Speaker
Description
Transport logistics facilities such as less-than-truckload terminals require fast and robust tactical decisions under uncertainty, for example regarding task scheduling, resource allocation, and terminal configuration. Since detailed simulation experiments are computationally expensive, surrogate-assisted optimization methods provide an important basis for decision support.
This contribution presents and compares strategies for integrating global sensitivity analysis into Bayesian optimization for simulation-based decision support in transport logistics. Gaussian process surrogate models are combined with sensitivity measures to guide sequential optimization, including variable screening, candidate generation, soft search space reduction, and sensitivity-informed acquisition strategies.
The approaches are evaluated in a multi-objective logistics setting involving throughput, waiting times, resource utilization, and process efficiency. The comparison focuses on optimization performance, computational efficiency, and interpretability, demonstrating the potential of sensitivity-guided Bayesian optimization for more targeted tactical decision-making.
| Classification | Both methodology and application |
|---|---|
| Keywords | Bayesian Optimization, Global Sensitivity Analysis, Transport Logistics Simulation |