| Title |
Resource Leveling using Genetic Algorithm |
| Authors |
Gwak, Han-Seong ; Bea, Sang-Hee ; Lee, Dong-Eun |
| DOI |
https://doi.org/10.5659/JAIK_SC.2018.34.2.67 |
| Keywords |
Resource leveling ; float ; genetic algorithm |
| Abstract |
Resource leveling minimizes resource fluctuations by deferring the earliest start times (ESTs) of non-critical activities within their corresponding total float. The intentional float-consumption for resource leveling purpose reduces the schedule delay contingency. This paper presents a method called Genetic Algorithm based Resource Leveling (GARL) that minimizes resource fluctuations and float-consumption impact over project duration. It identifies activities that are less sensitive to float-consumption and performs resource leveling using those activities. The study is of value to project scheduler because GARL identifies the set of activities to be deferred and the number of shift day(s) of each and every activities in the set within its total float expeditiously. It contributes to establish a baseline schedule which implements an optimal resource leveling plan. A case study is presented to verify the validity and usability of the method. It was confirmed that GARL satisfies the project duration constraint by considering resource fluctuations and float-consumption over project duration. |