Development of an Innovative Multi-Objective Optimization Framework for Sustainable Roadway Asset Management

The United States faces a dual challenge: a rapidly aging transportation infrastructure and limited funding resources for maintenance and rehabilitation. This scenario demands the application of innovative Decision-Support Systems (DSS) to help transportation agencies make data-driven, cost-effective, and sustainable choices. The proposed project aims to develop and validate a multi-objective optimization framework for roadway asset management that integrates economic, environmental, and operational objectives using a Multi-Colony Ant Colony Optimization (MACO) approach. The proposed model will optimize three key and often conflicting objectives simultaneously: (1) minimization of maintenance costs; (2) minimization of maintenance duration; and (3) minimization of environmental impacts. The system will serve as a decision-support tool for Maintenance, Repair, and Rehabilitation (MR&R) planning, guiding practitioners in selecting optimal strategies under resource constraints. Beyond computational modeling, this research emphasizes technology transfer and workforce training—aligning with CSUTC’s mission to advance innovation, sustainability, and workforce readiness in transportation. The results will include a validated prototype model, field-tested on a case study (potentially within the CSULB roadway network), and an implementation and training framework to support broader deployment by local and state agencies.