Transportation and logistics research in ISE focuses on routing and delivery scheduling, transportation safety, intelligent transportation systems, modeling of pricing policies for traffic networks, and manufacturing logistics.
Government sponsors have included agencies such as the National Science Foundation and National Institute for Disability and Rehabilitation Research. Other sponsors have included the CUBRC Center for Transportation Injury Research, Federal Express, and Praxair Incorporated.
Affiliated faculty include Batta, He, Walteros, Karwan, Kang, Wu and Murray.
Perhaps the most significant applications of operations research are in routing and scheduling of goods and services. These problems are based on real-world situations found in warehouses, shipping and transportation services. Much of the Department’s research in this area has focused on optimizing routing costs in shipping and airline industries via efficient scheduling approaches. Some has focused transportation network loading.
This research has primarily focused on the routing of hazardous materials and driver safety. The volume of hazardous materials (250,000 shipments daily) and the accompanying risk to which citizens are exposed necessitates the development of a set of guidelines to regulate its transport. Research has developed ways to minimize exposure to citizens. Other studies use experimental and computational methods to model driver behavior, or measure neurological responses during high-fidelity driving simulation. Other work related to driver safety includes the prevention of transportation-related injuries through the use of automated call notification devices, and improvement of accessibility among public transportation vehicles.
This line of work focuses on design of intelligent transportation systems considering driver, road conditions, environment, and in-vehicle tasks. Both experimental and computational modeling approaches are used to build intelligent transportation systems which adaptively work with human driver to prevent road accidents and improve transportation efficiency.
This research has focused on control policies for vehicular networks to reduce congestion, generate revenue, reduce risk, and increase sustainability. To achieve social welfare in using the vehicular infrastructure, the effects of road pricing on travelers’ behavior are investigated. The road pricing framework may be static, dynamic and/or stochastic.
In the manufacturing context, this research has explored strategic facility location, capacity planning (sizing), and production allocation problems, in which the use of fixed and mobile manufacturing facilities are considered simultaneously. Such problems frequently occur at the strategic planning level of industries such as chemical process plants, industrial gases, etc. A related research project involves studying the challenges associated with using automated guided vehicles that transport products in manufacturing facilities. In the distribution context, the research has explored integrated distribution center selection and space requirement problems on a two-stage network where products are shipped from plants to distribution centers, and then delivered to retailers to minimize total inbound and outbound transportation costs and total distribution center construction costs.
The explosion of traffic data in recent years has aroused increasing attentions and amounted to the best interests of intelligent transportation systems. Two areas can be fundamentally transformed by transportation big data. First, conventional loop-detector data including the traffic flow and occupancy, as well as the social media data such as Twitter, can be of great use in mining useful knowledge of highway traffic operations and in turn provide insights in alleviating traffic congestions, detecting traffic accidents, extracting the travel patterns etc. Second, traditional paper-based travel survey is no longer good enough to understand individuals’ travel demand. Researchers now aim to gain deep knowledge in people’s travel behavior, including travel modes, travel distance, trip purposes, departure times, and route choice, from leveraging high resolution longitudinal data sources (e.g. smartphones, Connected Vehicles and social media).
The ultimate goal of this project is to establish a theoretical framework for control and management of a large-scale heterogeneous transportation network, addressing three types of heterogeneities, including automation heterogeneity (e.g., human-driven vehicles and connected and automated vehicles), traffic mode heterogeneity (e.g., emergency vehicles, transit buses, subway, pedestrians, bicycles, cars, etc.), and area heterogeneity (e.g., central business district, residential area, trucking area, etc.).
The increasing attention and popularity gained by social media and social networking services over the past several years have brought new opportunities for e-commerce. Such form of e-commerce, called “Social Commerce”, is mediated by social media and social network services.
The goal of this project is to model order fulfillment with local stores and further reduce its operation and transportation costs by leveraging “Social Commerce”, enhanced by Big Data in social networks. The core idea is to allow customers’ online orders to be delivered by a network of “Social Transportation”, which consists of friends’ daily travel routines (trip chains), potentially intercepting packages at local stores and the target customer’s locations at home, work, gym, etc. The proposed project will serve as a theoretic foundation to leverage social networks to improve online order fulfillment with same day delivery from local stores.