A Graph-Traversing Algorithm for Computing Some Stable Sets in Effectiveness Coalitional Games

We propose an algorithm for computing "main stable sets" recently introduced by Ciardiello, Di Liddo (2009) on effectiveness form coalitional games modeled through a directed pseudograph. The algorithm is based upon a graph traversing method exploring extended paths minimal in coalitions and we study some its interesting computational aspects for making these stability concepts as useful tools for decision theory.Algorithmic game theory; coalitional games; dominance relations; stable sets; graph theory.

A Study of Arc Strong Connectivity of Digraphs

My dissertation research was motivated by Matula and his study of a quantity he called the strength of a graph G, kappa\u27( G) = max{lcub}kappa\u27(H) : H G{rcub}. (Abstract shortened by ProQuest.)

Cooperation in Networks and Scheduling

This thesis deals with various models of cooperation in networks and scheduling. The main focus is how the benefits of this cooperation should be divided among the participating individuals. A major part of this analysis is concerned with stability of the cooperation. In addition, allocation rules are investigated, as well as properties of the underlying situations and games.

Metaheuristics for designing efficient routes & schedules for urban transportation networks

This thesis tackles the Urban Transit Network Design Problem (UTNDP) which involves determining an efficient set of routes and schedules for public transit networks. The UTNDP can be divided into five subproblems as identified by Ceder and Wilson [24]: i) network design, ii) frequency setting, iii) timetable development, iv) bus scheduling, and v) driver scheduling, with each problem requiring the output of the previous. In this thesis we focus on the first two stages, network design and frequency setting. We identify that evaluation is a major bottleneck for the network design problem and propose alternative approaches with the aim of decreasing the computation time. A multi-objective evolutionary algorithm (MOEA) for the network design problem is then presented that trades-off the passenger and operator costs. A passenger wishes to travel from their origin to destination in the shortest possible time, whereas the network operator must provide an adequate level of service whilst balancing the operational costs i.e. number of drivers and vehicles. The proposed MOEA combines a heuristically seeded population, using a novel construction algorithm, with several genetic operators to produce improved results compared with the state of the art from the literature. We provide an evaluation of the effectiveness of the genetic operators showing that improved performance, in terms of the number of dominating and nondominating solutions, is achieved as the size of the problem instance increases. Four surrogate models are proposed and an empirical evaluation is performed to assess the solution quality versus run trade-off in each case. It is found that surrogate models perform well on large problem instances producing improved Pareto sets compared with the original algorithm due to the increased amount of evolution that is allowed to occur under fixed time limits. Finally we empirically evaluate three multi-objective approaches for the frequency setting problem utilising the route networks produced during our network design procedure. It is shown that a MOEA based on the NSGAII framework provides the best quality solutions due to the cost of evaluation when using a neighbourhood based approach such as multi-objective tabu search. Constraints on vehicle capacity and fleet size are then introduced. It is shown that such constraints vastly reduce the number of solutions from network design that can successfully undergo frequency setting. A discussion is then presented highlighting the limitations of conducting network design and frequency setting separately along with alternative approaches that could be used in the future. We conclude this thesis by summarising our findings and presenting topics for future works

Topology Control, Routing Protocols and Performance Evaluation for Mobile Wireless Ad Hoc Networks

A mobile ad-hoc network (MANET) is a collection of wireless mobile nodes forming a temporary network without the support of any established infrastructure or centralized administration. There are many potential applications based the techniques of MANETs, such as disaster rescue, personal area networking, wireless conference, military applications, etc. MANETs face a number of challenges for designing a scalable routing protocol due to their natural characteristics. Guaranteeing delivery and the capability to handle dynamic connectivity are the most important issues for routing protocols in MANETs. In this dissertation, we will propose four algorithms that address different aspects of routing problems in MANETs. Firstly, in position based routing protocols to design a scalable location management scheme is inherently difficult. Enhanced Scalable Location management Service (EnSLS) is proposed to improve the scalability of existing location management services, and a mathematical model is proposed to compare the performance of the classical location service, GLS, and our protocol, EnSLS. The analytical model shows that EnSLS has better scalability compared with that of GLS. Secondly, virtual backbone routing can reduce communication overhead and speedup the routing process compared with many existing on-demand routing protocols for routing detection. In many studies, Minimum Connected Dominating Set (MCDS) is used to approximate virtual backbones in a unit-disk graph. However finding a MCDS is an NP-hard problem. In the dissertation, we develop two new pure localized protocols for calculating the CDS. One emphasizes forming a small size initial near-optimal CDS via marking process, and the other uses an iterative synchronized method to avoid illegal simultaneously removal of dominating nodes. Our new protocols largely reduce the number of nodes in CDS compared with existing methods. We show the efficiency of our approach through both theoretical analysis and simulation experiments. Finally, using multiple redundant paths for routing is a promising solution. However, selecting an optimal path set is an NP hard problem. We propose the Genetic Fuzzy Multi-path Routing Protocol (GFMRP), which is a multi-path routing protocol based on fuzzy set theory and evolutionary computing