Selfish Routing on Dynamic Flows

Selfish routing on dynamic flows over time is used to model scenarios that vary with time in which individual agents act in their best interest. In this paper we provide a survey of a particular dynamic model, the deterministic queuing model, and discuss how the model can be adjusted and applied to different real-life scenarios. We then examine how these adjustments affect the computability, optimality, and existence of selfish routings.Comment: Oberlin College Computer Science Honors Thesis. Supervisor: Alexa Sharp, Oberlin Colleg

Evacuation Network Optimization Model with Lane-Based Reversal and Routing

Sometimes, the evacuation measure may seem to be the best choice as an emergency response. To enable an efficiency evacuation, a network optimization model which integrates lane-based reversal design and routing with intersection crossing conflict elimination for evacuation is constructed. The proposed bilevel model minimizes the total evacuation time to leave the evacuation zone. A tabu search algorithm is applied to find an optimal lane reversal plan in the upper-level. The lower-level utilizes a simulated annealing algorithm to get two types of “a single arc for an intersection approach” and “multiple arcs for an intersection approach” lane-based route plans with intersection crossing conflict elimination. An experiment of a nine-intersection evacuation zone illustrates the validity of the model and the algorithm. A field case with network topology of Jianye District around the Nanjing Olympics Sports Center is studied to show the applicability of this algorithm

The 1st International Electronic Conference on Algorithms

This book presents 22 of the accepted presentations at the 1st International Electronic Conference on Algorithms which was held completely online from September 27 to October 10, 2021. It contains 16 proceeding papers as well as 6 extended abstracts. The works presented in the book cover a wide range of fields dealing with the development of algorithms. Many of contributions are related to machine learning, in particular deep learning. Another main focus among the contributions is on problems dealing with graphs and networks, e.g., in connection with evacuation planning problems

Earliest Arrival Flows in Networks with Multiple Sinks

Earliest arrival flows model a central aspect of evacuation planning: In a dangerous situation, as many individuals as possible should be rescued at any point in time. Unfortunately, given a network with multiple sinks, flows over time satisfying this condition do not always exist. We analyze the special case of flows over time with zero transit times and characterize which networks always allow for earliest arrival flows

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms