Framework Convention on Climate Change

Introduction to stochastic programming

https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2011-62.pdf

A review of dynamic vehicle routing problems

Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.

https://www.pnas.org/content/pnas/114/3/462.full.pdf

On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

Pickup and delivery problems constitute an important class of vehicle routing problems in which objects or people have to be collected and distributed. This paper introduces a general framework to model a large collection of pickup and delivery problems, as well as a three-field classification scheme for these problems. It surveys the methods used for solving them.

Static pickup and delivery problems: a classification scheme and survey

General solutions to the single vehicle routing problem with pickups and deliveries

The single vehicle routing problem with deliveries and selective pickups

Purpose – In the Norwegian oil and gas industry the upstream logistics includes providing the offshore installations with needed supplies and return flow of used materials and equipment. This paper considers a real‐life routing problem for supply vessels serving offshore installations at Haltenbanken off the northwest coast of Norway from its onshore supply base. The purpose of the paper is to explore how the offshore installation's limited storage capacity affects the routing of the supply vessels aiming towards creating efficient routes.Design/methodology/approach – A simplified version of the real‐life routing problem for one supply vessel is formulated as a mixed integer linear programming model that contains constraints reflecting the storage requirements problem. These constraints ensure that there is enough capacity at the platform decks and that it is possible to perform both pickup and delivery services.Findings – The model has been tested on real‐life‐sized instances based on data provided by th...

Irina Gribkovskaia

Papers

Static pickup and delivery problems: a classification scheme and survey

General solutions to the single vehicle routing problem with pickups and deliveries

The single vehicle routing problem with deliveries and selective pickups

Routing of supply vessels to petroleum installations

Lasso solution strategies for the vehicle routing problem with pickups and deliveries