Analysis of Strategies to Combat Cargo Theft and Robbery in Peripheral Communities of São Paulo, Brazil, Using a Paraconsistent Expert System

Abstract

Background: Cargo theft represents a persistent challenge to last-mile logistics in the peripheral regions of São Paulo, Brazil, compromising transportation security and increasing operational costs. These high-crime areas disrupt supply chain stability and hinder e-commerce growth. Traditional security methods often fail to address the complexity and uncertainty present in these environments, necessitating adaptive approaches. Methods: This study applies an Expert System based on Paraconsistent Annotated Evidential Logic Eτ to assess the effectiveness of security interventions. Logic Eτ is particularly suited for analyzing uncertain, incomplete, and contradictory data in complex logistics settings. A mixed-methods approach was employed, integrating evaluations from nine experts representing different hierarchical levels within a logistics company. Six key security measures, including GPS tracking, armed escorts, optimized delivery windows, and the hiring of local drivers, were analyzed using favorable degrees and unfavorable degrees for each parameter. Results: The results demonstrated that five measures were effective, contributing to a 58% reduction in security costs in Arujá and 75% in Cajamar, two major distribution hubs. Conclusions: This study highlights the potential of combining Expert Systems and Eτ Logic to enhance cargo transport security, offering a scalable decision support framework for companies operating in high-risk urban regions.