TY - JFULL AU - Javier Roca and Etienne Pugnaghi and Gaƫtan Libert PY - 2008/11/ TI - Solving an Extended Resource Leveling Problem with Multiobjective Evolutionary Algorithms T2 - International Journal of Computer and Information Engineering SP - 3439 EP - 3451 VL - 2 SN - 1307-6892 UR - https://publications.waset.org/pdf/14791 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 22, 2008 N2 - We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results. ER -