Dynamic pricing services to minimise CO2 emissions of delivery vehicles

In recent years, companies delivering goods or services to customers have been under increasing legal and administrative pressure to reduce the amount of CO2 emissions from their delivery vehicles, while the need to maximise profit remains a prime objective. In this research, we aim to apply revenue management techniques, in particular incentive/dynamic pricing to the traditional vehicle routing and scheduling problem while the objective is to reduce CO2 emissions. With the importance of accurately estimating emissions recognised, emissions models are first reviewed in detail and a new emissions calculator is developed in Java which takes into account time-dependent travel speeds, road distance and vehicle specifications. Our main study is a problem where a company sends engineers with vehicles to customer sites to provide services. Customers request for the service at their preferred time windows and the company needs to allocate the service tasks to time windows and decide on how to schedule these tasks to their vehicles. Incentives are provided to encourage customers choosing low emissions time windows. To help the company in determining the schedules/routes and incentives, our approach solves the problem in two phases. The first phase solves time-dependent vehicle routing/scheduling models with the objective of minimising CO2 emissions and the second phase solves a dynamic pricing model to maximise profit. For the first phase problem, new solution algorithms together with existing ones are applied and compared. For the second phase problem, we consider three different demand modelling scenarios: linear demand model, discrete choice demand model and demand model free pricing strategy. For each of the scenarios, dynamic pricing techniques are implemented and compared with fixed pricing strategies through numerical experiments. Results show that dynamic pricing leads to a reduction in CO2 emissions and an improvement in profits

Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps

Historical maps provide useful spatio-temporal information on the Earth's surface before modern earth observation techniques came into being. To extract information from maps, neural networks, which gain wide popularity in recent years, have replaced hand-crafted map processing methods and tedious manual labor. However, aleatoric uncertainty, known as data-dependent uncertainty, inherent in the drawing/scanning/fading defects of the original map sheets and inadequate contexts when cropping maps into small tiles considering the memory limits of the training process, challenges the model to make correct predictions. As aleatoric uncertainty cannot be reduced even with more training data collected, we argue that complementary spatio-temporal contexts can be helpful. To achieve this, we propose a U-Net-based network that fuses spatio-temporal features with cross-attention transformers (U-SpaTem), aggregating information at a larger spatial range as well as through a temporal sequence of images. Our model achieves a better performance than other state-or-art models that use either temporal or spatial contexts. Compared with pure vision transformers, our model is more lightweight and effective. To the best of our knowledge, leveraging both spatial and temporal contexts have been rarely explored before in the segmentation task. Even though our application is on segmenting historical maps, we believe that the method can be transferred into other fields with similar problems like temporal sequences of satellite images. Our code is freely accessible at https://github.com/chenyizi086/wu.2023.sigspatial.git

Moira

Thesis (MM) – Indiana University, Music, 202

The Granularity Effects: Numerical Judgment from a Social Perspective.

In marketplace, information is communicated to consumers by marketers. Accordingly, judgments and decisions made in response to this information ought to be considered in their communicative context. Research in quantitative judgment typically fails to do so; it analyzes judgment and decision making in a social vacuum, essentially pretending that the quantitative information simply exists and comes from nowhere. In contrast, my research places quantitative judgment in its conversational context by investigating how people make inferences from the expression of a quantity. The theoretical basis of this research is built on Grice’s (1975) logic of conversation, which suggests that information recipients interpret a piece of communication based on the assumption that the speaker provides as much information as is relevant while remaining truthful. A series of three essays addresses this issue. In the first essay, I study how consumers draw inferences from a time estimate expressed at different levels of granularity. Consumers consider estimates expressed in finer granularity more precise and have more confidence in their accuracy. Hence, they perceive products as more likely to deliver on their promises when the promise is described in fine grained rather than coarse units. In the second essay, I find that precise numbers have a stronger influence on subsequent estimates than round numbers, in the way that people make small adjustment from the anchor when the anchor is a precise (vs. round) number. In the third essay, I argue that pragmatic inference is situated in the judgment task, so that the influence of numerical expression can go beyond quantitative judgment. In judgments of product value, precise statements of volume on a package give rise to the inference that the product is particularly valuable. Importantly, all these effects are eliminated when consumers doubt that the communicator complies with Gricean norms of cooperative conversational conduct. My dissertation concludes that there is more to “numeric cognition” than mere numbers – the numbers are communicated and we cannot fully understand their influence without taking communicative processes into account. It highlights the role of pragmatic inferences in consumer judgment and suggests important implications for the design of marketing communications.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99916/1/yizi_1.pd

Probabilistic forecast of nonlinear dynamical systems with uncertainty quantification

Data-driven modeling is useful for reconstructing nonlinear dynamical systems when the underlying process is unknown or too expensive to compute. Having reliable uncertainty assessment of the forecast enables tools to be deployed to predict new scenarios unobserved before. In this work, we first extend parallel partial Gaussian processes for predicting the vector-valued transition function that links the observations between the current and next time points, and quantify the uncertainty of predictions by posterior sampling. Second, we show the equivalence between the dynamic mode decomposition and the maximum likelihood estimator of the linear mapping matrix in the linear state space model. The connection provides a data generating model of dynamic mode decomposition and thus, uncertainty of predictions can be obtained. Furthermore, we draw close connections between different data-driven models for approximating nonlinear dynamics, through a unified view of data generating models. We study two numerical examples, where the inputs of the dynamics are assumed to be known in the first example and the inputs are unknown in the second example. The examples indicate that uncertainty of forecast can be properly quantified, whereas model or input misspecification can degrade the accuracy of uncertainty quantification

Motion-Invariant Variational Auto-Encoding of Brain Structural Connectomes

Mapping of human brain structural connectomes via diffusion MRI offers a unique opportunity to understand brain structural connectivity and relate it to various human traits, such as cognition. However, motion artifacts from head movement during image acquisition can impact the connectome reconstructions, rendering the subsequent inference results unreliable. We aim to develop a generative model to learn low-dimensional representations of structural connectomes that are invariant to motion artifacts, so that we can link brain networks and human traits more accurately, and generate motion-adjusted connectomes. We applied the proposed model to data from the Adolescent Brain Cognitive Development (ABCD) study and the Human Connectome Project (HCP) to investigate how our motion-invariant connectomes facilitate understanding of the brain network and its relationship with cognition. Empirical results demonstrate that the proposed motion-invariant variational auto-encoder (inv-VAE) outperforms its competitors on various aspects. In particular, motion-adjusted structural connectomes are more strongly associated with a wide array of cognition-related traits than other approaches without motion adjustment

Scheduling and pricing of services to minimise CO2 emissions of delivery vehicles

Previous research found that minimising emissions often conflicts with maximising profit in service delivery. In this study, we consider a service scheduling problem and propose a new approach to the problem which applies low-emission vehicle scheduling techniques with dynamic pricing to reduce CO2 emissions and maximise profit. Incentives are included in the service prices to influence the customer’s choice in order to reduce CO2 emissions. To help the company determining the incentives, our approach solves the problem in two phases. The first phase solves vehicle scheduling models with the objective of minimising CO2 emissions and the second phase solves a dynamic pricing model to maximise profit. This approach is tested through numerical experiments

Scan cell design for enhanced delay fault testability

Problems in testing scannable sequential circuits for delay faults are addressed. Modifications to improve circuit controllability and observability for the testing of delay faults are implemented efficiently in a scan cell design. A layout on a gate array is designed and evaluated for this scan cel