Abstract
Bad scheduling and resource management can cause delays or cost overruns. Optimization in solving resource leveling is necessary to avoid those problems. Several objective criteria are used to solve resource leveling. Each of them has the same objective, which is to reduce the fluctuation of resource demand of the project. This study compares the performance of particle swarm optimization (PSO) and symbiotic organisms search (SOS) in solving resource leveling problems using separate objective functions in order to find which one produces a better solution. The results show that SOS produced a better solution than PSO, and one objective function is better in solving resource leveling than the others.
Highlights
One of the earliest resource leveling methods was developed by Burgess and Killebrew [4], in which the sum of the squares of the resource usage was minimized in order to have a smooth rectangleshaped resource histogram
This research compares the performance of symbiotic organisms search (SOS) and particle swarm optimization (PSO) in solving the resource leveling problem with nine objective criteria for resource leveling used in previous literatures
Since SOS is proven to be better than PSO in providing an optimal solution for the resource leveling problem, the comparison of the objective function will use the results of each objective function given by the SOS algorithm
Summary
One of the earliest resource leveling methods was developed by Burgess and Killebrew [4], in which the sum of the squares of the resource usage was minimized in order to have a smooth rectangleshaped resource histogram. Heuristics methods were used [8,9], but they still do not produce optimal results [10] The shortcomings of both mathematical and heuristic methods encouraged many researchers to study metaheuristic approaches in order to find more reliable optimization alternatives in solving resource leveling problems. Many studies have used some type of metaheuristic method as an alternative to resource leveling, such as genetic algorithm (GA) [2,6], ant colony organization (ACO) [11], particle swarm optimization (PSO) [12], and differential evolution (DE) [10].
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