Robustness of genetic algorithm solutions in resource leveling

Abstract

Algorithms for solving the resource leveling problem (RLP) in construction projects are proven to increase efficiency, create predictability, and balance demand across adjacent time periods or the project's duration while observing time and resource constraints. Leveling resources reduces the amount of change between one time period and the next in the project’s resource usage. Conventional optimization methods of the RLP can become difficult as the problem size grows, because the solution space grows exponentially as decision variables are added. Genetic algorithms are very capable when applied to large-scale instances of the RLP, and here the author applies a genetic algorithm testing multiple objective functions in literature with different performance measures. Results show that given a large problem, genetic algorithms capably produce a range of options for stakeholders and decision-makers and highlight changes in resource while preserving the strength of the solution.