Building multi-objective multimodal AI system by bi-symmetrical weighted distance project management

Abstract

This study proposes a Bi-symmetrical Weighted Distance (BWD) optimization framework for multimodal AI system development under uncertainty. By integrating fuzzy multi-objective linear programming with possibilistic programming, the approach simultaneously minimizes development costs, deployment time, and acceleration costs. The BWD method effectively handles imprecise parameters through distance-based defuzzification, enhancing decision transparency in intelligent hyperautomation contexts. An industrial case study validates the methodology, demonstrating practical capability to navigate trade-offs among time, cost, and resources where traditional methods struggle with uncertain parameter relationships.