Bus Bridging for Rail Disruptions: A Distributionally Robust Fuzzy Optimization Approach

Dealing with uncertain rail disruptions effectively raises a significant challenge for computational intelligence research. This article studies the bus bridging problem under demand uncertainty, where passenger is represented as parametric interval-valued fuzzy variables and their associated uncertainty distribution sets. A distributionally robust optimization model proposed to minimize maximum travel time search optimal scheme vehicle allocation, route selection, frequency determination. To solve model, we discuss issues concerning credibilistic constraints, turning counterpart into computationally tractable equivalent formulations. The approach verified, resulting method validated report on parameters in real-world disrupted event of Shanghai Rail Line 1. Experimental results show that can provide better uncertainty-immunized solution.