Logistics

Dynamic Dispatch for Same-Day Delivery


creative commons licensed ( BY ) flickr photo shared by (vincent desjardins)

Last week's Friday seminar featured Georgia Tech professor (and ITS Berkeley alum) Alan L. Erera presenting his work in the area of dynamic dispatching for same-day deliveries, focusing on the last mile problem. He briefly mentioned that he would not be discussing drones because they are not as efficient as trucks due to batching (and the new FAA regulations make them even more unfeasible). Erera focused on optimizing delivery dispatch multiple times throughout the day with prediction of when new orders may arrive and how to route the deliveries. Here are his slides so you can experience the talk all over again (without cookies).

Friday Seminar: Improving Bus Service with a Scalable Dynamic Holding Control

Commuter Warning

This week's Friday Seminar features UC Berkeley Ph.D. candidate Juan Argote presenting his research on control methods for transit services in, "Improving Bus Service with a Scalable Dynamic Holding Control":

Service unreliability is widely recognized as one of the main deterrents for travelers to use buses as their mode of transportation. Bus systems are exposed to an adverse feedback loop that generates a tendency for them to fall out of sync. This tendency can be counteracted by the application of control strategies that regulate the motion of the buses. This is well known among transit operators and some research has been devoted to address the issue. However, existing methods that are simple enough to be scalable can only handle headway-based operations of a single line.

This research proposes a scalable control method that applies dynamic holding based on real-time conditions and that allows buses to stay on schedule. A formulation that generalizes dynamic holding control strategies is developed for isolated bus lines. Stability conditions are derived and a quasi-optimal control that requires minimal data is also presented. The performance of this control is validated through simulation. The control is then extended to corridors where multiple bus lines overlap. A real-world case study in San Sebastián, where a system of coordinated on-board devices was deployed, is used to validate the control performance in this type of scenario. Finally, the resilience of the control is assessed considering multiple potential adversities.

The seminar takes place on Friday, April 25 in 534 Davis from 4-5 PM. Cookie Hour will be in the library at 3:30. 

 

Uber to expand into "urban logistics"?

Travis Kalanick, Co-Founder & CEO, Uber @ LeWeb Paris Day 1 2013-2555

Transportation Network Company, or as most people refer to them, rideshare company Uber is looking to expand its market. This week Chief Executive Travis Kalanick spoke a Le Web, where he described the Uber's plans to enter "urban logistics". He said, "Today, we are in the business of delivering cars in five minutes. Once you're in the business of delivering cars in five minutes, there are a lot of things you can deliver in five minutes."

Phase 2 will build upon notable promotions as delivering kittens, ice cream, and Christmas trees

Friday Seminar: Dynamic Weather Routes

Southampton Airport

Seminar Time! This week's TRANSOC Friday Seminar is about smarter routing of flights to avoid severe weather. Dave McNally of NASA Ames Research Center, Aviation Systems Division will present, "Dynamic Weather Routes: The Search for Smarter Flight Routes"

Adverse weather is the leading cause of flight delay in the US National Airspace System. Airline flight dispatchers must file flight plans about an hour before push-back from the gate using their best available weather forecasts. FAA traffic managers assess the impact of weather on traffic flows, and, when necessary, implement standard reroutes for groups of flights. Given the uncertainty in weather, standardized reroutes may result in large buffers between flight routes and forecast weather. Weather changes as flights progress along planned routes, and because airline dispatchers and FAA traffic managers are busy, especially during weather events, they may miss workable opportunities for more efficient routes around weather. Dynamic Weather Routes (DWR) is a search engine that continuously and automatically analyzes in-flight aircraft in en route airspace and proposes simple route amendments for more efficient routes around convective weather while considering winds aloft, sector congestion, traffic conflicts, and active Special Use Airspace. NASA and American Airlines (AA) are conducting an operational trial of DWR at the AA System Operations Center in Fort Worth, Texas. A key result of the trial is that since airline operators are especially busy during weather events, it is more effective to let the automation identify and alert users to potentially high-value reroute options.

The seminar is Friday October 18, 2013 from 4:00-5:00 PM in 534 Davis. Cookie Hour happens at 3:30 in the library. See you there!

Rebalancing and Bikeshare

The mythical #DivvyRed

Is this the summer of Bikeshare? Divvy Bikes in Chicago launched last month. CitiBikes in New York City launched around Memorial Day. Any time now Bay Area Bike Share will be launching in San Francisco and on then Peninsula. 

The issue of having bikes where people want them is a perennial issue for bikeshare systems. "Rebalancing" is the act of moving inventory around to match demand and travel patterns. This map provides realtime visualizations of the demand of bikeshare systems around the world. Researchers are working on solving the rebalancing problem

A new article from EURO Journal on Transportation and Logistics works to develop a model for rebalancing. "Static repositioning in a bike-sharing system: models and solution approaches" by Tal Raviv, Michal Tzur, and Iris A. Forma, looks at how rebalancing or repositioning can help bikeshare systems.

Bike-sharing systems allow people to rent a bicycle at one of many automatic rental stations scattered around the city, use them for a short journey and return them at any station in the city. A crucial factor for the success of a bike-sharing system is its ability to meet the fluctuating demand for bicycles and for vacant lockers at each station. This is achieved by means of a repositioning operation, which consists of removing bicycles from some stations and transferring them to other stations, using a dedicated fleet of trucks. Operating such a fleet in a large bike-sharing system is an intricate problem consisting of decisions regarding the routes that the vehicles should follow and the number of bicycles that should be removed or placed at each station on each visit of the vehicles. In this paper, we present our modeling approach to the problem that generalizes existing routing models in the literature. This is done by introducing a unique convex objective function as well as time-related considerations. We present two mixed integer linear program formulations, discuss the assumptions associated with each, strengthen them by several valid inequalities and dominance rules, and compare their performances through an extensive numerical study. The results indicate that one of the formulations is very effective in obtaining high quality solutions to real life instances of the problem consisting of up to 104 stations and two vehicles. Finally, we draw insights on the characteristics of good solutions.

The full paper can be found here

Truck Emissions at Container Terminals

Port of Singapore

Freight and transportation are large producers of emissions and considered a good area to target for emissions reduction. How though? A new paper from Transportation Research Part E: Logistics and Transportation Review examines how queing optimization can help. "Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern," estimate emissions produced during truck idling and wait times at ports. 

This study proposes a methodology to optimize truck arrival patterns to reduce emissions from idling truck engines at marine container terminals. A bi-objective model is developed minimizing both truck waiting times and truck arrival pattern change. The truck waiting time is estimated via a queueing network. Based on the waiting time, truck idling emissions are estimated. The proposed methodology is evaluated with a case study, where truck arrival rates vary over time. We propose a Genetic Algorithm based heuristic to solve the resulting problem. Result shows that, a small shift of truck arrivals can significantly reduce truck emissions, especially at the gate.

You can read the full paper here

 

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