SavedAdd your journal

Journals / Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering / 2019 / Cilt: 20 - Sayı: 3

PROJECT SCHEDULING AND STAFF ALLOCATION PROBLEM WITH TIME-DEPENDENT LEARNING EFFECT: A MIXED INTEGER NON-LINEAR PROGRAMMING APPROACH

Oğuzhan Ahmet ARIK
Pages
204–215
Publication Date
2019-09-26
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Therefore, task sequence is essential to assign tasks to project staffs in order to minimize project completion time by considering task similarities in view of learning.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper proposes a mixed integer non-linear programming (MINLP) for project scheduling and staff allocation problem with time-dependent learning effect considering task similarities for learning.
📊
Key Findings & Data:Resources of a project, especially staffs, can learn from previous tasks if those tasks and the current task are similar.
💡
Conclusion & Impact:Therefore, task sequence is essential to assign tasks to project staffs in order to minimize project completion time by considering task similarities in view of learning.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Time-dependent learning effect · Project scheduling · Mixed integer non-linear programming

Abstract

This paper proposes a mixed integer non-linear programming (MINLP) for project scheduling and staff allocation problem with time-dependent learning effect considering task similarities for learning. Resources of a project, especially staffs, can learn from previous tasks if those tasks and the current task are similar. Considering similarities among tasks for each staff can produce a more realistic schedule for a project. The objective function of the proposed problem is to minimize project completion time. The learning effect in this paper is dependent on the time when the project staff starts to do that task. If a project staff does repetitive and/or similar tasks then that staff can get experience and do tasks faster than planned durations. Therefore, task sequence is essential to assign tasks to project staffs in order to minimize project completion time by considering task similarities in view of learning.

🔖

Cite This Article

ARIK, O. A. (2019). PROJECT SCHEDULING AND STAFF ALLOCATION PROBLEM WITH TIME-DEPENDENT LEARNING EFFECT: A MIXED INTEGER NON-LINEAR PROGRAMMING APPROACH. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, Cilt: 20 - Sayı: 3, 204–215. https://ajindex.com/articles/196828

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (17)

  1. Wright TP. Factors Affecting the Cost of Airplanes. J Aeronaut Sci 1936; 3: 122–128
  2. Biskup D. Single-machine scheduling with learning considerations. Eur J Oper Res 1999; 115: 173–178
  3. Kuo WH, Yang DL. Minimizing the total completion time in a single-machine scheduling problem with a time-dependent learning effect. Eur J Oper Res 2006; 174: 1184–1190
  4. Arık OA, Toksarı MD. Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and learning effects. Int J Prod Res 2018; 56: 2488–2505
  5. Arık OA, Toksarı MD. Fuzzy Parallel Machine Scheduling Problem Under Fuzzy Job Deterioration and Learning Effects With Fuzzy Processing Times in Advanced Fuzzy Logic Approaches in Engineering Science, M. Ram, Ed. IGI Global, 2019, pp. 49–67
  6. Arık OA, Toksari MD. Fuzzy chance constrained programming technique for single machine earliness/tardiness scheduling problem under effects of fuzzy learning and deterioration. Sak. Univ J Sci 2018; 22(2): 652–662
  7. Toksarı MD, Arık OA. Single machine scheduling problems under position-dependent fuzzy learning effect with fuzzy processing times. J Manuf Syst, 2017; 45:159-179
  8. Shtub A, LeBlanc LJ, Cai Z. Scheduling programs with repetitive projects: A comparison of a simulated annealing, a genetic and a pair-wise swap algorithm. Eur J Oper Res 1996; 88: 124–138
  9. Ash R, Smith-Daniels DE. The effects of learning, forgetting, and relearning on decision rule performance in multi project scheduling. Decis Sci 1999; 30: 47–79
  10. Arditi D, Tokdemir OB, Suh K. Effect of learning on line-of-balance scheduling. Int J Proj Manag 2001; 19: 265–277
  11. Lam KC, Lee D, Hu T. Understanding the effect of the learning-forgetting phenomenon to duration of projects construction. Int J Proj Manag 2001; 19: 411–420
  12. Arditi D, Tokdemir OB, Suh K. Challenges in line-of-balance scheduling. J Constr Eng Manag 2002; 128: 545–556
  13. Moselhi O, Hassanein A. Optimized scheduling of linear projects, J Constr Eng Manag, 2003; 129: 664–673
  14. Hassanein A. Moselhi O. Planning and scheduling highway construction. J Constr Eng Manag 2004; 130: 638–646
  15. Wu MC. Sun SH. A project scheduling and staff assignment model considering learning effect. Int J Adv Manuf Technol 2006; 28: 1190–1195
  16. Yan JH, Wang ZM. GA based algorithm for staff scheduling considering learning-forgetting effect, in: 2011 IEEE 18th Int Conf Ind Eng Eng Manag IE EM 2011, 2011: 122–126
  17. Ammar M.A, Abdel-Maged