A diversity-based genetic algorithm for scenario generation

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Beatriz Brito Oliveira (Author) FEUP; Maria Antónia Carravilla (Author) FEUP; José Fernando Oliveira (Author) FEUP

Journal

Title: European Journal of Operational Research

Vol. 299

Pages: 1128-1141

ISSN: 0377-2217

Publisher: Elsevier

Indexing

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Other information

Authenticus ID: P-00V-H7N

DOI: 10.1016/j.ejor.2021.09.047

Abstract (EN): Tackling uncertainty is becoming increasingly relevant for decision-support across fields due to its critical impact on real-world problems. Uncertainty is often modelled using scenarios, which are combinations of possible outcomes of the uncertain parameters in a problem. Alongside expected value methods, decisions under uncertainty may also be tackled using methods that do not rely on probability distributions and model different decision-maker risk profiles. Scenarios are at the core of these approaches. Therefore, we propose a scenario generation methodology that seizes the structure and concepts of genetic algorithms. This methodology aims to obtain a diverse set of scenarios, evolving a scenario population with a diversity goal. Diversity is here expressed as the difference in the impact that scenarios have on the value of potential solutions to the problem. Moreover, this method does not require a priori knowledge of probability distributions or statistical moments of uncertain parameters, as it is based on their range. We adapt the available code for Biased-Random Key Genetic Algorithms to apply the methodology to a packing problem under demand uncertainty as a proof of concept, also extending its use to a multiobjective setting. We make available these code adaptations to allow the straightforward application of this scenario generation method to other problems. With this, the decision-maker obtains scenarios with a distinct impact on potential solutions, enabling the use of different criteria based on their profile and preferences.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14