泛地應用於工程界中。而當面臨實務的工程問題時，最佳化模式中的某些參數可能是不確定性的，因不確定性參數值通常無法被精確估算，故將具有誤差。如果有不確定性參數被包含在工程最佳化模式中時，亦即模式輸入具有誤差時，則該模式解亦可能存在或多或少的誤差，亦即模式輸出具有誤差。在此情況下，決策者無法根據該模式解制定出最佳的決策方案。以往有關估算不確定性參數值的研究主要是使用推估或預測程序找出較合適的不確定性參數值並將其視為模式輸入資料。然而，使用那些估算的參數值所獲得的模式解的誤差仍然無法得知。因為無法知道真實的最佳解，故此等研究的模式解只能與先前所獲得的最好的解相互比較，並無法知道模式解與真實最佳解之間的差距為何，亦難以客觀證實此等研究的模式解的績效。此外，過去已有許多的研究使用近似解演算法，在給定容許誤差下以提升模式求解效率，但尚未有研究探討不同的求解容許誤差對於具有輸入誤差的模式解的影響。因此，本研究主要是探討工程最佳化模式中不確定性參數值，具有不同的可控制誤差與隨機誤差情境，並使用不同的求解容許誤差設定時，模式輸出的誤差為何，亦即評估求解之最佳性。
最佳化數學規劃模式通常具有兩類不確定性參數。一類為目標函數之不確定性參數，另一類為限制式之不確定性參數。因這兩類參數的誤差分析結果可能具有差異性，故本研究分為三個部份以分別進行探討與分析。在第一部份中，本研究發展一實驗評估方法以評估當工程最佳化模式中屬於目標函數的不確定性參數值，具有不同的可控制誤差與隨機誤差情境，並使用不同的求解容許誤差設定時，模式輸出的誤差為何。在第二部份中，本研究亦發展一實驗評估方法以評估當工程最佳化模式中屬於限制式的不確定性參數值，具有不同的可控制誤差與隨機誤差情境，並使用不同的求解容許誤差設定時，模式輸出的誤差為何。在第三部份中，本研究結合前兩部份的方法，發展一實驗評估方法以評估當工程最佳化模式中屬於目標函數及限制式的不確定性參數值，具有不同的可控制誤差與隨機誤差情境，並使用不同的求解容許誤差設定時，模式輸出的誤差為何。本研究以一工程專案最佳化排程模式作為此三部份的測試模式，以能夠方便比較此三部份的分析結果。此外，本研究為進一步了解在不同的誤差情境及使用不同的求解容許誤差設定時，模式輸入誤差、求解容許誤差與模式輸出誤差間的相關性，故將此三部份的分析結果進行迴歸分析。最後，本研究從分析結果中歸納出實務上在設計最佳化模式及求解演算法時的注意事項與管理意涵。

摘要(英)

Decision makers have widely applied engineering optimization models in the field of engineering in order to effectively solve various types of engineering optimization problems and secure optimal decisions. However, confronted with practical engineering problems, some parameters of the optimization model may be uncertain. Uncertain parameter values are hard to accurately estimate, causing that they include errors. In case there are uncertain parameters involved in the engineering optimization model (i.e., the model input includes errors), the obtained solution may also include errors (i.e., the model output includes errors). In this case, the decision makers cannot make the optimal decisions. In the past, the studies regarding estimating uncertain parameter values have chiefly utilized the estimation or prediction approach to find the proper uncertain parameter values that can be used as model input data. However, there could still be unknown errors in model solutions obtained using estimated uncertain parameter values. Since it is hard to obtain a real optimal solution for a model that contains uncertain parameters, the evaluation of these solutions is carried out mainly by comparing them with the best solution secured previously. The gap between the obtained model solution and real optimal solution is unknown, that is to say, the performance of the solutions secured from previous studies cannot be confirmed objectively. Additionally, although there have been many studies that have employed approximate solution algorithms with a solution tolerance error to enhance the solution efficiency, there have not been any studies that further explore the effect of various solution tolerance errors on model solutions with input errors. Thus, the purpose of this study is to explore the output errors for an engineering optimization model that includes uncertain parameter values under different controllable and random error scenarios, coupled with different solution tolerance error settings (i.e., this study focuses on evaluating the optimality of model solutions with input errors).
There are usually two sorts of uncertain parameters included in an optimal mathematical programming model. One is the uncertain parameters included in the objective function; the other is the uncertain parameters included in the constraint set. The results of the error analysis of the two types of uncertain parameters may be different. In order to reflect this, this dissertation is divided into three essays. In the first essay, an experimental evaluation approach is developed to evaluate the output errors of an engineering optimization model in which uncertain parameter values are included in the objective function, under various controllable and random error scenarios, coupled with various solution tolerance error settings. The second essay also develops an experimental evaluation approach to evaluate the output errors of an engineering optimization model in which uncertain parameter values are included in the constraint set, under various controllable and random error scenarios, coupled with various solution tolerance error settings. In the third essay, the methods discussed in the first two essays are combined to develop an experimental evaluation approach to evaluate the output errors of an engineering optimization model in which uncertain parameter values are included in the objective function and the constraint set, under various controllable and random error scenarios, coupled with various solution tolerance error settings. To facilitate comparison of the test results, the same engineering project optimization scheduling model is used in the testing in all three essays. In addition, regression analysis of the test results of each error scenario associated with the three essays is also implemented to further comprehend how model input errors (i.e., controllable and random errors) and solution tolerance errors affect model output errors. Finally, some useful information and managerial meanings for designing optimization models and solution algorithms in practice are extrapolated from the test results.

關鍵字(中)

★ 工程最佳化模式
★ 專案排程模式
★ 不確定性參數
★ 模式輸入誤差
★ 可控制誤差
★ 隨機誤差
★ 求解容許誤差
★ 模式輸出誤差

關鍵字(英)

★ engineering optimization model
★ project scheduling model
★ uncertain parameter
★ model input error
★ controllable error
★ random error
★ solution tolerance error
★ model output error

論文目次

摘要 i
Abstract ii
誌謝 iv
Introduction 1
Chapter 2 5
Essay 1: An Experimental Method for the Evaluation of Output Errors for Engineering Optimization Models with Uncertain Parameters 5
2.1 Introduction 5
2.2 Introduction of the tested engineering optimization model 8
2.3 Method for evaluating the model output errors 9
2.4 Error testing over uncertain parameter values 11
2.4.1 Data analysis 11
2.4.2 Test results 14
2.4.3 Regression analysis of test results 19
2.4.4 Important findings obtained from the test results 23
2.5 Conclusions 26
Chapter 3 29
Essay 2: The Development of an Output Error Evaluation Method for Engineering Optimization Models containing Uncertain Parameters 29
3.1 Introduction 29
3.2 Introduction of the tested optimization model 31
3.3 Identification of uncertain parameters included in the constraint set of the model 32
3.4 Modification of the model 33
3.5 Evaluation method for model output errors 34
3.6 Error tests to uncertain parameter values 36
3.6.1 Input data for the modified model 37
3.6.2 Test results 39
3.6.3 Regression analysis of test results 44
3.6.4 Significant findings secured from the test results 47
3.7 Conclusions 50
Chapter 4 53
Essay 3: The Exploration of the Output Errors of Engineering Optimization Models with Uncertain Parameters 53
4.1 Introduction 53
4.2 Introduction of MRCPSPDCF 56
4.3 Judgment of uncertain parameters included in the model 56
4.4 Revision of the model 58
4.5 An approach for the evaluation of model output errors 59
4.6 Error tests for uncertain parameter values 61
4.6.1 Parameter value settings for the revised model 62
4.6.2 Output results 64
4.6.3 Regression analysis for test results 69
4.6.4 Important discoveries obtained from the output results 73
4.7 Conclusions 76
Chapter 5 78
Conclusions, Suggestions and Contributions 78
5.1 Conclusions 78
5.2 Suggestions 80
5.3 Contributions 81
References 82
Appendix 1 86
Appendix 2 88
Appendix 3 92
Appendix 4 95
Appendix 5 99
Appendix 6 102
Appendix 7 106

參考文獻

Albert, W.L., Yao, S.C., and Chi, “Analysis and Design of a Taguchi-Grey based Electricity Demand Predictor for Energy Management Systems,” Energy Conversion and Management, Vol. 45, pp. 1205-1217 (2004).
Aleksandar, D.J., Dragan, S.P., and Snezana, P.T., “Green Vehicle Routing in Urban Zones-A Neuro-Fuzzy Approach,” Expert Systems with Applications, Vol. 41, pp. 3189-3203 (2014).
Ali, B.A., Jabalameli, M. S., and Mirzapour, S. M. J. “A Multi-Objective Robust Stochastic Programming Model for Disaster Relief Logistics under Uncertainty,” OR Spectrum, Vol. 35, pp. 905-933 (2013).
Amiruddin, I., Mohammad, H.H., Foad, S., Mojtaba, S.B., and Mohammad, G., “Bus Scheduling Model User Interface,” Australian Journal of Basic and Applied Sciences, Vol. 6, pp. 181-184 (2012).
An, N., Zhao, W., Wang, J., Shang, D., and Zhao, E., “Using Multi-Output Feedforward Neural Network with Empirical Mode Decomposition based Signal Filtering for Electricity Demand Forecasting,” Energy, Vol. 49, pp. 279-288 (2013).
Better, M., Glover, F., and Laguna, M., “Advances in Analytics: Integrating Dynamic Data Mining with Simulation Optimization,” IBM journal of Research and Development, Vol. 51, pp. 477-487 (2007).
Chang, H.W., Tai, Y.C., and Hsu, Y.J., “Context-Aware Taxi Demand Hotspots Prediction,” Business Intelligence and Data Mining, Vol. 5, pp. 3-18 (2010).
Chen, C.F., Lai, M.C., and Yeh, C.C., “Forecasting Tourism Demand based on Empirical Mode Decomposition and Neural Network,” Knowledge-Based Systems, Vol. 26, pp. 281-287 (2012).
Chen, C.J., “A Self-Organized Neuro-Fuzzy System for Air Cargo and Airline Passenger Dynamics Modeling and Forecasting,” International Journal of Fuzzy System Applications, Vol. 2, pp. 36-49 (2012).
Chen, D., and Wu, H., “Research on Optimization Model and Algorithm of Initial Schedule of Intercity Passenger Trains based on Fuzzy Sets,” Journal of Software, Vol. 7, pp. 49-54 (2012).
Chen, D., Lv, M., and Ni, S., “Study on Initial Schedule Optimization Model of Intercity Passenger Trains based on ACO Algorithm,” International Journal of Advancements in Computing Technology, Vol. 3, pp. 222-228 (2011).
Chen, H., “An Urban Traffic Prediction Model based on Temporal Data Mining in Shanghai City,” Advances in Intelligent Systems and Computing, Vol. 181, pp. 633-639 (2013).
Chen, M., Yan, S., Wang, S.S., and Liu, C.L., “A Generalized Network Flow Model for the Multi-Mode Resource Constrained Project Scheduling Problem with Discounted Cash Flows,” Engineering Optimization, Vol. 47, No. 2, pp. 165-183 (2015).
Chiang, W.Y., “Establishment and Application of Fuzzy Decision Rules: An Empirical Case of the Air Passenger Market in Taiwan,” International Journal of Tourism Research, Vol. 13, pp. 447-456 (2011).
Choi, S.Y., “Short-Term Power Demand Forecasting using Information Technology based Data Mining Method,” Computational Science and ITS Applications, Vol. 3984, pp. 322-330 (2006).
David, J.S., Riham, A.K., and Lawrence, M.M., “Cost Model Development using Virtual Manufacturing and Data Mining: Part II—Comparison of Data Mining Algorithms,” International Journal of Advanced Manufacturing Technology, Vol. 66, pp. 1389-1396 (2013).
Deng, W., Li, W., and Yang, X.H., “A Novel Hybrid Optimization Algorithm of Computational Intelligence Techniques for Highway Passenger Volume Prediction,” Expert Systems with Applications, Vol. 38, pp. 4198-4205 (2011).
Dobrila, P., Rajat, R., and Radivoj, P., “Supply Chain Modelling using Fuzzy Sets,” International Journal of Production Economics, Vol. 59, pp. 443-453 (1999).
Garey, M.R., and Johnson, D.S., Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman & Company, San Francisco, CA (1979).
Gholamreza, Z., Saeed, A., Alireza, B., Ali, E., and Sharifah, R.W.A. “Electricity Demand Estimation using an Adaptive Neuro-Fuzzy Network: A Case Study from the Ontario Province-Canada,” Energy, Vol. 49, pp. 323-328 (2013).
Hsu, C.I., Li, H.C., Liu, S.M., and Chao, C.C., “Aircraft Replacement Scheduling: A Dynamic Programming Approach,” Transportation Research Part E, Vol. 47, pp. 41-60 (2011).
Hsu, C.I., and Wen, Y.H., “Application of Grey Theory and Multi-objective Programming towards Airline Network Design,” European Journal of Operational Research, Vol. 127, pp. 44-68 (2000).
Huang, Y.L., and Lee, Y.H. “Accurately Forecasting Model for the Stochastic Volatility Data in Tourism Demand,” Modern Economy, Vol. 2, pp. 823-829 (2011).
Kenyon, A.S., and Morton, D.P., “Stochastic Vehicle Routing with Random Travel Times,” Transportation Science, Vol. 37, pp. 69-82 (2003).
Kline, S.T., and Mcclintock, F.A., “Describing Uncertainties in Single-Sample Experiments,” Mechanical Engineering, Vol. 75, pp. 3-8 (1953).
Kolisch, R., Sprecher, A., and Drexl, A., “Characterization and Generation of a General Class of Resource-Constrained Project Scheduling Problems,” Management Science, Vol. 41, pp. 1693-1703(1995).
Lee, W.H., Tseng, S.S., Shieh, J.L., and Chen, H.H. “Discovering Traffic Bottlenecks in an Urban Network by Spatiotemporal Data Mining on Location-Based Services,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12, pp. 1047-1056 (2011).
Lee, Y.S., and Tong, L.I., “Forecasting Energy Consumption using A Grey Model Improved by Incorporating Genetic Programming,” Energy Conversion and Management, Vol. 52, pp. 147-152 (2011).
Lin, F.T., and Yao, J.S., “Using Fuzzy Numbers in Knapsack Problems,” European Journal of Operational Research, Vol. 135, pp. 158-176 (2001).
Miloš, M., and Nebojša, B., “A Fuzzy Random Model for Rail Freight Car Fleet Sizing Problem,” Transportation Research Part C, Vol. 33, pp. 107-133 (2013).
Namk and Schaefer, “Forecasting International Airline Passenger Traffic using Neural Networks,” Logistics and Transportation Review, Vol. 31, pp. 239-251 (1995).
Parpinelli, Heitor, S., Rafael, S., Lopes, and Alex, A.,“Data Mining with an Ant Colony Optimization Algorithm,”IEEE Transactions on Evolutionary Computing, Vol. 6, No.4, pp. 263-275 (2002).
Rafael, B.C.B., Rafael, B.C.P., Gabriel, L., and Joao, L.N., “Damp Trend Grey Model Forecasting Method for Airline Industry,” Expert Systems with Applications, Vol. 40, pp. 4915-4921 (2013).
Shanmugasundari, M., and Ganesan, K., “A Novel Approach for the Fuzzy Optimal Solution of Fuzzy Transportation Problem,” International Journal of Engineering Research and Applications, Vol. 3, pp. 1416–1421 (2013).
Tam, L., Taniar, D., and Smith, K.,“Parametric Optimization in Data Mining Incorporated with GA-based Search,”Computational Science, Vol. 2329, pp. 582-591 (2002).
Teodorovic, D., “Fuzzy Logic Systems for Transportation Engineering : the State of the Art,” Transportation Research Part A, Vol. 33, pp. 337-364 (1999).
Teodorovic, D., Kalic, M., and Pavkovic, G., “The Potential for using Fuzzy Set Theory in Airline Network Design,” Transportation Research Part B – Methodological, Vol. 25, pp. 103-121 (1994).
Teodorovic, D., and Pavkovic, G., “The Fuzzy Set Theory Approach to the Vehicle Routing Problem When Demand at Nodes Is Uncertain,” Fuzzy sets and system, Vol. 82, pp. 307-317 (1995).
Tsai, T.H., Lee, C.K., and Wei, C.H., “Neural Network based Temporal Feature Models for Short-Term Railway Passenger Demand Forecasting,” Expert Systems with Applications, Vol. 36, pp. 3728-3736 (2009).
Wang, C.H., “Predicting Tourism Demand using Fuzzy Time Series and Hybrid Grey Theory,” Tourism Management, Vol. 25, pp. 367-374 (2004).
Wang, C.N., and Phan, V.T., “An Improvement the Accuracy of Grey Forecasting Model for Cargo Throughput in International Commercial Ports of Kaohsiung,” International Journal of Business and Economics Research, Vol. 3, pp. 1-5 (2014).
Wang, Z.X., “A Genetic Algorithm-based Grey Method for Forecasting Food Demand after Snow Disasters: An Empirical Study,” Natural Hazards, Vol. 68, pp. 675-686 (2013).
Wanigasooriya, J., and Gifernando, T., “Multi-Vehicle Passenger Allocation and Route Optimization for Employee Transportation using Genetic Algorithms,” International Journal of Computer Applications, Vol. 64, pp. 1-9 (2013).
Wei, Y., and Chen, M.C., “Forecasting the Short-Term Metro Passenger Flow with Empirical Mode Decomposition and Neural Networks,” Transportation Research Part C, Vol. 21, pp. 148-162 (2012).
Weltner, K., Weber, W.J., Grosjean, J., and Schuster, P., “Theory of Errors,” Mathematics for Physicists and Engineers, pp. 537-556 (2009).
Wen, Y.H., “Shipment Forecasting for Supply Chain Collaborative Transportation Management using Grey Models with Grey Numbers,” Transportation Planning and Technology, Vol. 34, pp. 605-624 (2011).
William, C.C., Asunción, P.C., and Antonio, F.C., “Optimal Design of Energy-Efficient ATO CBTC driving for Metro Lines based on NSGA-II with Fuzzy Parameters,” Engineering Applications of Artificial Intelligence, Vol. 36, pp. 164-177 (2014).
Wu, H.H., Liao, A.Y.H., and Wang, P.C., “Using Grey Theory in Quality Function Deployment to Analyse Dynamic Customer Requirements,” International Journal of Advanced Manufacturing Technology, Vol. 25, pp. 1241-1247 (2005).
Xiao, Y., Liu, J.J., Hu, Y., Wang, Y., Lai, K.K., and Wang, S., “A Neuro-Fuzzy Combination Model based on Singular Spectrum Analysis for Air Transport Demand Forecasting,” Journal of Air Transport Management, Vol. 39, pp. 1-11 (2014).
Xue, H.W., and Norrie, D.H., “A Fuzzy Mathematics based Optimal Delivery Scheduling Approach,” Computers in Industry, Vol. 45, pp. 245-259 (2001).
Yan, S., Chi, C.J., and Tang, C.H., “Inter-City Bus Routing and Timetable Setting under Stochastic Demands,” Transportation Research Part A, Vol. 40, pp. 572-586 (2006).
Yan, S., and Tang, C.H., “Inter-City Bus Scheduling under Variable Market Share and Uncertain Market Demands,” OMEGA - The International Journal of Management Science, Vol. 37, pp. 178-192 (2009).
Yan, S., Tang, C.H., and Fu, T.C., “An Airline Scheduling Model and Solution Algorithms under Stochastic Demands,” European Journal of Operational Research, Vol. 190, pp. 22-39 (2008).
Yan, S., Wang, S.S., and Chang, Y.H., “Cash Transportation Vehicle Routing and Scheduling under Stochastic Travel Times,” Engineering Optimization, Vol. 46, pp. 289-307 (2014).
Zhineng, H.,Yixin, Z., and Liming, Y., “Radial Basis Function Neural Network with Particle Swarm Optimization Algorithms for Regional Logistics Demand Prediction,” Discrete Dynamics in Nature and Society, Vol. 2014, pp. 1-13 (2014).
Zhu, C., and Li, H., “Research on Optimization Algorithm based on Random Gray Ant Colony Neural Network and Its Applications,” Journal of Information and Computational Science, Vol. 9, pp. 2475-2483 (2012).

指導教授

顏上堯(Shangyao Yan)

審核日期

2015-7-23

推文