Investigating Bayesian optimization for rail network optimization

Optimizing the operation of rail networks using simulations is an on-going task where heuristic methods such as Genetic Algorithms have been applied. However, these simulations are often expensive to compute and consequently, because the optimization methods require many (typically >104) repeat simulations, the computational cost of optimization is dominated by them. This paper examines Bayesian Optimization and benchmarks it against the Genetic Algorithm method. By applying both methods to test-tasks seeking to maximize passenger satisfaction by optimum resource allocation, it is experimentally determined that a Bayesian Optimization implementation finds ‘good’ solutions in an order of magnitude fewer simulations than a Genetic Algorithm. Similar improvement for real-world problems will allow the predictive power of detailed simulation models to be used for a wider range of network optimization tasks. To the best of the authors’ knowledge, this paper documents the first application of Bayesian Optimization within the field of rail network optimization

Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps

Whilst energy-structure-function (ESF) maps are a powerful new tool for in silico materials design, the cost of acquiring an ESF map for many properties is too high for routine integration into high-throughput virtual screening workflows. Here, we propose the next evolution of the ESF map. This uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost. We use this approach to obtain a two orders of magnitude speedup on an ESF study that focused on the discovery of molecular crystals for methane capture, saving over 500,000 CPUh from the original protocol. By accelerating the acquisition of insight from ESF maps, we pave the way for the use of these maps in automated ultra-high throughput screening pipelines greatly reducing the opportunity risk associated with the choice of system to calculate

No-Regret Constrained Bayesian Optimization of Noisy and Expensive Hybrid Models using Differentiable Quantile Function Approximations

This paper investigates the problem of efficient constrained global optimization of hybrid models that are a composition of a known white-box function and an expensive multi-output black-box function subject to noisy observations, which often arises in real-world science and engineering applications. We propose a novel method, Constrained Upper Quantile Bound (CUQB), to solve such problems that directly exploits the composite structure of the objective and constraint functions that we show leads substantially improved sampling efficiency. CUQB is a conceptually simple, deterministic approach that avoid constraint approximations used by previous methods. Although the CUQB acquisition function is not available in closed form, we propose a novel differentiable sample average approximation that enables it to be efficiently maximized. We further derive bounds on the cumulative regret and constraint violation under a non-parametric Bayesian representation of the black-box function. Since these bounds depend sublinearly on the number of iterations under some regularity assumptions, we establis bounds on the convergence rate to the optimal solution of the original constrained problem. In contrast to most existing methods, CUQB further incorporates a simple infeasibility detection scheme, which we prove triggers in a finite number of iterations when the original problem is infeasible (with high probability given the Bayesian model). Numerical experiments on several test problems, including environmental model calibration and real-time optimization of a reactor system, show that CUQB significantly outperforms traditional Bayesian optimization in both constrained and unconstrained cases. Furthermore, compared to other state-of-the-art methods that exploit composite structure, CUQB achieves competitive empirical performance while also providing substantially improved theoretical guarantees

A methodology for passenger-centred rail network optimisation

Optimising the allocation of limited resources, be they existing assets or investment, is an ongoing challenge for rail network managers. Recently, methodologies have been developed for optimising the timetable from the passenger perspective. However, there is a gap for a decision support tool which optimises rail networks for maximum passenger satisfaction, captures the experience of individual passengers and can be adapted to different networks and challenges. Towards building such a tool, this thesis develops a novel methodology referred to as the Sheffield University Passenger Rail Experience Maximiser (SUPREME) framework. First, a network assessment metric is developed which captures the multi-stage nature of individual passenger journeys as well as the effect of crowding upon passenger satisfaction. Second, an agent-based simulation is developed to capture individual passenger journeys in enough detail for the network assessment metric to be calculated. Third, for the optimisation algorithm within SUPREME, the Bayesian Optimisation method is selected following an experimental investigation which indicates that it is well suited for ‘expensive-to-compute’ objective functions, such as the one found in SUPREME. Finally, in case studies that include optimising the value engineering strategy of the proposed UK High Speed Two network when saving £5 billion initial investment costs, the SUPREME framework is found to improve network performance by the order of 10%. This thesis shows that the SUPREME framework can find ‘good’ resource allocations for a ‘reasonable’ computational cost, and is sufficiently adaptable for application to many rail network challenges. This indicates that a decision support tool developed on the SUPREME framework could be widely applied by network managers to improve passenger experience and increase ticket revenue. Novel contributions made by this thesis are: the SUPREME methodology, an international comparison between the Journey Time Metric and Disutility Metric, and the application of the Bayesian Optimisation method for maximising the performance of a rail network