Data-Driven Stochastic Optimization for Transportation Road Network Design Under Uncertainty

Abstract

A data-driven stochastic program for bi-level network design with hazardous material (hazmat) transportation is proposed in this chapter. In order to regulate the risk associated with hazmat transportation and minimize total travel cost on interested area under stochasticity, a multi-objective stochastic optimization model is presented to determine generalized travel cost for hazmat carriers. Since the bi-level program is generally non-convex, a data-driven bundle method is presented to stabilize solutions of the proposed model and reduce relative gaps between iterations. Numerical comparisons are made with existing risk-averse models. The results indicate that the proposed data-driven stochastic model becomes more resilient than others in minimizing total travel cost and mitigating risk exposure. Moreover, the trade-offs among maximum risk exposure, generalized travel costs, and maximum equitable risk spreading over links are empirically investigated in this chapter.