Shuangshuang Nie^1,, Jihong Gao¹

The project scheduling and resource allocation problems have been studied using different optimization methods. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently resource constraint problem. For example, resource-constrained project scheduling problem (RCPSP) was proposed to optimize scheduling under resource constraints. This research proposed a new model which integrates the resource leveling problem and resource-constrained time-cost tradeoff problem. The evolutionary multi-objective optimization technique, strength Pareto evolutionary approach II (SPEA II) was applied to calculate the Pareto front of time and cost. The resource leveling measured by the metric, resource release/re-hiring, was converted to resource cost. The analysis of the time complexity of the model showed that the runtime of the algorithm was polynomial times of the number of activities. The results of case testing showed that the model was reasonably accurate in comparison with a proposed baseline model.

project scheduling, resource-constrained project scheduling problem, resource leveling, genetic algorithm, optimization, strength pareto evolutionary approach II