Graduation Year

2022

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Changhyun Kwon, Ph.D.

Committee Member

Changhyun Kwon, Ph.D.

Committee Member

Ankit Shah, Ph.D.

Committee Member

Hadi Charkhgard, Ph.D.

Committee Member

He Zhang, Ph.D.

Committee Member

Xiaopeng Li, Ph.D.

Keywords

Car Sharing, Cutting Plane, Metaheuristic, Network Design, Vehicle Routing

Abstract

This dissertation discusses three transportation problems. The first problem is a bi-level optimization problem that simultaneously optimizes facility locations and network design in hazardous materials transportation. In the upper level, the leader intends to reduce the facility setup cost and the hazmat exposure risk, by choosing facility locations and road segments to close for hazmat transportation. When making such decisions, the leader anticipates the response of the followers who want to minimize the transportation costs. A robust optimization approach with multiplicative uncertain parameters and polyhedral uncertainty sets is applied to deal with the uncertain risk and demand.

The second problem comes from the Free-floating electric vehicle sharing systems. It allows users to pick up and return an electric vehicle at any permissible parking location within a service area. Such service flexibility can drive a severe spatial imbalance between vehicle availability and trip demands. We consider the operations to relocate the EV fleet to meet the next day’s demand with sufficient battery levels. This relocation operation involves a complicated routing problem for a fleet of shuttles to transport the staff drivers who relocate the EVs to proper demand locations. We devise an efficient algorithm, which adapts the Adaptive Large Neighborhood Search framework. The experimental results validate the efficiency and effectiveness of our proposed algorithm and prove it is quite flexible to adapt to a dynamic environment.

The third problem is arc routing problem with the truck and the drones which cooperatively service the required edges. While the trucks follow road networks, drones can fly directly between any two points and off the network. The cooperation of the truck and the drone extends the traditional arc routing problem. We consider routing the truck and the drone with the limited flight range. An Adaptive Large Neighborhood Search is devised to solve the Drone-Truck Arc Routing Problem. The experimental results on the small-size and large-size instances validate the efficiency and effectiveness of the proposed method.

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