Abstract: This study investigates the optimization of hazardous materials (hazmat) transportation networks under depot disruption scenarios, motivated by the increasing occurrence of “low-probability, high-consequence” events such as natural disasters and facility failures. Traditional models often minimize total risk and cost but overlook two critical factors: potential depot disruptions and risk equity—the fair distribution of risk among regions and populations. These omissions limit model applicability in complex real-world environments where governmental, public, and industrial demands must be balanced. To address this gap, we propose a multi-objective optimization model that simultaneously minimizes (1) total system risk, (2) logistics cost (including leasing, storage, and transport), and (3) risk compensation costs that penalize deviations from average risk exposure. A novel risk assessment function integrates depot disruption probabilities into both route and node-level risk. Risk equity is captured through compensation coefficients that quantify disparities and encourage balanced risk allocation.To solve this complex problem, we develop a hybrid metaheuristic algorithm combining Ant Colony Optimization (ACO) and Genetic Algorithm (GA), which enhances solution quality and convergence speed under various disruption scenarios. A real-world case study of Shanghai’s road hazmat network—with 5 candidate depots and 20 customers—is used to validate the model. Parameter values are calibrated using government and industry data. Results show that considering depot disruptions significantly alters warehouse selection and routing strategies, leading to reduced system risk and improved network resilience. The hybrid algorithm outperforms standalone ACO and GA in all key objectives. Sensitivity analysis further reveals that: (1) higher compensation improves equity but may increase total risk; (2) expanding depot capacity reduces the number of active depots, raising both risk and inequality; and (3) ignoring depot disruptions may significantly increase systemic risk. This study enriches the theoretical framework of risk assessment and equity under uncertainty and offers a practical decision-support tool for designing safer, fairer, and more resilient hazmat logistics systems.

Key words: Hazardous chemical transportation network, Risk equity, Depot disruption, Multi-objective optimization, Ant colony -Genetic hybrid algorithm