1,040 research outputs found
Recommended from our members
Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable Last-Mile Strategies
In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. While this has brought prosperity for the e-retailers, the ever-increasing consumer demand has brought more trucks to the residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. To cope with this, different logistics strategies such as the introduction of micro-hubs, alternative delivery points, and use of cargo bikes and zero emission vehicles for the last mile have been introduced and, in some cases, implemented as well. This project, hence, aims to develop an analytical framework to model urban last mile delivery. In particular, this study will build upon the previously developed econometric behavior models that capture e-commerce demand. Then, based on continuous approximation techniques, the authors will model the last-mile delivery operations. And finally, using the cost-based sustainability assessment model (developed in this study), the authors will estimate the economic and environmental impacts of residential deliveries under different city logistics strategies.View the NCST Project Webpag
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization
Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying
on hand-crafted domain knowledge. This paper broadens the current scope of
neural solvers for NPC problems by introducing a new graph-based diffusion
framework, namely DIFUSCO. Our framework casts NPC problems as discrete {0,
1}-vector optimization problems and leverages graph-based denoising diffusion
models to generate high-quality solutions. We investigate two types of
diffusion models with Gaussian and Bernoulli noise, respectively, and devise an
effective inference schedule to enhance the solution quality. We evaluate our
methods on two well-studied NPC combinatorial optimization problems: Traveling
Salesman Problem (TSP) and Maximal Independent Set (MIS). Experimental results
show that DIFUSCO strongly outperforms the previous state-of-the-art neural
solvers, improving the performance gap between ground-truth and neural solvers
from 1.76% to 0.46% on TSP-500, from 2.46% to 1.17% on TSP-1000, and from 3.19%
to 2.58% on TSP10000. For the MIS problem, DIFUSCO outperforms the previous
state-of-the-art neural solver on the challenging SATLIB benchmark. Our code is
available at "https://github.com/Edward-Sun/DIFUSCO"
Optimal Minimax Mobile Sensor Scheduling Over a Network
We investigate the problem of monitoring multiple targets using a single
mobile sensor, with the goal of minimizing the maximum estimation error among
all the targets over long time horizons. The sensor can move in a
network-constrained structure, where it has to plan which targets to visit and
for how long to dwell at each node. We prove that in an optimal observation
time allocation, the peak uncertainty is the same among all the targets. By
further restricting the agent policy to only visit each target once every
cycle, we develop a scheme to optimize the agent's behavior that is
significantly simpler computationally when compared to previous approaches for
similar problems
Learning-Based Approaches for Graph Problems: A Survey
Over the years, many graph problems specifically those in NP-complete are
studied by a wide range of researchers. Some famous examples include graph
colouring, travelling salesman problem and subgraph isomorphism. Most of these
problems are typically addressed by exact algorithms, approximate algorithms
and heuristics. There are however some drawback for each of these methods.
Recent studies have employed learning-based frameworks such as machine learning
techniques in solving these problems, given that they are useful in discovering
new patterns in structured data that can be represented using graphs. This
research direction has successfully attracted a considerable amount of
attention. In this survey, we provide a systematic review mainly on classic
graph problems in which learning-based approaches have been proposed in
addressing the problems. We discuss the overview of each framework, and provide
analyses based on the design and performance of the framework. Some potential
research questions are also suggested. Ultimately, this survey gives a clearer
insight and can be used as a stepping stone to the research community in
studying problems in this field.Comment: v1: 41 pages; v2: 40 page
- …