Deep Reinforcement Learning (DRL)-based frameworks, featuring Transformer-style policy networks, have demonstrated their efficacy across various Vehicle Routing Problem (VRP) variants. However, the application of these methods to the Multi-Trip Time-Dependent Vehicle Routing Problem (MTTDVRP) with maximum working hours constraints—a pivotal element of urban logistics—remains largely unexplored. This article introduces a DRL-based method called the Simultaneous Encoder and Dual Decoder Attention Model (SED2AM), tailored for the MTTDVRP with maximum working hours constraints. The proposed method introduces a temporal locality inductive bias to the encoding module of the policy networks, enabling it to effectively account for the time dependency in travel distance/time. The decoding module of SED2AM includes a vehicle selection decoder that selects a vehicle from the fleet, effectively associating trips with vehicles for functional multi-trip routing. Additionally, this decoding module is equipped with a trip construction decoder leveraged for constructing trips for the vehicles. This policy model is equipped with two classes of state representations, fleet state, and routing state, providing the information needed for effective route construction in the presence of maximum working hours constraints. Experimental results using real-world datasets from two major Canadian cities not only show that SED2AM outperforms the current state-of-the-art DRL-based and metaheuristic-based baselines but also demonstrate its generalizability to solve larger scale problems.

Dispatching large-scale fleets has been one of the fundamental aspects of managing heterogeneous truckload logistics. This operation involves optimization, visualization, and reporting of the resource plans meticulously crafted by expert planners and dispatchers on a daily basis. However, the limitations of human dispatchers, including errors in communication, routing, compliance, load planning, maintenance oversight, and neglect of driver preferences, can lead to lower customer satisfaction. We present FleetWiz, a Large Language Model-based (LLM) platform that enables logistics industry dispatchers to receive optimal recommendations based on real-time spatial data. FleetWiz seamlessly connects dispatchers, drivers, and resources, centralizing information within a unified resource-request network. This leads to enhanced transit times, reduced delays, and adaptive responses to dynamic conditions. It can execute tasks in different domains including filtering, optimizing, and answering questions based on the network of resources and requests. Specifically, a local Llama3 model is equipped with access to a geo-database for filtering, five different optimization methods for generating plans, and knowledge about the entire network and operations inside the company. Lastly, the tool's reliability, a generic interface for applying LLM agents alongside spatio-temporal optimization models, is demonstrated. The optimization models handle complex dispatching tasks requiring sequential reasoning, allowing the LLM to provide well-informed feedback based on the results.

The heterogeneous fleet and demand vehicle routing problem with time-window constraints (HFDVRPTW) is a crucial optimization problem of significant importance in real-world logistics operations. In this paper, we propose a deep reinforcement learning (DRL)-based method, termed spatial Edge-Feature EnhanCed mulTIgraph fusion encoder With spectral-based embedding and hieRarchical decOder with learnable TEmpoRal positional embedding (EFECTIW-ROTER, pronounced "Effective Router"), to tackle this complex and practical optimization problem. EFECTIW-ROTER utilizes two sparse graphs to represent node connectivity, where nodes correspond to customers and the depot. This sparsity results from the time-window constraints and customers' demand relative to the list of acceptable vehicle attributes specified for service within a heterogeneous fleet, determined by the reachability of the nodes based on these two factors. Leveraging two graph Transformer models, EFECTIW-ROTER's encoding module captures the interactions between the nodes based on these factors. One model encodes customers' heterogeneous demand with spatial edge features based on travel time between the nodes, while the second employs temporal positional embeddings to capture temporal relationships based on time-window ordering. A fusion model is introduced to integrate node interactions based on these graphs. Additionally, a spectral-attention-based pooling ensures effective state representation for the DRL-based method. EFECTIW-ROTER features a hierarchical attention decoder operating in two stages: heterogeneous vehicle selection and node selection. Enhanced with positional embeddings, the decoder is empowered to make effective routing decisions based on time-window constraints' ordering. Experimental results using real-world traffic data from two major Canadian cities confirm EFECTIW-ROTER's better performance over current state-of-the-art DRL-based and heuristic methods. EFECTIW-ROTER reduces travel times while also achieving faster computational times when compared to conventional heuristics. Additional experiments demonstrate its generalizability across larger instances.

Differential privacy (DP) has emerged as a rigorous notion to quantify data privacy. Subsequently, Renyi differential privacy (RDP) becomes an alternative to the ordinary DP notion in both theoretical and empirical studies, for its convenient compositional rules and flexibility. However, most mechanisms with DP (RDP) guarantees are essentially based on randomizing a fixed, finite-dimensional vector output. In this work, following Hall et al. (2013) we further extend RDP to functional outputs, where the output space can be infinite-dimensional, and develop all necessary tools, *e.g.*, (subsampled) Gaussian mechanism, composition, and post-processing rules, to facilitate its practical adoption. As an illustration, we apply functional RDP (f-RDP) to functions in the reproducing kernel Hilbert space (RKHS) to develop a differentially private generative model (DPGM), where training can be interpreted as iteratively releasing loss functions (in an RKHS) with DP (RDP) guarantees. Empirically, the new training paradigm achieves a significant improvement in privacy-utility trade-off compared to existing alternatives, especially when $\epsilon=0.2$. Our code is available at https://github.com/dihjiang/DP-kernel.

Efficient goods movement is a vital aspect of logistics and urban planning, impacting the flow of goods and the quality of life for residents. To aid in this, we present an interactive map-based system for visualizing and analyzing goods’ movements using GPS traces. The system takes raw GPS signal data and road network data as input, then performs preprocessing and spatial analysis on the data using Flask framework and Python scripts. The system offers a user-friendly interface to explore the patterns of goods movement dynamically. It displays the temporal and spatial movement trips in the city, providing an intuitive way to analyze and optimize goods movement. Our system’s ability to explore and visualize goods movement patterns makes it an essential addition to the existing literature on urban transportation analysis and a valuable tool for logistics companies and urban planners.

Truck speed prediction plays a key role in truck transportation management. However, it is a very challenging task since the truck traffic usually shows complex patterns. Most of the existing traffic prediction methods lack the ability to model the dynamic spatial-temporal correlations of truck traffic or ignore contributing contextual factors that impact traffic. Also, truck traffic data is typically sparse and noisy, which makes the truck speed prediction an even more challenging task. How to improve the truck speed prediction by taking advantage of other relevant truck traffic information (such as the truck flow) has not been investigated in depth. Additionally, traffic congestions and poor driving conditions caused by extreme weather conditions can make sudden changes in the general pattern of the truck speed. In this paper, we propose a novel Multi-Task Context Based Gated Recurrent Unit Graph Convolutional Network (MT-C2G) to predict the truck speed under extreme weather conditions. MT-C2G includes four major components: The spatial dependence learning component captures the spatial dependencies shaped by the topological structure of the road network. Truck traffic feature temporal dependence modeling component is built to acquire the temporal dependencies involved in the truck traffic features, and contextual feature temporal dependence modeling component employs a layer of GRU units to capture the temporal dependencies of contextual factors. The multi-task learning component then leverages the information between the truck speed and flow prediction tasks through attention mechanism for improving the performance. Moreover, a data augmentation method SMOTE is utilized to balance the data with the extreme weather conditions. Experiments on two real datasets demonstrate that the proposed MT-C2G fairly outperforms six state-of-the-art traffic prediction methods.

Leveraging the framework of Optimal Transport, we introduce a new family of generative autoencoders with a learnable prior, called Symmetric Wasserstein Autoencoders (SWAEs). We propose to symmetrically match the joint distributions of the observed data and the latent representation induced by the encoder and the decoder. The resulting algorithm jointly optimizes the modelling losses in both the data and the latent spaces with the loss in the data space leading to the denoising effect. With the symmetric treatment of the data and the latent representation, the algorithm implicitly preserves the local structure of the data in the latent space. To further improve the quality of the latent representation, we incorporate a reconstruction loss into the objective, which significantly benefits both the generation and reconstruction. We empirically show the superior performance of SWAEs over the state-of-the-art generative autoencoders in terms of classification, reconstruction, and generation.

Function estimation under shape restrictions, such as convexity, has many practical applications and has drawn a lot of recent interests. In this work we argue that convexity, as a global property, is too strict and prone to outliers. Instead, we propose to use weakly convex functions as a simple alternative to quantify “approximate convexity”—a notion that is perhaps more relevant in practice. We prove that, unlike convex functions, weakly convex functions can exactly interpolate any finite dataset and they are universal approximators. Through regularizing the modulus of convexity, we show that weakly convex functions can be efficiently estimated both statistically and algorithmically, requiring minimal modifications to existing algorithms and theory for estimating convex functions. Our numerical experiments confirm the class of weakly convex functions as another competitive alternative for nonparametric estimation.

Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches. In this work, we present a general framework for neural novelty detection that centers around a multivariate extension of the univariate quantile function. Our framework unifies and extends many classical and recent novelty detection algorithms, and opens the way to exploit recent advances in flow-based neural density estimation. We adapt the multiple gradient descent algorithm to obtain the first efficient end-to-end implementation of our framework that is free of tuning hyperparameters. Extensive experiments over a number of real datasets confirm the efficacy of our proposed method against state-of-the-art alternatives.