Expressway networks represent evolving complex systems whose topological properties significantly impact regional development. This paper presents a decision support framework for addressing the expressway infrastructure sequencing problem using computational intelligence. We develop a novel framework that models expressways as L-space networks and evaluates how construction sequences create path-dependent evolutionary trajectories, introducing network science principles into infrastructure planning decisions. Our decision support framework quantifies project impacts on accessibility, connectivity, and reliability using nine topological metrics and a hybrid weighting mechanism that combines domain expertise with entropy-based uncertainty quantification. The system employs a hybrid TOPSIS algorithm that relies on geometric symmetry to simulate network evolution, capturing emergent properties in which each decision restructures possibilities for subsequent choices—a computational challenge that conventional planning approaches have not addressed. The system was validated with real-world Chongqing expressway planning data, demonstrating its ability to identify sequences that maximize synergistic network effects. Results reveal how topologically equivalent projects produce dramatically different system-wide outcomes depending on implementation order. Analysis shows that network science-informed sequencing substantially enhances system performance by exploiting structural synergies. This research advances decision support frameworks by bridging complex network theory with computational decision-making, creating a novel analytical tool that enables transportation authorities to implement evidence-based infrastructure sequencing strategies beyond the reach of conventional planning methods.
Yu et al. (Fri,) studied this question.