ABSTRACT: We introduce a framework for optimizing Petri nets in the context of drug placement within Automated Drug Dispensing Systems. This framework incorporates both consumption data and co-use relationship data to improve retrieval efficiency. It effectively models adjacency constraints, token flow dynamics, and operational rules through the use of an incidence matrix and structural invariants, thereby ensuring stable allocation. Simulation experiments conducted with Snoopy evaluated 200 probabilistic prescription orders across various co-use thresholds. The findings indicate that a threshold of = 0.6) minimizes retrieval time while simultaneously decreasing data variability. In comparison to random placement, the optimized allocation demonstrates a significant enhancement in efficiency, resulting in an estimated annual savings of approximately 9 hours in retrieval time per machine. This research addresses a critical gap in the optimization of ADDS by concurrently modeling co-use and consumption data, representing a novel approach that has not been previously investigated in the literature. The results underscore the operational advantages of employing structured, data-driven drug placement strategies in high-volume healthcare environments.
Academic Journal of Manufacturing Engineering (Fri,) studied this question.