Linking Requirements to Solution Methods via Taxonomies: An EVRP Case Study

Many scientific and engineering domains, including machine learning, software engineering, operations research, and logistics optimization, are characterized by a fragmented landscape of problem variants, methodological approaches, and application-specific requirements. This diversity makes it difficult to systematically understand, compare, and select appropriate solution approaches, particularly when real-world industry needs must be translated into formal problem definitions and scientific methods. The Electric Vehicle Routing Problem (EVRP) provides a representative example: the literature spans numerous problem variants and a wide range of exact, heuristic, learning-based, and hybrid methods, yet there is no clear structure for linking practical requirements to relevant solution approaches through scientific evidence. We propose a dual-taxonomy framework that structures problem features and solution features as complementary conceptual spaces. Using this structure, industry requirements and scientific publications are annotated with shared taxonomy elements. Publications provide traceable links between problem features and solution methods reported in the mapped literature, while requirements provide an industry-facing entry point into the problem space. In a real-world EVRP case study, we demonstrate how the framework can support method exploration, reveal mismatches between industrial needs and the mapped research corpus, and identify gaps in the requirement set, taxonomy, and analyzed literature corpus. This provides a transparent and extensible pathway from practical problem descriptions to relevant scientific evidence and candidate solution approaches.