12 research outputs found
A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery
Electric vehicle routing problems can be particularly complex when recharging
must be performed mid-route. In some applications such as the e-commerce parcel
delivery truck routing, however, mid-route recharging may not be necessary
because of constraints on vehicle capacities and maximum allowed time for
delivery. In this study, we develop a mixed-integer optimization model that
exactly solves such a time-constrained capacitated vehicle routing problem,
especially of interest to e-commerce parcel delivery vehicles. We compare our
solution method with an existing metaheuristic and carry out exhaustive case
studies considering four U.S. cities -- Austin, TX; Bloomington, IL; Chicago,
IL; and Detroit, MI -- and two vehicle types: conventional vehicles and battery
electric vehicles (BEVs). In these studies we examine the impact of vehicle
capacity, maximum allowed travel time, service time (dwelling time to
physically deliver the parcel), and BEV range on system-level performance
metrics including vehicle miles traveled (VMT). We find that the service time
followed by the vehicle capacity plays a key role in the performance of our
approach. We assume an 80-mile BEV range as a baseline without mid-route
recharging. Our results show that BEV range has a minimal impact on performance
metrics because the VMT per vehicle averages around 72 miles. In a case study
for shared-economy parcel deliveries, we observe that VMT could be reduced by
38.8\% in Austin if service providers were to operate their distribution
centers jointly
CREPE: Learnable Prompting With CLIP Improves Visual Relationship Prediction
In this paper, we explore the potential of Vision-Language Models (VLMs),
specifically CLIP, in predicting visual object relationships, which involves
interpreting visual features from images into language-based relations. Current
state-of-the-art methods use complex graphical models that utilize language
cues and visual features to address this challenge. We hypothesize that the
strong language priors in CLIP embeddings can simplify these graphical models
paving for a simpler approach. We adopt the UVTransE relation prediction
framework, which learns the relation as a translational embedding with subject,
object, and union box embeddings from a scene. We systematically explore the
design of CLIP-based subject, object, and union-box representations within the
UVTransE framework and propose CREPE (CLIP Representation Enhanced Predicate
Estimation). CREPE utilizes text-based representations for all three bounding
boxes and introduces a novel contrastive training strategy to automatically
infer the text prompt for union-box. Our approach achieves state-of-the-art
performance in predicate estimation, mR@5 27.79, and mR@20 31.95 on the Visual
Genome benchmark, achieving a 15.3\% gain in performance over recent
state-of-the-art at mR@20. This work demonstrates CLIP's effectiveness in
object relation prediction and encourages further research on VLMs in this
challenging domain
Electric Vehicle Supply Equipment Location and Capacity Allocation for Fixed-Route Networks
Electric vehicle (EV) supply equipment location and allocation (EVSELCA)
problems for freight vehicles are becoming more important because of the
trending electrification shift. Some previous works address EV charger location
and vehicle routing problems simultaneously by generating vehicle routes from
scratch. Although such routes can be efficient, introducing new routes may
violate practical constraints, such as drive schedules, and satisfying
electrification requirements can require dramatically altering existing routes.
To address the challenges in the prevailing adoption scheme, we approach the
problem from a fixed-route perspective. We develop a mixed-integer linear
program, a clustering approach, and a metaheuristic solution method using a
genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach
simplifies the problem by grouping customers into clusters, while the GA
generates solutions that are shown to be nearly optimal for small problem
cases. A case study examines how charger costs, energy costs, the value of time
(VOT), and battery capacity impact the cost of the EVSELCA. Charger costs were
found to be the most significant component in the objective function, with an
80\% decrease resulting in a 25\% cost reduction. VOT costs decrease
substantially as energy costs increase. The number of fast chargers increases
as VOT doubles. Longer EV ranges decrease total costs up to a certain point,
beyond which the decrease in total costs is negligible