Optimization of Resource Allocation and Leveling Using Genetic Algorithms
Publication: Journal of Construction Engineering and Management
Volume 125, Issue 3
Abstract
Resource allocation and leveling are among the top challenges in project management. Due to the complexity of projects, resource allocation and leveling have been dealt with as two distinct subproblems solved mainly using heuristic procedures that cannot guarantee optimum solutions. In this paper, improvements are proposed to resource allocation and leveling heuristics, and the Genetic Algorithms (GAs) technique is used to search for near-optimum solution, considering both aspects simultaneously. In the improved heuristics, random priorities are introduced into selected tasks and their impact on the schedule is monitored. The GA procedure then searches for an optimum set of tasks' priorities that produces shorter project duration and better-leveled resource profiles. One major advantage of the procedure is its simple applicability within commercial project management software systems to improve their performance. With a widely used system as an example, a macro program is written to automate the GA procedure. A case study is presented and several experiments conducted to demonstrate the multiobjective benefit of the procedure and outline future extensions.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Allam, S. I. G. ( 1998). “Multi-project scheduling: A new categorization for heuristic scheduling rules in construction scheduling problems.” J. Constr. Mgmt. and Economics, E&FN Spon, 6(2), 93–115.
2.
Davis, E. W., and Patterson, J. H. ( 1975). “A comparison of heuristic and optimum solutions in resource-constrained project scheduling.” Mgmt. Sci., 21(8), 944–955.
3.
Easa, S. (1989). “Resource leveling in construction by optimization.”J. Constr. Engrg. and Mgmt., ASCE, 115(2), 302–316.
4.
Feng, C., Liu, L., and Burns, S. (1997). “Using genetic algorithms to solve construction time-cost trade-off problems.”J. Comp. Civ. Engrg., ASCE, 11(3), 184–189.
5.
Gavish, B., and Pirkul, H. ( 1991). “Algorithms for multi-resource generalized assignment problem.” Mgmt. Sci., 37(6), 695–713.
6.
Goldberg, D. E. ( 1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading, Mass.
7.
Harris, R. ( 1978). Resource and arrow networking techniques for construction. Wiley, New York.
8.
Hegazy, T., and El-Zamzamy, H. ( 1998). “Project management software that meet the challenge.” Cost Engrg. J., 4(5), 25–33.
9.
Hegazy, T., and Moselhi, O. (1994). “Analogy-based solution to markup estimation problem.”J. Comp. Civ. Engrg., ASCE, 8(1), 72–87.
10.
Johnson, R. ( 1992). “Resource constrained scheduling capabilities of commercial project management software.” Proj. Mgmt. J., 22(4), 39–43.
11.
Karshenas, S., and Haber, D. ( 1990). “Economic optimization of construction project scheduling.” J. Constr. Mgmt. and Economics, E&FN Spon, 8(2), 135–146.
12.
Li, H., and Loing, P. (1997). “Using improved genetic algorithms to facilitate time-cost optimization.”J. Constr. Engrg. and Mgmt., ASCE, 123(3), 233–237.
13.
Microsoft Project. ( 1995). Reference manual, ver. 4.1 for Windows 95. Microsoft Corporation, Redmond, Wash.
14.
Moselhi, A., and Lorterapong, P. ( 1993). “Least impact algorithm for resource allocation.” Can. J. Civ. Engrg., CSCE, 20(2), 180–188.
15.
Premavera. ( 1995). Reference manual, ver. 1.0 for Windows. Primavera, Bala Cynwyd, Pa.
16.
Shah, K., Farid, F., and Baugh, J. ( 1993). “Optimal resource leveling using integer-linear programming.” Proc., 5th Int. Conf. in Comp. in Civ. & Bldg. Engrg., ASCE, Reston, Va., 1, 501–508.
17.
Talbot, F., and Patterson, J. ( 1979). “Optimal methods for scheduling projects under resource constrains.” Proj. Mgmt. Quarterly, Dec., 26–33.
18.
Wiest, D. ( 1964). “Some properties of schedules for large projects with limited resource.” Operations Res., 12, 395–416.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 125 • Issue 3 • June 1999
Pages: 167 - 175
History
Published online: Jun 1, 1999
Published in print: Jun 1999
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.