Abstract
Resource-Constrained Project Scheduling Problem (RCPSP) is a NP-hard combinatorial problem that consists in scheduling different activities in such a way the resource, precedence, and temporal constraints are satisfied. The main problem when dealing with NP-hard problems is the exponential growth of the computational resources needed to solve the problems. This work is an extension of a previous one, where a new CSP graph-based representation to solve Constraint Satisfaction Problems (CSP) by using Ant Colony Optimization (ACO) were proposed. This paper studies the behaviour of the CSP graph-based representation when it is applied to a real-world complex problem, in this case the RCPSP. The dataset used in this work has been extracted from Project Scheduling Problem Library (PSPLIB). Experimental results show that the proposed approach provides excellent results, closer to the optimum values published in the PSPLIB repository. Also, it has been analysed how the number of jobs and the number of different execution modes affect the performance of the algorithm.
Antonio Gonzalez-Pardo
Associate Professor
Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.