multicriterion

Multicriterion refers to the consideration of multiple criteria or objectives in decision making, design, and optimization. It contrasts with single-criterion approaches that focus on one measure of performance. The concept underlies a family of methods that seek to evaluate, compare, or optimize alternatives when several, often conflicting, criteria must be addressed.

Two main strands are typically distinguished. Multicriteria decision analysis (MCDA) provides a structured framework for ranking or choosing among alternatives based on explicit criteria and stakeholder preferences. Common techniques include simple additive weighting, Analytic Hierarchy Process (AHP), and non-compensatory methods such as ELECTRE and PROMETHEE, as well as distance-based or ranking methods like TOPSIS. MCDA involves selecting criteria, normalizing scales, assigning weights or preference structures, aggregating information, and performing sensitivity analysis to assess robustness of the results.

Multiobjective optimization, the mathematical counterpart, seeks solutions that optimize several objectives simultaneously. Because improving one objective may worsen another, the focus is often on finding Pareto-efficient solutions, where no objective can be improved without a trade-off in at least one other. The set of such solutions forms the Pareto frontier and is explored using algorithms from operations research and computational optimization.

Applications of multicriterion approaches span engineering, environmental planning, economics, finance, product design, healthcare, and public policy. Typical challenges include eliciting accurate preferences, dealing with incomplete or uncertain data, ensuring criteria independence and comparable scales, and interpreting results for decision makers. Robustness and transparency are central considerations in practical use.