Petri net (PN) is a mathematical modeling tool for representing and analyzing discrete-event dynamic systems. Applying PNs in managerial decision-making contributes to studying the systems dynamics, performance characteristics, and resource utilization. It is previously known that Boolean Petri nets (BPNs) generate all \(2^k\) binary vectors in their reachability analysis that are used to study safe systems. In this article, the concept of reachability tree for a defined class of BPNs is used to merge the marking nodes by proving its homomorphism with a graph isomorphic to the multidimensional hypercube network topology. Using this approach, we apply the model in frequent pattern mining to find the association rules in the Market Basket Analysis (MBA) Problem. MBA is extensively studied using Apriori, FP Growth and ECLAT algorithms but it scans the databases multiple times for generating association rules. The reachability-based approach followed here uses the idea from these algorithms and is implemented in the defined BPN model. This suggests the use of reachability in solving the problem does not require multiple data scans. This approach with reachability is novel and helps in understanding customer behavior and transactional patterns.
