In the bilateral matching problem under dynamic environments, the mechanism for handling time constraints and multi-objective optimization is one of the important factors affecting matching efficiency. The transport order assignment in online freight platforms serves as a typical instance of such problems. Existing methods exhibit significant limitations in rigid modeling of time constraints and in the trade-off mechanisms for multi-objective conflicts, making it difficult to accurately characterize the behavioral patterns of decision agents near constraint boundaries. To address these issues, this study proposes a time-constraint-aware transport order assignment framework called TB-Match. The framework consists of four collaborative modules: elastic constraint quantification, preference representation learning, dynamic objective trade-off optimization, and policy generation. The core contributions are as follows: (1) a constraint elasticity representation mechanism based on conditional diffusion probabilistic models, which converts deterministic time boundaries into continuous probabilistic distributions through progressive noise diffusion and reverse denoising processes, thus accurately modeling the acceptance probability of decision agents in boundary regions; (2) a hierarchical decision framework integrating dynamic objective trade-off and proximal policy optimization, where the high-level network adaptively adjusts objective weights according to feedback signals, and the low-level network maximizes long-term cumulative rewards under trust region constraints. Experimental results on two large-scale real-world logistics datasets demonstrate that TB-Match achieves a 17.66% relative improvement in matching rate compared with state-of-the-art methods. It also exhibits significant advantages in metrics such as satisfaction, verifying the effectiveness and applicability of the proposed method under complex constraint environments.