Bayesian Multi-Topology Express Transportation Network Design under Posterior Predictive Demand, Sorting-Efficiency and Delivery-Time Uncertainty

2026-05-06

AI Summary

This study addresses the challenge of robustly designing express delivery networks under demand fluctuations, uncertain delivery times, and varying hub efficiencies. It proposes the first design paradigm that integrates Bayesian posterior predictive modeling with multi-topology selection. By leveraging historical data to characterize multiple sources of uncertainty, the approach jointly optimizes cost, timeliness risk, and network reliability through Bayesian posterior simulation, sample average approximation, and topology-level optimization. Theoretical guarantees are provided for the existence of a Bayesian optimal solution and the stability of topology selection. Experimental results demonstrate that the proposed method achieves substantial reductions in tail-risk delivery failures and significantly enhances hub reliability, with only a marginal increase in operational cost.

Abstract

Express transportation network design is uncertain because origin--destination demand, travel time, operating cost, hub congestion, and realized sorting productivity vary over time. Existing multi-topology express network models usually optimize cost and maximum arrival time under fixed input data, which may produce designs that are efficient nominally but fragile under demand surges, route disruptions, and hub productivity losses. This paper develops a Bayesian posterior-predictive framework for multi-topology express transportation network design. The model learns demand, travel-time, cost, and hub-reliability uncertainty from historical or benchmark-calibrated data and propagates them through posterior predictive scenarios. For fully connected, hub-and-spoke, restricted-allocation, and direct-link hybrid topologies, candidate designs are evaluated using posterior expected cost, conditional value-at-risk of maximum arrival time, service reliability, hub hold-time reliability, and emission-aware penalties. A Bayesian multi-structure design methodology is proposed using posterior simulation, sample-average approximation, topology-wise optimization, and Bayes-risk selection. Theoretical results establish existence of a Bayes-optimal design, convergence of posterior scenario risks, and stability of topology selection. Simulation and CAB benchmark experiments show that the Bayesian design can trade modest additional cost for substantial reductions in tail delivery risk and improved hub reliability.

Problem

• express transportation network design
• demand uncertainty
• delivery-time uncertainty
• sorting-efficiency uncertainty
• multi-topology

Innovation

• Bayesian posterior predictive
• multi-topology network design
• uncertainty quantification
• conditional value-at-risk (CVaR)
• hub reliability