Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem.
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AYODELE, M., MCCALL, J. and REGNIER-COUDERT, O. 2017. Estimation of distribution algorithms for the multi-mode resource constrained project scheduling problem. In Proceedings of the IEEE congress on evolutionary computation (CEC 2017), 5-8 June 2017, San Sebastian, Spain. New York: IEEE [online], pages 1579-1586. Available from: https://doi.org/10.1109/CEC.2017.7969491
Multi-Mode Resource Constrained Project Problem (MRCPSP) is a multi-component problem which combines two interacting sub-problems; activity scheduling and mode assignment. Multi-component problems have been of research interest to the evolutionary computation community as they are more complex to solve. Estimation of Distribution Algorithms (EDAs) generate solutions by sampling a probabilistic model that captures key features of good solutions. Often they can significantly improve search efficiency and solution quality. Previous research has shown that the mode assignment sub-problem can be more effectively solved with an EDA. Also, a competitive Random Key based EDA (RK-EDA) for permutation problems has recently been proposed. In this paper, activity and mode solutions are respectively generated using the RK-EDA and an integer based EDA. This approach is competitive with leading approaches of solving the MRCPSP.