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How Can Digitization in the Private Sector Benefit Everyone?

Publication: Goods Movement 2030: An Urban Freight Blog
Publication Date: 2023
Summary:

We’ve dug into how digitization continues to spark new developments in the urban freight landscape across the private and public sectors alike — with cities lagging behind digitization veterans like Amazon.

As Urban Freight Lab members noted at the fall meeting, it’s understandable why the private sector is ahead. Digitization helps companies improve operations toward lowering costs, saving time and money, and keeping customers satisfied. In other words, digitization helps companies with their fundamental concern: The bottom line.

And yet, companies’ choices and behavior in using digital tools can have the effect of helping more than their bottom lines. Private sector digitization can have spillover benefits, winding up helping communities and society at large, too. (To be clear, when we talk here about societal benefits, that includes mitigating and/or reducing the negative impacts of delivering goods to our homes and businesses.) But too often we treat the private and public sectors as wholly separate and siloed systems — though clearly they’re not.

The efficiencies digitization supports in urban freight might well wind up contributing to quality of life in city neighborhoods and communities. Those efficiencies can impact everything from congestion and traffic flow to pollution and Co2 emissions that contribute to climate change.

In this blog, we map three digitization moves in the private sector that could generate benefits for the public.

Recommended Citation:
"How Can Digitization in the Private Sector Benefit Everyone?" Goods Movement 2030 (blog). Urban Freight Lab, February 14, 2023. https://www.goodsmovement2030.com/post/3-digitization-moves
Technical Report

Cost, Emissions, and Customer Service Trade-Off Analysis In Pickup and Delivery Systems

Publication: Oregon Department of Transportation, Research Section
Publication Date: 2011
Summary:

This research offers a novel formulation for including emissions into fleet assignment and vehicle routing and for the trade-offs faced by fleet operators between cost, emissions, and service quality. This approach enables evaluation of the impact of a variety of internal changes (e.g. time window schemes) and external policies (e.g. spatial restrictions), and enables comparisons of the relative impacts on fleet emissions. To apply the above approach to real fleets, three different case studies were developed. Each of these cases has significant differences in their fleet composition, customers’ requirements, and operational features that provide this research with the opportunity to explore different scenarios.

The research includes estimations of the impact on cost and CO2 and NOX emissions from fleet upgrades, the impact on cost, emissions, and customer wait time when demand density or location changes, and the impact on cost, emissions, and customer wait time from congestion and time window flexibility. Additionally, it shows that any infrastructure use restriction increases cost and emissions. A discussion of the implications for policymakers and fleet operators in a variety of physical and transportation environments is also presented.

Authors: Dr. Anne Goodchild, Felipe Sandoval
Recommended Citation:
Goodchild, A., & Sandoval, F. (2011). Cost, Emissions, and Customer Service Trade-Off Analysis In Pickup and Delivery Systems (No. OR-RD 11-13). Oregon Department of Transportation Research Section.
Student Thesis and Dissertations

Emissions, Cost, and Customer Service Trade-off Analyses in Pickup and Delivery Systems

Publication Date: 2011
Summary:

As commercial vehicle activity grows, the environmental impacts of these movements have increasing negative effects, particularly in urban areas. The transportation sector is the largest producer of CO2 emissions in the United States, by end-use sector, accounting for 32% of CO2 emissions from fossil fuel combustion in 2008. Medium and heavy-duty trucks account for close to 22% of CO2 emissions within the transportation sector, making systems using these vehicles key contributors to air quality problems. An important well-known type of such systems is the “pickup and delivery” in which a fleet of vehicles pickups and/or delivers goods from customers.

Companies operating fleet of vehicles reduce their cost by efficiently designing the routes their vehicles follow and the schedules at which customers will be visited. This principle especially applies to pickup and delivery systems. Customers are spread out in urban regions or are located in different states which makes it critical to efficiently design the routes and schedules vehicles will follow. So far, a less costly operation has been the main focus of these companies, particularly pickup and delivery systems, and less attention has been paid to understand how cost and emissions relate and how to directly reduce the environmental impacts of their transportation activities. This is the research opportunity that motivates the present study.

While emissions from transportation activities are mostly understood broadly, this research looks carefully at relationships between cost, emissions and service quality at an individual-fleet level. This approach enables evaluation of the impact of a variety of internal changes and external policies based on different time window schemes, exposure to congestion, or impact of CO2 taxation. It this makes it possible to obtain particular and valuable insights from the changes in the relationship between cost, emissions and service quality for different fleet characteristics.

In an effort to apply the above approach to real fleets, two different case studies are approached and presented in this thesis. Each of these cases has significant differences in their fleet composition, customers’ requirements and operational features that provide this research with the opportunity to explore different scenarios.

Three research questions guide this research. They are explained in more detailed below. The present study does not seek to provide a conclusive answer for each of the research questions but does shed light on general insights and relationships for each of the different features presented in the road network, fleet composition, and customer features.

In summary, this research provides a better understanding of the relationships between fleet operating costs, emissions reductions and impacts on customer service. The insights are useful for companies trying to develop effective emission-reduction strategies. Additionally, public agencies can use these results to develop emissions reductions policies.

Authors: Felipe Sandoval
Recommended Citation:
Sandoval, Felipe (2011). Emissions, Cost, and Customer Service Trade-off Analyses in Pickup and Delivery Systems, University of Washington Master's Degree Thesis.
Thesis: Array
Paper

Evaluation of Emissions Reduction in Urban Pickup Systems Heterogeneous Fleet Case Study

 
Download PDF  (0.31 MB)
Publication: Transportation Research Record: Journal of the Transportation Research Board
Volume: 2224
Pages: 8-16
Publication Date: 2011
Summary:

A case study of the University of Washington Mailing Services, which operates a heterogeneous fleet of vehicles, provides insight into the impact of operational changes on cost, service quality, and emissions. An emissions minimization problem was formulated and solutions were identified with a creation and local search algorithm based on the I1 and 2-opts heuristics.

The algorithm could be used to find many solutions that could improve existing routing on both cost and emissions metrics, reduce emissions by an average of almost 6%, and reduce costs by an average of 9%. More significant cost and emissions savings could be found with service quality reductions. For example, reducing delivery frequency to once a day could lead to emissions and cost savings of close to 35% and 3%, respectively.

Rules of thumb for vehicle assignment within heterogeneous fleets were explored to gain an understanding of simple implementations, such as assigning cleaner vehicles to routes with more customers and longer travel distances.

This case study identified significant emissions reductions that could be obtained with minimal effects on cost and service and that offered new, practical applications that could be used by fleet managers interested in reducing their carbon footprint.

Authors: Dr. Anne Goodchild, Kelly Pitera, Felipe Sandoval
Recommended Citation:
Pitera, Kelly, Felipe Sandoval, and Anne Goodchild. "Evaluation of Emissions Reduction in Urban Pickup Systems: Heterogeneous Fleet Case Study." Transportation Research Record 2224, no. 1 (2011): 8-16. 

The Route Machine: An Optimization Framework (Phase 1)

The University of Washington Department of Laboratory Medicine runs 12 routes per day moving lab specimens and conducting departmental business. These routes have been developed over time in an ad hoc fashion.

The Urban Freight Lab will primarily focus on the following objectives for optimization:

  1. Minimize expected lead time (from the time the specimens are ready for pick up to the time they are delivered to the lab for testing)
  2. Minimize the extent to which couriers work outside of their maximum shift durations

The decisions the ‘route machine’ optimization framework ideally should inform:

  1. Day-to-day (operational) decision-making: Given all of the current capacities (i.e., number of vehicles) can routes be improved through changing order of routes or destinations serviced in route?
  2. Tactical decision-making: What modifications to the current capacities (i.e., increasing the number of vehicles) will produce the greatest benefit? How will the optimal routes change if there are modifications to customer requirements?
  3. Strategic decision-making: If UW Medicine Department of Laboratory Medicine expands its operations how will routes and capacities need to change to accommodate the new situation? What should the workforce balance between full-time workers and contractors look like?

This analysis includes:

  • Phase 1: Evaluate the existing routes on a qualitative basis to judge whether there is sufficient opportunity for improvement, and strategies that show greatest opportunity for improvement
  • Phase 1: Conduct an inventory of off the shelf tools and determine their suitability for the application
  • Phase 2: Build the Route Machine tool, either using off-the shelf software tools, building the tool from scratch, or some combination of the two.

Common Microhub (Seattle Neighborhood Delivery Hub)

Background

The importance of efficient urban logistics has never been greater. The response to COVID-19 has put new constraints and demands on the urban freight system but also highlighted the essential and critical nature of delivery and distribution. New requirements for reducing human contact only add weight to many of the strategies such as neighborhood kitchens, locker deliveries, and autonomous driverless delivery vehicles, already envisioned before the coronavirus pandemic. Social distancing and virus vector management also add new requirements and metrics for evaluating and managing logistics that are catalyzing innovation and motivating change in the urban logistics space.

What is a Common Microhub?

Also known as an urban consolidation center or a delivery transfer point, a microhub is a central drop-off/pick-up location for goods and services, which can be used by multiple delivery providers, retailers, and consumers. Microhubs can reduce energy consumption, noise pollution, congestion, and cost, and increase access, sustainability, and livability in cities, by allowing the final mile of delivery to be shifted to low-emission vehicles or soft transportation modes (cargo bike or walking), In addition to allowing for consolidation or deconsolidation of shipments, the design also enables neighbors to engage with additional services.

Microhubs provide:

  • access points for shared mobility
  • touchless pick-up and drop-off points
  • a home base for zero-emissions last-mile delivery, autonomous, and modalities
  • a shared public space
  • charging infrastructure
  • increased delivery density, reducing traffic and delivery vehicle dwell time
  • trip chaining capability

Urban Freight Lab’s Common Microhub Pilot: The Seattle Neighborhood Delivery Hub

The Urban Freight Lab’s Common Microhub project—the Seattle Neighborhood Delivery Hub—provides an opportunity for members to test and evaluate urban logistics strategies on the ground in Seattle’s Uptown neighborhood. As third-party logistics companies entering the last-mile space and more cities committing to environmental focus and zero-emissions vision, the interest in creating logistics places in urban proximity is growing. The outcomes of this research can guide the development of future microhub implementations in other cities. Participating stakeholders, while collaborative, operate with relative independence within the hub space. Data collection and analysis are ongoing; key indicators being measured include both operator performance and expected local impacts. In addition, lessons learned are encountered continuously and shared with UFL members as the project progresses.

Participants and Products

Product: Common Carrier Parcel Lockers
Host: Urban Freight LabDescription: The Urban Freight Lab is operating a common carrier parcel locker — a secure, automated, self-service storage system designed to accommodate deliveries from multiple transportation providers delivering a range of parcel sizes and open to all neighbors and commuters. Such lockers create delivery density, enabling vehicles to transport many packages to a single stop, rather than making multiple trips to accomplish the same task. This new approach reduces dwell time and failed first deliveries, both of which produce congestion and emissions, and increase costs. During the COVID-19 pandemic, the lockers also provide a no-contact solution for customers.

REEF neighborhood kitchen

Product: Neighborhood Kitchen and Infrastructure
Host: REEF

Description: Neighborhood kitchens are non-customer-facing modular vessels where food is prepared for mobile app or delivery orders. Removing front-of-house operations reduces a restaurant’s footprint, increases sustainability, and gives food entrepreneurs a platform by reducing overhead costs.

REEF is also the infrastructure partner, leveraging their parking lot holdings for the Seattle Neighborhood Delivery Hub location.

Coaster Cycles bike

Product: Electric-Assist Cargo Bike Fleet
Host: ​​Coaster Cycles

Description: Montana-based Coaster Cycles is providing an electric-assist cargo trikes fleet. These trikes are customized to carry BrightDrop EP1s, providing an agile, sustainable last-mile delivery solution in dense urban areas, reducing the emissions, congestion, and noise produced by traditional truck delivery.
(Watch the Coaster Cycle / EP1 deployment: https://vimeo.com/528552173)

Screenshot of Axlehire app

Product: Last-Mile Delivery Routing Software
HostAxleHire

Description: Berkeley-based logistics startup Axlehire provides last-mile delivery routing software that creates the fastest, most efficient routes possible. AxleHire is using the Seattle Neighborhood Delivery Hub site as a transshipment point, where trucks will transfer packages transported from a suburban depot to smaller, more nimble Coaster Cycle electrically-assisted bicycles, which are driven by Axlehire operators to a final customer.

Brightdrop's EP1 electric pallet

Product: Electric Pallet (EP1)
Host: ​BrightDrop (General Motors)

Description: BrightDrop (a subsidiary of General Motors) focuses on electrifying and improving the delivery of goods and services. BrightDrop’s first product to market is the EP1, a propulsion-assisted electric pallet designed to easily move goods over short distances. Because the pallet is electric-powered, it supports sustainability efforts, improves driver safety and freight security, lowers labor costs, and reduces errors and package touches.

Product: MUST Devices and Data Collection
Host: University of Washington Smart Transportation Application & Research (STAR) Lab

Description: To assess performance, researchers have deployed a multitude of sensors, including STAR Lab’s Mobile Unit for Sensing Traffic (MUST) sensors, cameras with vehicle recognition technology, GPS tracking sensors, and parking occupancy sensors. Researchers can gain a comprehensive understanding of delivery operations (such as miles traveled, infrastructure usage, speed, battery usage, interaction with other vehicles, bikes, and pedestrians) and activities at the site itself (such as parking occupancy, duration and, mode distribution of vehicle types at the site).

Location

The Seattle Neighborhood Delivery Hub is located at 130 5th Ave. N. in Seattle’s Uptown neighborhood.

Goals

The goals of the Common Microhub Research Project are to:

    1. Conduct a research scan of published reports that provide data-based evidence of the results of projects that have elements that are similar to Common Microhubs.
    2. Identify and characterize informal microhub activities observed in cities worldwide.
    3. Solicit input from UFL members as to the perceived benefits of microhubs and  the desired physical characteristics of a microhub
    4. Compare and contrast the priorities of UFL members with established metrics in the literature.
    5. Seek agreement from UFL members as to the microhub characteristics and location that would be feasible and desirable to operate in the Seattle region. Priority will be given to current UFL members, but should a third party external to UFL be necessary to run the microhub, proposals to host the microhub would be sought.
    6. Collect and analyze field data to measure both operator performance (including VMT, parking demand, fuel, and energy consumption) and expected local impacts (including travel and parking activity) before and after implementation. Data collection will rely on VMT, GPS, and travel time sources where available, but we expect to develop and implement customized methods to collect additional traffic and travel time data as needed. We may also interview the microhub operator and users to obtain qualitative data on the operations. The following tasks will be completed by the Urban Freight Lab in the two-year project.

Project Tasks

The following tasks will be completed by the Urban Freight Lab in the two-year project.

Task 1: Research Scan

Subtasks:

  1. Conduct a research scan of published reports that provide data-based evidence of the results of projects that have elements that are similar to Common MicroHubs.
  2. Identify and characterize informal microhub activities observed in cities worldwide.
  3. Write a summary of the results.

Task 2: Develop MicroHub Priorities

Subtasks:

  1. Solicit input from UFL members as to:
    • the perceived benefits of microhubs
    • the desired physical characteristics of a microhub
  2. Compare and contrast the priorities of UFL members with priorities demonstrated in the literature.

Task 3: Select Operator and Define Operational Model

Subtasks:

  1. With the help of a microhub operator, seek agreement from UFL members as to the microhub characteristics, services, operational goals and location that would be feasible and desirable to operate in the Seattle region.
    • Priority will be given to current UFL members to operate the Hub, but should a third party external to UFL be necessary to run the microhub, proposals to host the microhub would be sought.
  2. Go/No Go decision by researchers, UFL members, and microhub operator as to whether a pilot test will move forward.
    • Sufficient interest amongst participating UFL members and an understanding of the operating model and participants’ business objectives will be necessary to move forward as per the operator’s approval.
    • The operator will work independently with participants and/or the University of Washington to establish operating model(s) under separate agreement(s).

Task 4: Select Operator and Define Operational Model

Subtasks:

  1. Define key metrics for evaluation and data collection plan.
  2. With the support of UFL members participating in the pilot, collect “before” data to contrast with data collected during pilot operations.

Task 5: Implementation

Subtasks:

  1. Support the implementation of a microhub with UFL partners that have agreed to the terms of the pilot.
  2. Project schedule will allow for 6 months of operations, followed by 3 months for analysis.
  3. Collect and analyze field data to measure both operator performance (including VMT, parking demand, fuel, and energy consumption) and expected local impacts (including travel and parking activity) after implementation. Data collection will rely on VMT, GPS, and travel time sources where available, but we expect to develop and implement customized methods to collect additional traffic and travel time data as needed. We may also interview the operator and users to obtain qualitative data on the operations.

Task 6: Evaluate Operations

Subtasks:

  1. Provide progress reports at quarterly UFL meetings.
  2. Final report with key project findings.
Paper

A Meta-Heuristic Solution Approach to Isolated Evacuation Problems

 
Download PDF  (0.40 MB)
Publication: IEEE (Institute of Electrical and Electronics Engineers)
Volume: 2022 Winter Simulation Conference (WSC) INFORMS
Publication Date: 2022
Summary:

This paper provides an approximation method for the optimization of isolated evacuation operations, modeled through the recently introduced Isolated Community Evacuation Problem (ICEP). This routing model optimizes the planning for evacuations of isolated areas, such as islands, mountain valleys, or locations cut off through hostile military action or other hazards that are not accessible by road and require evacuation by a coordinated set of special equipment. Due to its routing structure, the ICEP is NP-complete and does not scale well. The urgent need for decisions during emergencies requires evacuation models to be solved quickly. Therefore, this paper investigates solving this problem using a Biased Random-Key Genetic Algorithm. The paper presents a new decoder specific to the ICEP, that allows to translate in between an instance of the S-ICEP and the BRKGA. This method approximates the global optimum and is suitable for parallel processing. The method is validated through computational experiments.

Authors: Dr. Anne GoodchildFiete Krutein, Linda Ng Boyle (University of Washington Dept. of Industrial & Systems Engineering)
Recommended Citation:
K. F. Krutein, L. N. Boyle and A. Goodchild, "A Meta-Heuristic Solution Approach to Isolated Evacuation Problems," 2022 Winter Simulation Conference (WSC), Singapore, 2022, pp. 2002-2012, doi: 10.1109/WSC57314.2022.10015470.
Paper

How to Improve Urban Delivery Routes’ Efficiency Considering Cruising for Parking Delays

 
Download PDF  (2.27 MB)
Publication Date: 2022
Summary:

This paper explores the value of providing parking availability data in urban environments for commercial vehicle deliveries. The research investigated how historic cruising and parking delay data can be leveraged to improve the routes of carriers in urban environments to increase cost efficiency. To do so, the research developed a methodology consisting of a travel time prediction model and a routing model to account for parking delay estimates. The method was applied both to a real-world case study to show its immediate application potential and to a synthetic data set to identify environments and route characteristics that would most benefit from considering this information.

Results from the real-world data set showed a mean total drive time savings of 1.5 percent. The synthetic data set showed a potential mean total drive time savings of 21.6 percent, with routes with fewer stops, a homogeneous spatial distribution, and a higher cruising time standard deviation showing the largest savings potential at up to 62.3 percent. The results demonstrated that higher visibility of curb activity for commercial vehicles can reduce time per vehicle spent in urban environments, which can decrease the impact on congestion and space use in cities.

Authors: Fiete KruteinDr. Giacomo Dalla ChiaraDr. Anne Goodchild, Todor Dimitrov (University of Washington Paul G. Allen School of Computer Science & Engineering)
Recommended Citation:
Krutein, Klaas Fiete and Dalla Chiara, Giacomo and Dimitrov, Todor and Goodchild, Anne, How to Improve Urban Delivery Routes' Efficiency Considering Cruising for Parking Delays. http://dx.doi.org/10.2139/ssrn.4183322