跳到主要內容

臺灣博碩士論文加值系統

(18.97.9.172) 您好!臺灣時間:2025/02/16 21:00
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:Indy Cesara
研究生(外文):Indy Cesara
論文名稱:運用重疊設計時程為基之相依結構矩陣於專案時間最小化之研究
論文名稱(外文):Applying An Overlapped Design Schedule Based Dependency Structure Matrix to Minimize Project Makespan
指導教授:歐陽超歐陽超引用關係
指導教授(外文):Chao Ou-Yang
口試委員:郭人介阮業春
口試委員(外文):Ren-Jieh KuoYeh-Chun Juan
口試日期:2017-07-24
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:74
中文關鍵詞:Human Resources AllocationProject MakespanDependency Structure MatrixWorker ClusterOverlapped Design ProcessRework Time
外文關鍵詞:Human Resources AllocationProject MakespanDependency Structure MatrixWorker ClusterOverlapped Design ProcessRework Time
相關次數:
  • 被引用被引用:0
  • 點閱點閱:115
  • 評分評分:
  • 下載下載:8
  • 收藏至我的研究室書目清單書目收藏:0
Design process scheduling is conducted by optimizing human resources or workers allocation to several tasks in project with several real constraint to achieve the objective, minimizing the project makespan. Minimizing the project makespan means minimizing the product launching time too. Thus their product could be more competitive than competitor in market. In real business case, despite the tasks are already allocated to the optimal workers, rework still can happen because of the uncertainty. The rework can lead to unexpected extra time consumption. To anticipate this unexpected problem, tasks overlapping method is proposed in this research to reduce the project makespan after worker allocation.

The time reduction could be used as rework time, therefore extra time would not be needed. However the task overlapping method has a drawback, it requires more coordination and interaction between workers who conduct each of overlapping tasks. This research emphasize overlapped design schedule in a design process based Dependency Structure Matrix (DSM). Worker DSM is used to encounter the task overlapping’s drawback. Worker DSM could identify worker cluster. Workers who come from same cluster have more coordination and interaction to each other. It is expected workers who assigned on tasks overlapped are from same cluster. This research’s intention is to provide the workers allocation to obtain optimal project makespan result and create worker clustering from worker-DSM to identify the tasks that can be overlapped in design process. It is also expected to show the possible rework time from tasks overlapping’s reduction time.
Design process scheduling is conducted by optimizing human resources or workers allocation to several tasks in project with several real constraint to achieve the objective, minimizing the project makespan. Minimizing the project makespan means minimizing the product launching time too. Thus their product could be more competitive than competitor in market. In real business case, despite the tasks are already allocated to the optimal workers, rework still can happen because of the uncertainty. The rework can lead to unexpected extra time consumption. To anticipate this unexpected problem, tasks overlapping method is proposed in this research to reduce the project makespan after worker allocation.

The time reduction could be used as rework time, therefore extra time would not be needed. However the task overlapping method has a drawback, it requires more coordination and interaction between workers who conduct each of overlapping tasks. This research emphasize overlapped design schedule in a design process based Dependency Structure Matrix (DSM). Worker DSM is used to encounter the task overlapping’s drawback. Worker DSM could identify worker cluster. Workers who come from same cluster have more coordination and interaction to each other. It is expected workers who assigned on tasks overlapped are from same cluster. This research’s intention is to provide the workers allocation to obtain optimal project makespan result and create worker clustering from worker-DSM to identify the tasks that can be overlapped in design process. It is also expected to show the possible rework time from tasks overlapping’s reduction time.
Design process scheduling is conducted by optimizing human resources or workers allocation to several tasks in project with several real constraint to achieve the objective, minimizing the project makespan. Minimizing the project makespan means minimizing the product launching time too. Thus their product could be more competitive than competitor in market. In real business case, despite the tasks are already allocated to the optimal workers, rework still can happen because of the uncertainty. The rework can lead to unexpected extra time consumption. To anticipate this unexpected problem, tasks overlapping method is proposed in this research to reduce the project makespan after worker allocation.

The time reduction could be used as rework time, therefore extra time would not be needed. However the task overlapping method has a drawback, it requires more coordination and interaction between workers who conduct each of overlapping tasks. This research emphasize overlapped design schedule in a design process based Dependency Structure Matrix (DSM). Worker DSM is used to encounter the task overlapping’s drawback. Worker DSM could identify worker cluster. Workers who come from same cluster have more coordination and interaction to each other. It is expected workers who assigned on tasks overlapped are from same cluster. This research’s intention is to provide the workers allocation to obtain optimal project makespan result and create worker clustering from worker-DSM to identify the tasks that can be overlapped in design process. It is also expected to show the possible rework time from tasks overlapping’s reduction time
Adam, T., Hashim, U., Leow, P. L., Foo, K. L., & Chee, P. S. (2013). Selection of optimal parameters in fabrication of poly (dimethylsiloxane) microfluidics using taguchi method. Advanced Science Letters, 19(1), 32-36.

Banerjee, N., Mehta, V., & Pandey, S. (2004, February). A genetic algorithm approach for solving the routing and wavelength assignment problem in WDM networks. In 3rd IEEE/IEE international conference on networking, ICN(pp. 70-78).

Bogus, S. M., Molenaar, K. R., & Diekmann, J. E. (2005). Concurrent engineering approach to reducing design delivery time. Journal of construction engineering and management, 131(11), 1179-1185.

Browning, T. R., & Eppinger, S. D. (2002). Modeling impacts of process architecture on cost and schedule risk in product development. IEEE transactions on engineering management, 49(4), 428-442.

Browning, Tyson R. (2009). Using the Design Structure Matrix to Design Program Organizations. In Handbook of Systems Engineering and Management, 2nd ed., eds. Andrew P. Sage and William B. Rouse. New York: Wiley, pp. 1401-1424.

Cabanillas, C., García, J.M., Resinas, M., Ruiz, D., Mendling, J. & Ruiz-Cortés, A. (2013). Priority-based human resource allocation in business processes. In S. Basu, C. Pautasso, L. Zhang & X. Fu (eds), Service-Oriented Computing: 374- 388. Berlin: Springer Berlin Heidelberg.

Carrascosa, M., Eppinger, S. D., & Whitney, D. E. (1998). Using the design structure matrix to estimate product development time. In Proceedings of the ASME design engineering technical conferences (design automation conference) (pp. 1-10).

Chang, C. K., Christensen, M. J., & Zhang, T. (2001). Genetic algorithms for project management. Annals of Software Engineering, 11(1), 107-139.

De Jong, K. A., & Spears, W. M. (1990, October). An analysis of the interacting roles of population size and crossover in genetic algorithms. In International Conference on Parallel Problem Solving from Nature (pp. 38-47). Springer, Berlin, Heidelberg.

Eppinger, S. D., & Browning, T. R. (2012). Design structure matrix methods and applications. MIT press.

Fayek, A.R. et al. (2003). Measuring and Classifying Construction Field Rework: A Pilot Study. Manage. Sci., vol. 45, no. 4, pp. 455–465, 1999.

Garrett, D., Vannucci, J., Silva, R., Dasgupta, D., & Simien, J. (2005). Genetic algorithms for the sailor assignment problem. In Proceedings of the 7th annual conference on Genetic and evolutionary computation (pp. 1921-1928). ACM.

Harper, P. R., de Senna, V., Vieira, I. T., & Shahani, A. K. (2005). A genetic algorithm for the project assignment problem. Computers & Operations Research, 32(5), 1255-1265.

Herroelen, W., Demeulemeester, E., & De Reyck, B. (1999). A classification scheme for project scheduling. In Project scheduling (pp. 1-26). Springer US.

Järventausta, S. and Pulkkinen, A. (2001). Enhancing product modularisation with multiple views of decomposition and clustering, in Design for Configuration – a debate based on the 5th WDK Workshop on Product Structuring, Riitahuhta, A. and Pulkkinen, A (eds), 153-168, Springer.

Kerzner, H. (2003). Project Management. A systems approach to planning, scheduling and controlling. 8 th Edition. John Wiley and Sons Inc, Hoboken, New Jersey.

Krishnan, V., Eppinger, S. D., & Whitney, D. E. (1997). A model-based framework to overlap product development activities. Management science, 43(4), 437-451.

Li, H. (2011). A Rollout Algorithm for the Resource-Constrained Project Scheduling Problem with Stochastic Task Durations. University of Missouri, Project Final Report.

Maenhout, B., & Vanhoucke, M. (2016). An exact algorithm for an integrated project staffing problem with a homogeneous workforce. Journal of Scheduling, 19(2), 107-133.

Melnic, A. S., & Puiu, T. (2011). The management of human resources within projects: the structures of the project team, the responsibility assignment matrix. Economy Transdisciplinarity Cognition, 14(1), 476.

Quashem, R. (2015). Design structure matrix: Models, applications and data exchange format (Doctoral dissertation, University of Lethbridge (Canada)).

Roemer, T. A., Ahmadi, R., & Wang, R. H. (2000). Time-cost trade-offs in overlapped product development. Operations Research, 48(6), 858-865.

Sahu, A., & Tapadar, R. (2006). Solving the Assignment Problem using Genetic Algorithm and Simulated Annealing. In IMECS (pp. 762-765).

Sosa, Manuel E., Steven D. Eppinger, and Craig M. Rowles. (2003). Identifying Modular and Integrative Systems and Their Impact on Design Team Interactions. Journal of Mechanical Design 125 (2):240-252.

Terwiesch, C., & Loch, C. H. (1999). Measuring the effectiveness of overlapping development activities. Management science, 45(4), 455-465.

Weydt, I. K. (2008). The human side of project management: an investigation of critical chain concepts in the Argentinean Project Environment (Doctoral dissertation).

Whitfield, R. I., Smith, J. S., & Duffy, A. B. (2002). Identifying component modules. In Artificial Intelligence in Design’02 (pp. 571-592). Springer Netherlands.

Yang, Q., Yao, T., Lu, T., & Zhang, B. (2014). An overlapping-based design structure matrix for measuring interaction strength and clustering analysis in product development project. IEEE Transactions on Engineering Management, 61(1), 159-170.

Yassine, A. (2004). An introduction to modeling and analyzing complex product development processes using the design structure matrix (DSM) method. Urbana, 51(9), 1-17.

Younas, I. (2014). Using Genetic Algorithms for Large Scale Optimizationof Assignment, Planning and Rescheduling Problems (Doctoral dissertation, KTH Royal Institute of Technology).

Zakarian, A. (2008). A new nonbinary matrix clustering algorithm for development of system architectures. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(1), 135-141.
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