arXiv subjects

Xiaoping Jiang

Publications and source records attributed to Xiaoping Jiang.

3 recordsLinked to original sources

arXiv2021

Analytics and Machine Learning in Vehicle Routing Research

The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial optimisation problems for which numerous models and algorithms have been proposed. To tackle the complexities, uncertainties and dynamics involved in real-world VRP applications, Machine Learning (ML) methods have been used in combination with analytical approaches to enhance problem formulations and algorithmic performance across different problem solving scenarios. However, the relevant papers are scattered in several traditional research fields with very different, sometimes confusing, terminologies. This paper presents a first, comprehensive review of hybrid methods that combine analytical techniques with ML tools in addressing VRP problems. Specifically, we review the emerging research streams on ML-assisted VRP modelling and ML-assisted VRP optimisation. We conclude that ML can be beneficial in enhancing VRP modelling, and improving the performance of algorithms for both online and offline VRP optimisations. Finally, challenges and future opportunities of VRP research are discussed.

cs.LG↗

arXiv2020

Fuzzy C-means-based scenario bundling for stochastic service network design

Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solving the resulting SMIP. The computational performance of the PHA can be greatly enhanced by decomposing according to scenario bundles instead of individual scenarios. At the heart of bundle-based decomposition is the method for grouping the scenarios into bundles. In this paper, we present a fuzzy c-means-based scenario bundling method to address this problem. Rather than full membership of a bundle, which is typically the case in existing scenario bundling strategies such as k-means, a scenario has partial membership in each of the bundles and can be assigned to more than one bundle in our method.

cs.AI↗

arXiv2018

Quantification of Flow Noise Produced by an Oscillating Foil

Getting inspired from swimming natural species, a lot of research is being carried out in the field of unmanned underwater vehicles. During the last two decades, more emphasis on the associated hydrodynamic mechanisms, structural dynamics, control techniques and, its motion and path planning has been prominently witnessed in the literature. Considering the importance of the involved acoustic mechanisms, we focus on the quantification of flow noise produced by an oscillating hydrofoil here employed as a kinematic model for fish or its relevant appendages. In our current study, we perform numerical simulations for flow over an oscillating hydrofoil for a wide range of flow and kinematic parameters. Using the Ffowcs-Williams and Hawkings (FW-H) method, we quantify the flow noise produced by a fish during its swimming for a range of kinematic and flow parameters including Reynolds number, reduced frequency, and Strouhal number. We find that the distributions of the sound pressure levels at the oscillating frequency and its first even harmonic due to the pressure fluctuations in the fluid domain are dipole-like patterns. The magnitudes of these sound pressure levels depend on the Reynolds number and Strouhal number, whereas the direction of their dipole-axes appears to be affected by the reduced frequency only. Moreover, We also correlate this emission of sound radiations with the hydrodynamic force coefficients.

physics.flu-dyn↗