Fuzzy Multi-Criteria Decision-Making in Military Systems

Fuzzy multi-criteria decision-making (MCDM) integrates fuzzy set theory with structured evaluation frameworks to support complex military decisions under uncertainty. By accommodating imprecise data and subjective expert judgements, fuzzy MCDM methods enable planners to prioritise assets, tactics and training programmes when faced with multiple, often conflicting criteria. Common steps include defining criteria hierarchies, eliciting expert preferences via membership functions, deriving criterion weights through techniques such as the Analytic Hierarchy Process (AHP), and ranking options with outranking or distance-based methods like TOPSIS or MABAC. These approaches have been applied across domains such as defence system deployment, resilience training, weapon-system effectiveness assessment and capability-based planning. Hybrid models combine fuzzy MCDM with network-based optimisation, evidential reasoning and evolutionary algorithms to address large-scale weapon system-of-systems planning and to balance cost, risk and performance. The global significance of these methods has grown alongside advances in simulation and artificial intelligence, offering military organisations robust decision support tools for dynamic operational environments.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Fuzzy Multi-Criteria Decision-Making in Military Systems publication trend

The graph below shows the total number of articles in fuzzy multi-criteria decision-making in military systems across all publications each year (not limited to Nature Index journals).

Technical terms

Fuzzy set theory: Mathematical framework handling partial membership of elements in sets, enabling representation of imprecise or subjective expert assessments.

Analytic Hierarchy Process (AHP): Structured method that decomposes decision problems into hierarchies and derives criterion weights through pairwise comparisons.

Technique for Order Performance by Similarity to Ideal Solution (TOPSIS): Distance-based ranking method that orders alternatives by closeness to an ideal solution and remoteness from a nadir solution.

Multi-Attributive Border Approximation area Comparison (MABAC): Outranking technique that measures alternatives’ distances from a defined border approximation area between ideal and anti-ideal points.

Non-dominated Sorting Genetic Algorithm II (NSGA-II): Evolutionary optimisation algorithm that produces a set of Pareto-optimal trade-off solutions in multiobjective problems.

Evidential Reasoning (ER) algorithm: Method for aggregating evidence from multiple sources with associated degrees of belief under uncertainty.

References

1. MULTI-CRITERIA DECISION-MAKING IN A DEFENSIVE OPERATION OF THE GUIDED ANTI-TANK MISSILE BATTERY: AN EXAMPLE OF THE HYBRID MODEL FUZZY AHP - MABAC. Decision Making Applications in Management and Engineering (2018).
2. Determining the Main Resilience Competencies by Applying Fuzzy Logic in Military Organization. Mathematics (2023).
3. Navigating Uncertainty in Weapon System-of-Systems Planning: A Hybrid Multiobjective Network-Based Optimization and Fuzzy Set Approach. International Journal of Computational Intelligence Systems (2023).