請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91294
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃奎隆 | zh_TW |
dc.contributor.advisor | Kwei-Long Huang | en |
dc.contributor.author | 洪智健 | zh_TW |
dc.contributor.author | Derryadi Angputra | en |
dc.date.accessioned | 2023-12-20T16:21:28Z | - |
dc.date.available | 2023-12-21 | - |
dc.date.copyright | 2023-12-20 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-09-05 | - |
dc.identifier.citation | Alhanjouri, M. (2017). Optimization Techniques for Solving Travelling Salesman Problem. International Journal of Advanced Research in Computer Science and Software Engineering, 7, 165-174. https://doi.org/10.23956/ijarcsse/V7I3/01305
Bektur, G., & Saraç, T. (2019). A mathematical model and heuristic algorithms for an unrelated parallel machine scheduling problem with sequence-dependent setup times, machine eligibility restrictions and a common server. Computers & Operations Research, 103, 46-63. https://doi.org/https://doi.org/10.1016/j.cor.2018.10.010 Bureau of Energy, M. (2020). Promote Green Energy, Increase Nature Gas, Reduce Coal-fired, Achieve Nuclear-free. https://www.moea.gov.tw/Mns/english/Policy/Policy.aspx?menu_id=32904&policy_id=19 Castillo-Salazar, J. A., Landa-Silva, D., & Qu, R. (2016). Workforce scheduling and routing problems: literature survey and computational study. Annals of Operations Research, 239(1), 39-67. https://doi.org/10.1007/s10479-014-1687-2 Froger, A., Gendreau, M., Mendoza, J. E., Pinson, E., & Rousseau, L.-M. (2018). Solving a wind turbine maintenance scheduling problem. Journal of Scheduling, 21(1), 53-76. https://doi.org/10.1007/s10951-017-0513-5 G.-de-Alba, H., Nucamendi-Guillén, S., & Avalos-Rosales, O. (2022). A mixed integer formulation and an efficient metaheuristic for the unrelated parallel machine scheduling problem: Total tardiness minimization. EURO Journal on Computational Optimization, 10, 100034. https://doi.org/https://doi.org/10.1016/j.ejco.2022.100034 Glass, C. A., Potts, C. N., & Shade, P. (1994). Unrelated parallel machine scheduling using local search. Mathematical and Computer Modelling, 20(2), 41-52. https://doi.org/https://doi.org/10.1016/0895-7177(94)90205-4 Global Wind Energy Council, G. (2019). Global Wind Report 2019. https://gwec.net/global-wind-report-2019/ Hedjazi, D. (2015). Scheduling a maintenance activity under skills constraints to minimize total weighted tardiness and late tasks. International Journal of Industrial Engineering Computations, 6, 135-144. https://doi.org/10.5267/j.ijiec.2015.1.002 Irawan, C. A., Eskandarpour, M., Ouelhadj, D., & Jones, D. (2021). Simulation-based optimisation for stochastic maintenance routing in an offshore wind farm. European Journal of Operational Research, 289(3), 912-926. https://doi.org/https://doi.org/10.1016/j.ejor.2019.08.032 Irawan, C. A., Ouelhadj, D., Jones, D., Stålhane, M., & Sperstad, I. B. (2017). Optimisation of maintenance routing and scheduling for offshore wind farms. European Journal of Operational Research, 256(1), 76-89. https://doi.org/https://doi.org/10.1016/j.ejor.2016.05.059 Király, A., & Abonyi, J. (2011). Optimization of Multiple Traveling Salesmen Problem by a Novel Representation Based Genetic Algorithm. In M. Köppen, G. Schaefer, & A. Abraham (Eds.), Intelligent Computational Optimization in Engineering: Techniques and Applications (pp. 241-269). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_9 Logendran, R., McDonell, B., & Smucker, B. (2007). Scheduling unrelated parallel machines with sequence-dependent setups. Computers & Operations Research, 34(11), 3420-3438. https://doi.org/https://doi.org/10.1016/j.cor.2006.02.006 Marmier, F., Varnier, C., & Zerhouni, N. (2006). Maintenance Activities Scheduling Under Competencies Constraints. Proceedings - ICSSSM'06: 2006 International Conference on Service Systems and Service Management, 2. https://doi.org/10.1109/ICSSSM.2006.320682 MOEA, T. (2023). Annual electricity consumption per capita in Taiwan from 2012 to 2022 (in kilowatt hours) [Graph]. In Statista. Retrieved July 2023 from https://www.statista.com/statistics/334268/taiwan-per-capita-electricity-consumption/ Öztaş, T., & Tuş, A. (2022). A hybrid metaheuristic algorithm based on iterated local search for vehicle routing problem with simultaneous pickup and delivery. Expert Systems with Applications, 202, 117401. https://doi.org/https://doi.org/10.1016/j.eswa.2022.117401 Sarkar, A., & Behera, D. K. (2012). Wind Turbine Blade Efficiency and Power Calculation with Electrical Analogy. International Journal of Scientific and Research Publications, 2(2). United Nations, U. (2019). Sustainable Development Goal 7 Report : Ensure access to affordable, reliable, sustainable and modern energy for all. In. https://unstats.un.org/sdgs/report/2021/goal-07/ Vallada, E., & Ruiz, R. (2011). A genetic algorithm for the unrelated parallel machine scheduling problem with sequence dependent setup times. European Journal of Operational Research, 211(3), 612-622. https://doi.org/https://doi.org/10.1016/j.ejor.2011.01.011 Yu, Q., Bangalore, P., Fogelström, S., & Sagitov, S. (2021). Optimal preventive maintenance scheduling for wind turbines under condition monitoring. Zhang, J., Chowdhury, S., Zhang, J., Tong, W., & Messac, A. (2012). Optimal Preventive Maintenance Time Windows for Offshore Wind Farms Subject to Wake Losses. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91294 | - |
dc.description.abstract | 本研究專注於開發針對台灣風力渦輪機的維護規劃和途程問題的解決方案,目標是減少可能發生的能源損失,我們稱之為風力渦輪機維護排程和途程問題(TMSRP)。風力渦輪機分佈在台灣各地的許多風場,這些風場風速變化不定且擁有不同數量的風力渦輪機。這些發電機可以根據其能源輸出劃分為三種類型, 1.500 kW, 2.000 kW, 和2.500 kW。如果發電機出現問題,可能會導致其立即停止運行,或者在特定到期日後如果未維修則停止運行。為了限制能源損失,一些擁有不同技能的團隊將負責排除故障,每個團隊都有預定的工作時間,從中央出發移動到風場之間,並考慮到旅行時間。研究的主要目標是規劃維護和任務途程,以最小化可能的能源損失,並考慮到維修處理時間、團隊技能和任務到期日。
本研究提出了一種基於潛在能源損失價值和團隊特性的排程演算法TMSRP-貪婪算法(TMSRP-GA),以產生日維修某排程和途程,並提出了兩種排程改善算法Weighted Randomness (WR) 和Iterative Max-Min Interchange (IMMI)。通過實驗結果,我們發現TMSRP-GA能夠構建一種相比於其他排程演算法有最小目標值的排程別外,WR有最低的計算時間和良好的整體降低目標函數。然而IMMI對初始解有依賴性,需要大量計算時間來收斂。 | zh_TW |
dc.description.abstract | This research focused on developing a solution to the maintenance planning and routing problem for wind turbines in Taiwan, which aims to reduce the amount of energy loss that may occur which we called Turbine Maintenance Scheduling and Routing Problem (TMSRP). Wind turbines were built in numerous wind-farms spread across Taiwan, and these wind farms have different numbers of turbines with varying wind speeds that change unpredictably. The turbines can be categorized into three types based on their energy output. If a problem arises in a turbine, it could cause it to stop functioning immediately or after a particular due date if not repaired. To limit energy loss, teams with varying skills will handle the troubleshooting, each with a predetermined working hours, starting from a central warehouse and moving between wind-farms, accounting for travel time. The primary objective of the research is to plan maintenance and routing of tasks in such a way that minimizes potential energy loss by considering processing times, team skills, and task due dates.
The study proposes a schedule construction algorithm TMSRP-Greedy Algorithm (TMSRP-GA) based on the potential energy loss worth and team characteristics to generate the schedule along with the routing for one day horizon, and also two schedule improvement algorithms WR and IMMI which then compared with baselines algorithm. Through the experimental result, we found out that TMSRP-GA was able to construct a schedule with a minimized objective value compared to other compared schedule construction algorithm, and WR also performed well and consistent with lowest computational time and good overall reduction in objective function. IMMI showed dependency to the initial solution and required a lot of computational time to converge. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-12-20T16:21:28Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-12-20T16:21:28Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Table of Contents
Acknowledgements i 摘要 iii Abstract iv List of Figures viii List of Tables x Chapter 1 Introduction 1 Chapter 2 Literature Review 5 2.1. Maintenance Activity Scheduling in Wind Turbine Industry 5 2.2. Unrelated Machine Scheduling Problem 6 2.3. Routing Problem 7 Chapter 3 Problem Description 10 3.1. Problem Description 10 3.1.1. Time units 10 3.1.2. Problem definition 10 3.1.3. Illustrative Example 19 Chapter 4 Proposed Algorithms 22 4.1. Schedule Construction Algorithms 22 4.1.1. TMSRP-Greedy Algorithm (TMSRP-GA) 22 4.1.2. EDD-SCT 26 4.1.3. Dispatcher’s Rule (DR) 27 4.2. Schedule Improvement Algorithms 28 4.2.1. First stage 28 4.2.2. Second Stage 29 4.2.3. Baseline Algorithms 33 Chapter 5 Result and Discussion 36 5.1. Experiment setting 36 5.2. Experiment Results 38 5.2.1. Schedule Construction Algorithm Results 38 5.2.2. Schedule Improvement Algorithm 39 Chapter 6 Conclusion and Future Work 47 References 48 | - |
dc.language.iso | en | - |
dc.title | 風力渦輪機維修排程與維修途程規劃之研究 | zh_TW |
dc.title | Maintenance Planning and Routing Optimization for Wind Turbine Troubleshooting | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 鄭辰仰;吳政翰 | zh_TW |
dc.contributor.oralexamcommittee | Chen-Yang Cheng;Gen-Han Wu | en |
dc.subject.keyword | 風力渦輪機維修,服務團隊途程,最小化能源損失,排程演算法, | zh_TW |
dc.subject.keyword | Wind turbine maintenance,Service team routing,Minimize energy loss,Schedule algorithms, | en |
dc.relation.page | 51 | - |
dc.identifier.doi | 10.6342/NTU202304138 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-09-05 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 工業工程學研究所 | - |
顯示於系所單位: | 工業工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-112-1.pdf 目前未授權公開取用 | 5.28 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。