surrogate models

Consolidation in Crowdshipping with Scheduled Transfer Lines: A Surrogate-Based Network Design Framework

  • Ramazan Çevik
  • Ali Şardağ
  • Barış Yıldız
    • Categories Combinatorial Optimization, Optimization in Data Science, Transportation Tags , , ,

    Abstract: Crowdshipping has gained attention as an emerging delivery model thanks to advantages such as flexibility and an asset-light structure. Yet, it chronically suffers from a lackof mechanisms to create and exploit consolidation opportunities, limiting its efficiency and scalability. This work contributes to the literature in two ways: first, by introducing a novel consolidation concept … Read more

    Machine Learning Algorithms for Improving Black Box Optimization Solvers

  • Morteza Kimiaei
  • Vyacheslav Kungurtsev
    • Categories Global Optimization, Nonlinear Optimization, Optimization in Data Science Tags , , , , ,

    Black-box optimization (BBO) addresses problems where objectives are accessible only through costly queries without gradients or explicit structure. Classical derivative-free methods—line search, direct search, and model-based solvers such as Bayesian optimization—form the backbone of BBO, yet often struggle in high-dimensional, noisy, or mixed-integer settings. Recent advances use machine learning (ML) and reinforcement learning (RL) to … Read more

    Global Dynamic Optimization with Hammerstein-Wiener Models Embedded

  • Dominik Bongartz
  • Alexander Mitsos
  • Jaromił Najman
  • C. D. Kappatou
  • S. Sass
    • Categories Global Optimization Theory Tags , , , ,

    Hammerstein-Wiener models constitute a significant class of block-structured dynamic models, as they approximate process nonlinearities on the basis of input-output data without requiring identification of a full nonlinear process model. Optimization problems with Hammerstein-Wiener models embedded are nonconvex, and thus local optimization methods may obtain suboptimal solutions. In this work, we develop a deterministic global … Read more

    Locally weighted regression models for surrogate-assisted design optimization

  • Charles Audet
  • Michael Kokkolaras
  • Sébastien Le Digabel
  • Bastien Talgorn
    • Categories Mechanical Engineering, Nonlinear Optimization, Nonsmooth Optimization Tags , , , ,

    Locally weighted regression combines the advantages of polynomial regression and kernel smoothing. We present three ideas for appropriate and effective use of LOcally WEighted Scatterplot Smoothing (LOWESS) models for surrogate optimization. First, a method is proposed to reduce the computational cost of LOWESS models. Second, a local scaling coefficient is introduced to adapt LOWESS models … Read more