Fuzzy Logic and Petri Net‐Based Modeling for Analyzing Vulnerabilities in Smart Contract Compositions for Vaccine Supply Chain Management in Global Healthcare
M. Sreenu, Nitin Gupta, Chandrashekar Jatoth · Computational Intelligence · 2026
ABSTRACT Composite smart contracts coordinate vaccine supply chains, where multiple contracts run in parallel and inputs are uncertain. Existing verification tools typically capture concurrency while assuming crisp inputs, or handle uncertainty while omitting explicit interactions among contracts. This separation reduces detection power and weakens formal guarantees under realistic noise and interleavings. A fusion of Fuzzy Logic (FL) and Colored Petri Nets (CPN) (FL–CPN) models both aspects in one framework. The FL layer converts uncertain measurements into crisp scores; the CPN layer encodes places, transitions, colors, and guards; integration maps scores to token colors and guard enabling so that fuzzy outcomes drive execution. This enables formal verification and vulnerability analysis of composite smart contracts under uncertainty and concurrency. Contributions are: (1) a formal FL–CPN integration with algorithms and execution semantics; (2) a verification workflow covering safety, liveness, and reachability with attack path tracing; (3) case studies on counterfeit insertion and temperature log tampering with rule driven token flows and mitigations; (4) an empirical comparison against CPN, FSM, and NuSMV using 10‐fold cross‐validation and standard metrics. Across folds, the approach achieves precision 0.9828–0.9937, specificity 0.964–0.988, sensitivity 0.973–1.000, accuracy 0.9758–0.9933, and F 0.9733–0.9908. The method extends to other supply chains and multi‐party blockchain workflows that combine uncertain evidence with concurrent contract interactions.