The Simos-Roy-Figueira (SRF) deck-of-cards procedure is widely recognized as an intuitive method for eliciting criteria weights in outranking Multiple Criteria Decision Aiding (MCDA) methods. However, its numerous extensions, covering imprecise or fuzzy inputs, robustness analysis, hierarchical criteria structures, and diverse communication protocols, have developed independently. Although these variants share the same underlying deck-of-cards mechanism and are, in principle, mutually compatible, the literature offers little guidance on how to combine them coherently or design new Simos-Roy-Figueira (SRF) configurations for a given decision context. This paper fills that gap by introducing a modular SRF framework that decomposes existing and new methods into a set of interoperable building blocks, each with an explicit linear (or mixed-integer) programming formulation. The main novelty of the framework is threefold: (i) it unifies the main SRF variants as special cases of a common constraint system; (ii) it embeds generic models for consistency checking, inconsistency diagnosis and minimal restoration directly in that system; and (iii) it operationalizes these modules through a guided questionnaire that helps analysts and decision makers select appropriate configurations and derive the corresponding optimization models. The resulting modular SRF framework offers MCDA practitioners, researchers and actual Decision Makers (DMs) a transparent, user-configurable approach that preserves the rigor of outranking methods, while significantly enhancing practical flexibility and user experience. We demonstrate the framework’s versatility through two case studies, each providing custom SRF results, enriched with additional considerations, such as uncertainty and robustness analysis.

Over the past few decades, the strategies to perform energy systems analysis have evolved into multiple criteria-based frameworks. However, there still remains a lack of guidance on how to select the most suitable Multiple Criteria Decision Analysis (MCDA) method. These methods provide different decision recommendations for the Decision Makers, including ranking, sorting, choice, and clustering of the alternatives (e.g., technologies or scenarios) under evaluation. They deal with a variety of data typologies and preferences, and lead Decision Makers in shaping the energy systems of the future. Here, we evaluate the MCDA methods used in 56 case studies performing energy systems analysis at different scales. We find that close to 60% of these studies chose an MCDA method that was not the most adequate for the respective decision problem. In particular, this concerned the use of weighting methods (e.g., Analytical Hierarchy Process) in MCDA approaches not suited for this type of weights, sub-optimal selection of MCDA techniques for specific types of problem statements, and lack of handling rather evident interactions in preference models. Our analysis demonstrates that these deficiencies can be overcome by using a recently developed methodology and software that support Decision Makers and analysts in selecting the most suitable MCDA method for a given type of decision-making problem.