EURO 2024 Copenhagen
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2711. Robust De Novo Programming Under Different Uncertainty Sets and its Application to Renewable Energy Sector

Invited abstract in session MC-44: MCDM for project portfolio problems, stream Multiple Criteria Decision Analysis.

Monday, 12:30-14:00
Room: 20 (building: 324)

Authors (first author is the speaker)

1. Noureddine Kouaissah
Africa Business School, Mohammed VI Polytechnic University

Abstract

This paper proposes robust type models of de novo programming (R-DNP) using cardinality-constrained robustness via interval-based and norm-based uncertainty sets. The R-DNP has not been researched, and we aim to fill this literature gap. In particular, we develop the robust counterpart of the weighted DNP (W-DNP), Chebyshev DNP (C-DNP), and extended DNP (E-DNP) models to consider different uncertainty sets and incorporate the decision makers’ preferences. Methodologically, the proposed approach extends the conventional DNP model to enable it to solve uncertain coefficients for each decision variable on the left-hand side of each objective function and on the total budget, overcoming a limitation of the current multi-criteria solution procedure of the DNP approach. The proposed methods provide decision makers more flexibility to express their level of conservatism and preferences by setting aspiration levels. The proposed method’s usefulness over the standard DNP is demonstrated by providing an illustrative example. Moreover, we validate the proposed formulations for solving real-world problems through a hypothetical application: optimizing the renewable portfolio for electricity generation in Morocco. The results confirm the proposed methodologies and show that they can assist decision makers in determining the optimal system design for sustainable electricity generation under uncertain conditions.

Keywords

Status: accepted

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