Results 41 to 50 of about 558 (159)

Comparison of anticipatory algorithms for a dial-a-ride problem

open access: yesEuropean Journal of Operational Research, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ulrike Ritzinger   +3 more
openaire   +2 more sources

A tight formulation for the dial-a-ride problem

open access: yesEuropean Journal of Operational Research
Ridepooling services play an increasingly important role in modern transportation systems. With soaring demand and growing fleet sizes, the underlying route planning problems become increasingly challenging. In this context, we consider the dial-a-ride problem (DARP): Given a set of transportation requests with pick-up and delivery locations, passenger
Daniela Gaul   +4 more
openaire   +3 more sources

On-line single-server dial-a-ride problems

open access: yesTheoretical Computer Science, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
E. Feuerstein, L. Stougie (Leen)
openaire   +6 more sources

Leveraging Public Transit for Robotic Deliveries: A Column Generation Approach

open access: yesNaval Research Logistics (NRL), EarlyView.
ABSTRACT Autonomous mobile robots (AMRs) are small, electric, wheeled vehicles that operate at pedestrian speeds. In the last‐mile delivery service considered in this study, a fleet of AMRs is deployed across multiple recharging depots within a service area, from which they depart to perform point‐to‐point deliveries. We consider an operational setting
Yishay Shapira, Mor Kaspi
wiley   +1 more source

Typology and literature review for dial-a-ride problems [PDF]

open access: yesAnnals of Operations Research, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yves Molenbruch, Kris Braekers, An Caris
openaire   +2 more sources

An Exact Algorithm for the Traveling Salesman Problem With Pickup and Delivery Under Time Windows and Ride‐Time Constraints

open access: yesNetworks, EarlyView.
ABSTRACT The Traveling Salesman problem with pickup, delivery, and ride‐time constraints (TSPPD‐RT) requires a depot‐based vehicle to visit multiple known requests. Each request specifies a pickup vertex, corresponding delivery vertex, and maximum travel time between them (ride‐time). The goal is to find a tour that (i) starts and finishes at the depot,
Xinbo Zhang, Roberto Baldacci
wiley   +1 more source

Easing the Burden? Religiosity and Educational Inequality in Pain Interference

open access: yesJournal for the Scientific Study of Religion, EarlyView.
ABSTRACT A consistent inverse gradient in pain prevalence is typically observed across educational strata. Religion stands out as a source of social and personal support that may moderate the amount of chronic pain interference but may do so differently across the educational spectrum.
Laura Upenieks, Markus H. Schafer
wiley   +1 more source

Evaluating modified pairing insertion heuristics for efficient dial-a-ride problem solutions in healthcare logistics

open access: yesInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
A subset of the Vehicle Routing Problem (VRP), the Dial-a-Ride Problem (DARP) is concerned with the effective route planning of cars employed to pick up and deliver passengers to designated destinations.
Rodolfo Eleazar Pérez Loaiza   +2 more
doaj   +1 more source

Difference‐Making Under Metaphysical Indeterminacy

open access: yesPhilosophy &Public Affairs, Volume 54, Issue 4, Page 262-273, Fall 2026.
ABSTRACT Many of the most pressing moral problems we face involve collective harms generated by large numbers of individually insignificant actions. Unlike triggering cases—where a threshold exists such that a single act could be decisive—non‐triggering cases lack any such sharp cutoff.
Jessica Li
wiley   +1 more source

Advanced Demand Prediction for Demand‐Responsive Transit (DRT) Systems: Integrating Graph Convolutional Networks and Deep Learning Techniques

open access: yesEngineering Reports, Volume 8, Issue 11, November 2026.
This study develops a graph‐based deep learning framework to predict short‐term demand in demand‐responsive transit systems. Using real‐world data from Kalamazoo's Metro Connect, the model supports service zone optimization and infrastructure planning by accurately forecasting spatial–temporal passenger demand with high resolution.
Diana Al‐Nabulsi   +2 more
wiley   +1 more source

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