參考文獻 |
中文文獻
1. 工業技術研究院(2022),工業4.0大全:從淺到深一篇搞懂它!取得日期:01/09/2023,取自:https://reurl.cc/NGW14q。
2. 中華專案管理學會(2009),專案管理 Q & A,取得日期:01/09/2023,取自:https://reurl.cc/6N70E5。
3. 王鴻章(2019),資源及空間限制下之橋梁基礎施工排程最佳化,國立中興大學土木工程學系碩士學位論文。
4. 江霈柔(2021),零工式生產排程之派工法則的比較與建議,國立高雄科技大學運籌管理系運籌管理系碩士論文。
5. 吳民友(2016),多目標隨機排程最佳化:以台灣汽車零組件製造商為例。國立成功大學製造資訊與系統研究所碩士論文。
6. 李玟瑩(2014),考慮不同趕工成本結構下之資源受限專案最佳趕工策略研發,國立中正大學企業管理研究所碩士論文。
7. 林宏文(2021),工業4.0台灣兆元機械產業的升級方向,CIO Taiwan。取得日期:01/09/2023,取自:https://reurl.cc/qZEzV3。
8. 林榮基(2006),從資源可獲得性和里程碑來進行受限資源專案排程問題之相關研究,國立中正大學企業管理學系碩士班碩士論文。
9. 張仁鈞(2014),可調式動態資源限制之專案排程研究,天主教輔仁大學企業管理學系管理學碩士班碩士論文。
10. 張秉宸、黃誠甫、吳吉政、洪一薰、林義貴(2018),作業研究歷久彌新的回顧,管理與系統;25卷3期 (2018 / 07 / 01),P259 – 289。
11. 張政名(2018),Application of Simplified Swarm Optimization for Solving the Resource-Constrained Project Scheduling Problems,國立清華大學工業工程與工程管理學系碩士論文。
12. 許展維(2012),以啟發式演算法求解資源受限條件下資源分派與排程問題,國立中正大學企業管理學系碩士班碩士論文。
13. 曾煜皓(2022),工作壓力、職業倦怠、離職傾向與知覺組織支持之相關研究-以中部地區機械製造業為例,修平科技大學人力資源管理與發展系碩士學位論文。
14. 楊璧慈(2015),大型專案排程與資源配置問題研究-以造船業為例,國立高雄應用科技大學工業工程與管理系碩士班碩士論文。
15. 廖家宜(2019a),台灣以中小企業為骨幹推動工業4.0應先補強基礎體質,DigiTimes。取得日期:01/09/2023,取自:https://reurl.cc/109jbY。
16. 廖家宜(2019b),人力抄表紀錄仍常見台製造業自動化基礎普遍不足,DigiTimes。取得日期:01/09/2023,取自:https://www.digitimes.com.tw/iot /package_show.asp?cat=158&id=0000551155_1002P53J4TTE9965NTQMX&packageid=13184&startshow=Y。
17. 熊治民(2020),台灣精密機械產業發展挑戰與展望,機械工業。取得日期:01/09/2023,取自:https://reurl.cc/qZEzvE。
18. 鄭子瑋(2018),應用改良簡化群體演算法求解彈性零工式生產排程問題,國立清華大學工業工程與工程管理學系碩士論文。
19. 謝育倫(2016),生產線人員排班最佳化模式之個案研究-以 IC最終測試廠為例。國立交通大學管理學院(資訊管理學程)碩士班碩士論文。
英文文獻
1. Ahn, T., & Erenguc, S. S. (1998). The resource constrained project scheduling problem with multiple crashable modes: a heuristic procedure. European Journal of Operational Research, 107(2), 250-259.
2. Alias, Z., Zawawi, E. M. A., Yusof, K., & Aris, N. M. (2014). Determining critical success factors of project management practice: A conceptual framework. Procedia-Social and Behavioral Sciences, 153, 61-69.
3. Azab, A., & Naderi, B. (2014). Greedy heuristics for distributed job shop problems. Procedia CIRP, 20, 7-12.
4. Bowers, J. A. (2000). Multiple schedules and measures of resource constrained float. Journal of the Operational Research Society, 51, 855-862.
5. Bowers, J. A. (1995). Criticality in resource-constrained networks.Journal of the operational research society, 46(1), 80-91.
6. Chaudhry, I. A., & Mahmood, S. (2011). Identical parallel-Machine scheduling and worker assignment problem using genetic algorithms to minimize makespan. Electrical Engineering and Applied Computing, 529-541.
7. Drezet, L. E., & Billaut, J. C. (2008). A project scheduling problem with labour constraints and time-dependent activities requirements. International Journal of Production Economics, 112(1), 217-225.
8. Gupta, S. K., & Kyparisis, J. (1987). Single machine scheduling research. Omega, 15(3), 207-227.
9. Hwang, B. G., & Ng, W. J. (2013). Project management knowledge and skills for green construction: Overcoming challenges. International journal of project management, 31(2), 272-284.
10. Irani, Z. (2010). Investment evaluation within project management: an information systems perspective. Journal of the Operational Research Society, 61(6), 917-928.
11. Jia, Z. H., Zhang, Y. L., Leung, J. Y. T., & Li, K. (2017). Bi-criteria ant colony optimization algorithm for minimizing makespan and energy consumption on parallel batch machines. Applied Soft Computing, 55, 226-237.
12. Ke, H., & Liu, B. (2010). Fuzzy project scheduling problem and its hybrid intelligent algorithm. Applied Mathematical Modelling, 34(2), 301-308.
13. Kim, K. (2020). Generalized Resource-Constrained Critical Path Method to Improve Sustainability in Construction Project Scheduling. Sustainability, 12(21), 8918.
14. Kim, K., & de la Garza, J. M. (2003). Phantom float. Journal of construction engineering and management, 129(5), 507-517.
15. Kim, K., & De La Garza, J. M. (2005). Evaluation of the resource-constrained method algorithms. Journal of construction engineering and management, 131(5), 522-532.
16. Kolisch, R., & Padman, R. (2001). An integrated survey of deterministic project scheduling. Omega, 29(3), 249-272.
17. Kolisch, R., & Hartmann, S. (1999). Heuristic algorithms for the resource-constrained project scheduling problem: Classification and computational analysis (pp. 147-178).
18. Kwak, Y. H. (2005). A brief history of project management. The story of managing projects, 9.
19. Lee, J. H., & Kim, H. J. (2021). A heuristic algorithm for identical parallel machine scheduling: splitting jobs, sequence-dependent setup times, and limited setup operators. Flexible Services and Manufacturing Journal, 33, 992-1026.
20. Liaw, C. F. (2000). A hybrid genetic algorithm for the open shop scheduling problem. European Journal of Operational Research, 124(1), 28-42.
21. Lu, M., & Li, H. (2003). Resource-activity critical-path method for construction planning. Journal of construction engineering and management, 129(4), 412-420.
22. Masmoudi, M., & Haït, A. (2013). Project scheduling under uncertainty using fuzzy modelling and solving techniques. Engineering Applications of Artificial Intelligence, 26(1), 135-149.
23. Mingozzi, A., Maniezzo, V., Ricciardelli, S., & Bianco, L. (1998). An exact algorithm for project scheduling with resource constraints based on a new mathematical formulation. Management science, 44(5), 714-729.
24. Pan, Q. K., Tasgetiren, M. F., Suganthan, P. N., & Chua, T. J. (2011). A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information sciences, 181(12), 2455-2468.
25. Roca, J., Bossuyt, F., & Libert, G. (2007). PSPSolver: An Open Source Library for the RCPSP. PlanSIG 2007, 124.
26. Romero, M. A. F., García, E. A. R., Ponsich, A., & Gutiérrez, R. A. M. (2018). A heuristic algorithm based on tabu search for the solution of flexible job shop scheduling problems with lot streaming. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 285-292).
27. Sifaleras, A., Karakalidis, A., & Nikolaidis, Y. (2022). Shift scheduling in multi-item production lines: a case study of a mineral water bottling company. International Journal of Systems Science: Operations & Logistics, 9(1), 75-86.
28. Šišejkovic, D. (2016). Evolution of scheduling heuristics for the resource constrained scheduling problem (Doctoral dissertation, Master’s thesis, Fakultet elektrotehnike i racunarstva, Sveucilište u Zagrebu).
29. Snyder, J. R. (1987). Modern Project Management: how Did We Get Here--where Do We Go?. Project Management Institute.
30. Stützle, T. (1998, September). An ant approach to the flow shop problem. In Proceedings of the 6th European Congress on Intelligent Techniques & Soft Computing (EUFIT’98) (Vol. 3, pp. 1560-1564).
31. Tavana, M., Abtahi, A. R., & Khalili-Damghani, K. (2014). A new multi-objective multi-mode model for solving preemptive time–cost–quality trade-off project scheduling problems. Expert systems with applications, 41(4), 1830-1846.
32. Węglarz, J., Józefowska, J., Mika, M., & Waligóra, G. (2011). Project scheduling with finite or infinite number of activity processing modes–A survey. European Journal of operational research, 208(3), 177-205.
33. Wiest, J. D. (1964). Some properties of schedules for large projects with limited resources. Operations research, 12(3), 395-418.
34. Woodworth, B. M., & Shanahan, S. (1988). Identifying the critical sequence in a resource constrained project. International journal of project management, 6(2), 89-96.
35. Yannibelli, V., & Amandi, A. (2013). Hybridizing a multi-objective simulated annealing algorithm with a multi-objective evolutionary algorithm to solve a multi-objective project scheduling problem. Expert Systems with Applications, 40(7), 2421-2434.
36. Yin, Y., Cheng, T. E., Wu, C. C., & Cheng, S. R. (2014). Single-machine due window assignment and scheduling with a common flow allowance and controllable job processing time. Journal of the Operational Research Society, 65(1), 1-13.
37. Zhao, C. L., & Tang, H. Y. (2010). Single machine scheduling with general job-dependent aging effect and maintenance activities to minimize makespan. Applied Mathematical Modelling, 34(3), 837-841. |