A methodology for multicriteria stochastic anticipatory optimization

• Grant number: 12/16504-5
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: October 01, 2012
• End date: August 31, 2014
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Principal Investigator: Fernando José von Zuben
• Grantee: Carlos Renato Belo Azevedo
• Host Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

This research project aims to design new sequential decision-making systems, operating in uncertain environments under multiple conflicting optimization criteria. It is assumed that the dynamics of the system under control (in discrete time and over a finite horizon) is linear and that the exogenous uncertainty can be estimated by parametric probabilistic models. In this context, four challenges are covered, namely: (1) the learning of probabilistic models capable of measuring the influence of the decisions implemented on the future operating costs; (2) the determination of stochastic policies capable of modeling the decision maker; (3) the determination of the risks of violating the problem constraints; and (4) the incorporation of partial preferences in the decision making process. It is emphasized that research activity on the treatment of multiple conflicting criteria and the incorporation of chance-constraints is scarce, considering the literature of anticipatory meta-heuristics and approximate dynamic programming. As its main contribution, this project proposes a new methodology as well as tools to allow for the effective synthesis of anticipatory multicriteria decision-making systems. The methodology will be investigated over a broad class of problems, ranging from vendor managed inventory-routing problems; optimization of financial and product portfolios, and the control of public transport systems operating in real time.

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

AZEVEDO, CARLOS R. B.; VON ZUBEN, FERNANDO J.. Learning to Anticipate Flexible Choices in Multiple Criteria Decision-Making Under Uncertainty. IEEE TRANSACTIONS ON CYBERNETICS, v. 46, n. 3, p. 778-791, MAR 2016. (12/16504-5)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

AZEVEDO, Carlos Renato Belo. Antecipação na tomada de decisão com múltiplos critérios sob incerteza. 2014. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Elétrica e de Computação Campinas, SP.