In Mustafee, N., Bae, K. G., Lazarova-Molnar, S., Rabe, M., Szabo, C., Haas, P., & Son, Y., editors, Proceedings of the 2019 Winter Simulation Conference, pages 3516–3527, Piscataway, NJ, 2019. IEEE. Paper doi abstract bibtex
We introduce the Retrospective Multi-Gradient Search with Piecewise Linear Interpolation and Neighborhood Enumeration (R-MGSPLINE) algorithm for finding a local efficient point when solving a multi-objective simulation optimization problem on an integer lattice. In this nonlinear optimization problem, all objectives can only be observed with stochastic error, the decision variables are integer-valued, and a local solution is called the efficient set. R-MGSPLINE uses a retrospective approximation (RA) framework to repeatedly call the MGSPLINE sample-path solver at a sequence of increasing sample sizes, using the solution from the previous RA iteration as a warm start for the current RA iteration. The MGSPLINE algorithm performs a line search along a common descent direction constructed from pseudo-gradients of each objective, followed by a neighborhood enumeration for certification. Numerical experiments show that R-MGSPLINE converges to a local weakly efficient point.