Develops a CVaR-based model to optimize hazardous materials transport under uncertainty, indicating practical applications for transportation planning.
Key Points
The research aims to optimize the transport routes for hazardous materials while managing risks under uncertain conditions.
Developed a Conditional Value-at-Risk (CVaR)-based optimization model.
Incorporated risks from transportation, population exposure, and time constraints.
Proposed an improved chaotic simulated annealing-ant colony optimization (CSAACO) algorithm.
Conducted numerical experiments to evaluate performance against standard ACO.
CSAACO demonstrated better solution quality and stability compared to standard ACO.
The model effectively addresses tail risks in dynamic environments.
Route selection was significantly influenced by risk aversion and departure time.