Ahmad Baubaid

Two-stage stochastic service network design must fix how many vehicles run on each arc before demand is known, and the recourse cost of adding spot vehicles makes the problem hard to solve. A feedforward neural network replaces that recourse subproblem and is embedded in a mixed-integer program; because first-stage variables are general integers, training samples come from perturbing solutions of single-scenario problems. Across 18 instances, designs from the perturbation approach deviate 1.33% on average from their full-scenario evaluated cost, versus 10.81% for random sampling.

Two-stage stochastic service network design must fix how many vehicles run on each arc before demand is known, and the recourse cost of adding spot vehicles makes the problem hard to solve. A feedforward neural network replaces that recourse subproblem and is embedded in a mixed-integer program; because first-stage variables are general integers, training samples come from perturbing solutions of single-scenario problems. Across 18 instances, designs from the perturbation approach deviate 1.33% on average from their full-scenario evaluated cost, versus 10.81% for random sampling.