IADES Publications¶

"Sustainability Risk Management for Project-Oriented Organizations"¶

Sánchez, Y.A., Jane, L.G., Soler, J.A.P., Delgado, F.M. (2022). Sustainability Risk Management for Project-Oriented Organizations. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_9

Abstract

Sustainability is used as a reference in the management of organizations, so that, in addition to increasing economic benefits, social and environmental strategies are applied. To this end, approaches and models are used to define the dimensions for measuring sustainability. The increase of Cuban companies makes necessary the implementation of tools that contribute to the sustainability of an organization. This research aims to develop a procedure to contribute to sustainable risk management in project-oriented organizations. The proposed procedure consists of five stages: Prior preparation, Organizational analysis, Risk assessment, Treatment and Follow-up and continuous improvement. It incorporates fundamental characteristics of sustainability, such as multidimensional assessment, process approach and ethical character. The validation of the proposed strategy was carried out using the following methods: expert criteria using the Likert scale, taking into account that it is a useful validation method to check the reliability of a research, obtaining a percentage index higher than 90.77 in all the questions. The case study as a method that allows verifying the applicability of the proposal made possible its application in a real environment, which shows a high level of efficiency and effectiveness of the procedure.

"New Methods for Feasibility Analysis of Investment Projects in Uncertain Environments"¶

Peña Abreu, M., Rodríguez Rodríguez, C.R., Piñero Pérez, P.Y., García García, Y. (2022). New Methods for Feasibility Analysis of Investment Projects in Uncertain Environments. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_8

Abstract

Project feasibility analysis is a key process in organizations to predict the success of projects in the future. This process becomes complex because it involves multiple criteria, which are evaluated in uncertain environments. This paper presents a model to perform the feasibility analysis of investment projects in uncertain environments using soft computing techniques. The model includes technical, economic, social and commercial criteria and the methods for its calculation. Traditional economic criteria are extended by introducing triangular fuzzy numbers, which provides greater flexibility and certainty in predicting economic feasibility. The 2-tuple linguistic representation model is used in the qualitative evaluation of the criteria and the calculation of the final results. The model output is the feasibility analysis of the projects, integrating the results of all the evaluated criteria without loss of information, which provides greater interpretability for decision making. The case study and experiments conducted demonstrate that the model is applicable in real environments and that its ability to predict feasibility is greater than that of other proposals.

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

Mahdi, G.S.S., Piñero Pérez, P.Y., Madera Quintana, J., Al-subhi, S.H., García Vacacela, R. (2022). Constraints Learning Univariate Estimation of Distribution Algorithm on the Multi-mode Project Scheduling Problem. In: Piñero Pérez, P.Y., Bello Pérez, R.E., Kacprzyk, J. (eds) Artificial Intelligence in Project Management and Making Decisions. UCIENCIA 2021. Studies in Computational Intelligence, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-97269-1_7

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.

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