Flexible Emergency Vehicle Network Design considering Stochastic Demands and Inverse-Direction Lanes

Algorithm 1

Simulation-based genetic algorithm.

Input: The EV-NDP model, network parameters, sample size , population size ,

generation size , crossover rate , mutation rate , construction budget , and

etc.

Output: the optimal objective value (ETTT) and optimal solution .

Code variables and initialize the population;

for  every generation    to    do

for  every population    to    do

Decode the population and update link capacities;

for  every demand sample    to    do

Generate random travel demands for two classes of users;

Solve the bi-class user equilibrium traffic assignment with demand realization

by Frank-Wolfe Algorithm and consequently obtain the link flow patterns;

Compute the design objective function;

Compute the fitness function;

Generate better offspring by genetic operations, including reproduction, crossover and

mutation.

End the algorithm and obtain the solution.