Regresar

Constraints Learning Univariate Estimation of Distribution Algorithm on the Multi-mode Project Scheduling Problem

Abstract:

Project Scheduling Problems (PSP) constitute a family of problems that includes different variants, which range from simple task planning without taking into account the resources they consume, to more sophisticated variants that consider several modes of processing of projects tasks, generalization of precedence relationships, multiple projects simultaneously and projects with variable resources. In this sense, various algorithms, both exact and heuristic, have been used to find optimal or quasi-optimal project schedules. This research aims to propose a new Constraints Learning Univariate Estimation of Distribution Algorithm (CL_UMDA) as an extension of the Univariate Marginal Distribution Algorithm (UMDA). The new algorithm incorporates the constraints handling inside the probabilistic model, for the solution of the PSP problem in its multimode variant (MMRCPSP). For this purpose, a group of experiments was developed on five databases of the PSPLib library, comparing the proposed algorithm with others reported in the literature. The experimental results show the superiority of the CL_UMDA performance over other algorithms used in the experimentation.