Click for next page ( 11

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 10
10 els, which may or may not reflect how freight routing decisions Routing and Scheduling Models are made by private-sector operators, shippers, and receivers, and this contributes to challenges in interpreting results. Typically used by the private-sector freight community, routing and scheduling models optimize the routing and fre- quency of shipments. The objective of these models is to min- Supply Chain and Logistics Models imize vehicles, vehicle miles traveled, and labor; satisfy service Supply chain and logistics models aim to capture the requirements; maximize orders; and/or maximize the freight upstream and downstream relationships between suppliers volume delivered per mile. Different types of routing and and customers and the decisions that drive freight demand. scheduling models solve problems that range in complexity as They actually estimate the total logistics cost of shipping, follows: including direct transportation expense and inventory cost Traveling salesman problem--Determines the shortest associated with modal lot sizes and service profiles. The mod- els assume that customers (shippers) select the lowest-cost path routing through a tour of destinations, visiting each option, and they depend on information about logistical fac- destination exactly once and returning to the starting origin. tors in transportation and industry. Shipments are assigned to Vehicle routing problem--Allocates vehicles and assigns one mode or another, while allowing for uncertainty associ- routings from a central location to serve a set of geograph- ated with inventory risk, carrier performance, or unmeasured ically dispersed customers while minimizing the total dis- factors. tance traveled. These models can help provide information on a number of Vehicle routing problem with time windows--Schedules topics that would be of interest to public-sector freight plan- and allocates vehicles and assigns routings from a central ners, particularly freight trip chaining and mode-choice deci- location to serve a set of geographically dispersed customers sions. However, most of these models were initially developed with time-window requirements. with the intention of helping producers (who ship goods) Pickup and delivery problem with time windows-- decide on the best choices among shipping options. Usefulness Determines vehicle assignments, routes, and schedules to of these models for more general transportation planning is transport loads of specific size from a location with a pickup highly dependent on the actual availability of modal service time window to a delivery location with a specific delivery options for the specific type of commodity being shipped and time window. the shipper's specific set of customer destinations. Without that information, such models can overstate opportunities for The models are customized on a case-by-case basis to reflect modal diversion due to inability to sufficiently filter out modal a company's operating environment and customer needs. options that are not really available. Recently, dynamic routing and scheduling have grown in importance due to the availability of real-time information from GPS and wireless communication devices. Similar to the Network Design Models network design models described previously, there are few Network design models include private-sector models for examples of routing and scheduling model implementation locating factories, distribution centers, warehouses, and other among public-sector transportation planning agencies. How- freight-generating facilities. Freight logistics companies and ever, routing and scheduling information at intermodal facil- freight carriers must consider the frequency, mode, routing, ities, distribution centers, ports, etc., could greatly improve and scheduling of freight movement within a network to pro- the estimation of internal freight trips. vide high-quality, low-cost, reliable service to their customers. As shown in Table 2.1, most of these tools are widely used Network design planning is very challenging given its scale, in practice and can be used to answer a number of freight- complexities, and decision interdependencies. Likewise, net- related planning and policy questions. The exceptions are sup- work design formulations are very difficult to solve, except in ply chain/logistics, network design, and routing and schedul- the simplest of scenarios. ing models, each of which primarily serves private-sector As network design models inherently relate to private- functions. sector operations and efficiencies, examples of public-sector model implementations or applications remain scarce. Given 2.3 Gaps, Issues, and Challenges the proprietary nature of the data required to build and opti- mize a network design model, the public sector faces obsta- Despite the relatively wide use of several model types (time cles to applying network design techniques for their decision- series, behavioral, commodity IO, multimodal network, and making purposes. microsimulation), the models do not completely meet the

OCR for page 10
11 Table 2.1. A comparison of model development and implementation in the literature to public-sector applications. Model Model Public Sector Model Category Description Development Implementation Applications Time Series Short-, medium-, and long-term forecasts of freight demand and freight activity Behavioral Models how companies perceive and select from the many available freight choices. Includes choice- based and survey-based demand models Commodity- Estimate current and forecasted freight traffic Based and generation and distribution by linking industrial Input-Output activity through input-output models of economic activity Multimodal Link-node network representations of freight Network supply useful for determining travel times, costs, reliability, and overall level of service Microsimulation Microsimulation models the individual movement and Agent-Based of large numbers of units and their attributes, while agent-based modeli ng defines potential actors in freight transporatation and an allowable set of actions and interactions Supply Chain/ Supply chains define the life cycle of products from Logistics raw materials to the final consumer, including production, inventory, and transportation Network Design Private-sector models for locating factories, distri bution centers, warehouses, and other freight generating facilities Routing and Private-sector models for locating factories, Scheduling distri bution centers, warehouses, and other freight generating facilities Other and Hybrid models, real-time decision making Emerging Topics Widely used, state of the practice Emerging model, limited use Lacking research or application needs of public-sector freight planners, modelers, and decision- nomic advantage. Freight planners, modelers, and decision- makers. Key issues include the following: makers require quick and reliable methods to determine the economic benefits of transportation investments as well as Lack of a national vision for freight analysis--Since states how economic and accessibility constraints (bottlenecks are conduits for freight movements and regions are impacted and employment base) are hindering statewide and regional by policies and activities originating from outside areas, economic development efforts. many DOTs and MPOs stress the need to establish a Data limitations--Since freight models often are devel- national vision for freight analysis. Establishment of a oped and validated with insufficient data, public-sector national vision for freight demand modeling would help agencies and decisionmakers often lack confidence in model coordinate and guide freight data collection, model con- results. To improve the statistical validity of their freight sistency, and validation/calibration procedures across all models, agencies require more observed data that is gener- public-sector agencies. ated with greater frequency and accuracy, to conduct more Limited ties between freight planning and economic robust model validation. Similarly, agencies require freight development--There is a need to fully integrate freight data at the appropriate level of detail to support the level of demand models with economic models to facilitate trans- sophistication at which the model is expected to perform. portation strategies that maximize a state or region's eco- Many agencies that have not yet developed a freight demand

OCR for page 10
12 model, or are considering an upgrade to a more sophisti- behavioral models that capture trip chains, less than cated model indicated that data limitations are a primary truckload movements, local truck deliveries, and their obstacle. Specific data needs include associated routings. Seasonal trucking variations to account for crop harvest Freight routing and route diversion--Existing models are cycles (in rural areas) and consumer demand (in urban deficient in their ability to assign trucks to the routes they areas and around trade gateways); actually use. Similarly, agencies need the ability to esti- Time-of-day factors to help evaluate the impacts of mate freight diversion in response to dedicated truck lanes policy actions designed to shift truck traffic to off-peak and tolls under different pricing and policy scenarios. periods or other congestion mitigation strategies; and Model adaptability and responsiveness--Freight demand Private-sector data to better understand routing and models are too complex, unwieldy, and time-intensive to supply chain decisions and impacts of railroads, truck- respond quickly to changing economic conditions as they ing companies, ports, and shippers. arise, such as rising fuel costs or facility closures. The time Limitations of existing tools--As described in Table 2.1, required to develop or update a model is not aligned with existing freight demand modeling and analysis tools are the short timeline of freight market demands. There is a often insufficient to answer freight-related questions being need for additional analytical tools that can piggyback on posed by freight planners, freight decisionmakers, and other existing models to provide quick-response answers to stakeholders. Critical limitations include time-sensitive questions. Similarly, freight models need Multimodal network modeling--Agencies need the abil- to be capable of performing various applications and ity to model multimodal freight flows and interactions, adaptable to the dynamic nature of the freight industry. not just light, medium, and heavy trucks. Also needed are However, given the complexity of many freight demand dynamic modeling capabilities to evaluate logistics- models, incorporating new tools or changes into the driven, market-driven, and/or policy-driven mode shifts. model is often beyond the capabilities of in-house staff. Multimodal network modeling would also allow agencies The subset of people that can actually run the model gets to quantify and compare the burden of each freight mode smaller as the model gets more sophisticated. on the system's infrastructure. Temporal variability--Particularly relevant to urban Behavioral modeling--The conventional four-step travel truck models, current freight demand models often lack demand models cannot accurately capture the complex- the ability to evaluate temporal variability, such as time ities of supply chains and freight systems. They neglect of day and seasonal demand. Regional travel demand the importance of tour-based and activity-based model- models originally developed to support long-range plan- ing. However, few public-sector agencies have developed ning did not require time-of-day sensitivities.