International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
line decor
  
line decor

 Volume 6, Issue 5 (May 2019), Pages: 70-75

----------------------------------------------

 Original Research Paper

 Title: Project scheduling with variable renewable resource limits

 Author(s): Patience I. Adamu, Hilary I. Okagbue *, Pelumi E. Oguntunde

 Affiliation(s):

 Department of Mathematics, Covenant University, Ota, Nigeria

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-3779-9763

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2019.05.012

 Abstract:

Renewable resources are the resources that are made available per period in a resource-constrained project scheduling problem. For each period, the quantity of each renewable resource (manpower, money, equipment) made available is often assumed constant and this may not reflect true life situations. Hence, we present a more realistic model whose renewable resource limit varies from per period where possible. Experimentally, results show that this model ensures that projects are completed in the least possible time (no matter the financial situation), hence, no abandonment of projects. Also, it helps to check corruption, where it is prevalent because, in this model, the exact amount needed in each period can be made available without giving room for excesses which can be mismanaged. 

 © 2019 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Project scheduling, Priority rules, Network analysis, Resource-constraints, Optimization

 Article History: Received 4 July 2018, Received in revised form 3 March 2019, Accepted 20 March 2019

 Acknowledgement:

The authors appreciate Covenant University for providing an enabling environment. 

 Compliance with ethical standards

 Conflict of interest:  The authors declare that they have no conflict of interest.

 Citation:

 Adamu PI, Okagbue HI, and Oguntunde PE (2019). Project scheduling with variable renewable resource limits. International Journal of Advanced and Applied Sciences, 6(5): 70-75

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3

 Tables

 No Table

----------------------------------------------

 References (15)

  1. Adamu PI, Agarana MC, and Okagbue HI (2018). Machine learning heuristic for solving multi-mode resource-constrained project scheduling problems. In the International MultiConference of Engineers and Computer Scientists, Hong Kong, China, 1: 1-6.   [Google Scholar]
  2. Blazewicz J, Lenstra JK, and Kan AR (1983). Scheduling subject to resource constraints: Classification and complexity. Discrete Applied Mathematics, 5(1): 11-24. https://doi.org/10.1016/0166-218X(83)90012-4   [Google Scholar]
  3. Burgelman J and Vanhoucke M (2018). Maximising the weighted number of activity execution modes in project planning. European Journal of Operational Research, 270(3): 999-1013. https://doi.org/10.1016/j.ejor.2018.04.035   [Google Scholar]
  4. Crawford B, Soto R, Astorga G, Castro C, Paredes F, Misra S, and Rubio JM (2018). Solving the software project scheduling problem using intelligent water drops. Technical Gazette, 25(2): 350-357.   [Google Scholar]
  5. Crawford B, Soto R, Johnson F, Misra S, Paredes F, and Olguín E (2015). Software project scheduling using the hyper-cube ant colony optimization algorithm. Technical Gazette, 22(5): 1171-1178.   [Google Scholar]
  6. Elmaghraby SE (1977). Activity networks: Project planning and control by network models. John Wiley and Sons, Hoboken, New Jersey, USA.   [Google Scholar]
  7. Kolisch R (1996). Serial and parallel resource-constrained project scheduling methods revisited: Theory and computation. European Journal of Operational Research, 90(2): 320-333. https://doi.org/10.1016/0377-2217(95)00357-6   [Google Scholar]
  8. Kolisch R (2013). Project scheduling under resource constraints: Efficient heuristics for several problem classes. Springer Science and Business Media, Berlin, Germany. https://doi.org/10.1016/0377-2217(95)00357-6   [Google Scholar]
  9. Słowinski R (1980). Two approaches to problems of resource allocation among project activities—A comparative study. Journal of the Operational Research Society, 31(8): 711-723. https://doi.org/10.1057/jors.1980.134   [Google Scholar]
  10. Sprecher A, Hartmann S, and Drexl A (1997). An exact algorithm for project scheduling with multiple modes. Operations-Research-Spektrum, 19(3): 195-203. https://doi.org/10.1007/BF01545587   [Google Scholar]
  11. Talbot FB (1982). Resource-constrained project scheduling with time-resource tradeoffs: The nonpreemptive case. Management Science, 28(10): 1197-1210. https://doi.org/10.1287/mnsc.28.10.1197   [Google Scholar]
  12. Talbot FB and Patterson JH (1978). An efficient integer programming algorithm with network cuts for solving resource-constrained scheduling problems. Management Science, 24(11): 1163-1174. https://doi.org/10.1287/mnsc.24.11.1163   [Google Scholar]
  13. Tao S, Wu C, Sheng Z, and Wang X (2018). Stochastic project scheduling with hierarchical alternatives. Applied Mathematical Modelling, 58: 181-202. https://doi.org/10.1016/j.apm.2017.09.015   [Google Scholar]
  14. Vanhoucke M (2012). Optimizing regular scheduling objectives: Schedule generation schemes. Pm Knowledge Center. Available online at: http://www.pmknowledgecenter.com   [Google Scholar]
  15. Weglarz J (1980). On certain models of resource allocation problems. Kybernetes, 9(1): 61-66. https://doi.org/10.1108/eb005544   [Google Scholar]