An extensive English language bibliography on graph theory and its applications

Bibliography on graph theory and its application

Exact Localisations of Feedback Sets

The feedback arc (vertex) set problem, shortened FASP (FVSP), is to transform a given multi digraph $G=(V,E)$ into an acyclic graph by deleting as few arcs (vertices) as possible. Due to the results of Richard M. Karp in 1972 it is one of the classic NP-complete problems. An important contribution of this paper is that the subgraphs $G_{\mathrm{el}}(e)$, $G_{\mathrm{si}}(e)$ of all elementary cycles or simple cycles running through some arc $e \in E$, can be computed in $\mathcal{O}\big(|E|^2\big)$ and $\mathcal{O}(|E|^4)$, respectively. We use this fact and introduce the notion of the essential minor and isolated cycles, which yield a priori problem size reductions and in the special case of so called resolvable graphs an exact solution in $\mathcal{O}(|V||E|^3)$. We show that weighted versions of the FASP and FVSP possess a Bellman decomposition, which yields exact solutions using a dynamic programming technique in times $\mathcal{O}\big(2^{m}|E|^4\log(|V|)\big)$ and $\mathcal{O}\big(2^{n}\Delta(G)^4|V|^4\log(|E|)\big)$, where $m \leq |E|-|V| +1$, $n \leq (\Delta(G)-1)|V|-|E| +1$, respectively. The parameters $m,n$ can be computed in $\mathcal{O}(|E|^3)$, $\mathcal{O}(\Delta(G)^3|V|^3)$, respectively and denote the maximal dimension of the cycle space of all appearing meta graphs, decoding the intersection behavior of the cycles. Consequently, $m,n$ equal zero if all meta graphs are trees. Moreover, we deliver several heuristics and discuss how to control their variation from the optimum. Summarizing, the presented results allow us to suggest a strategy for an implementation of a fast and accurate FASP/FVSP-SOLVER

Agent-Based Computational Economics: A Constructive Approach to Economic Theory

This chapter explores the potential advantages and disadvantages of Agent-based Computational Economics (ACE) for the study of economic systems. General points are concretely illustrated using an ACE model of a two-sector decentralized market economy. Six issues are highlighted: Constructive understanding of production, pricing, and trade processes; the essential primacy of survival; strategic rivalry and market power; behavioral uncertainty and learning; the role of conventions and organizations; and the complex interactions among structural attributes, behaviors, and institutional arrangements. Extensive annotated pointers to ACE surveys, research, course materials, and software can be accessed here: http://www.econ.iastate.edu/tesfatsi/ace.htmagent-based computational economics; Learning; network formation; decentralized market economy

A Methodology to Determine Non-Fixed Performance Based Thresholds for Infrastructure Rehabilitation Scheduling

In an era of increasing demand and loading, aging infrastructure, and funding shortfalls, infrastructure agencies continue to seek cost-effective solutions to persistent and pervasive questions regarding the upkeep of their physical assets. One such question is the appropriateness of the current fixed condition thresholds used at several agencies for rehabilitation timing purposes, whether there is the possibility of having flexible rather than fixed thresholds, and determining what these thresholds should be. A related question is how these flexible thresholds may vary, depending on the objectives of the decision maker, the relative weight of agency and user costs, and the form of expression of the life-cycle cost associated with the candidate rehabilitation schedules. Fortunately, a number of past researchers have developed inputs that are valuable for addressing this issue. Also, there exists data from in-service infrastructure that could be used to test the hypotheses regarding the sensitivity of the optimal schedules

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Bidding Strategy to Support Decision-Making Based on Comprehensive Information in Construction Projects

© 2016 Ru Liang et al. This paper develops a unified method to support contractor for bidding selection in construction projects. A cross-functional contractor with 28 candidate units distributed in the three departments (construction units, design units, and suppliers) is used as an example. This problem is first formulated as a 0-1 quadratic programming problem through optimizing individual performance and collaborative performance of the candidate units based on individual information and collaborative information. Then, a multiobjective evolutionary algorithm is designed to solve this problem and a bidding selection problem for a major bridge project is used to demonstrate our proposed method. The results show that the decision-maker (DM) obtains a better contractor if he pays more attention to collaborative performance

Hybrid metaheuristics for solving multi-depot pickup and delivery problems

In today's logistics businesses, increasing petrol prices, fierce competition, dynamic business environments and volume volatility put pressure on logistics service providers (LSPs) or third party logistics providers (3PLs) to be efficient, differentiated, adaptive, and horizontally collaborative in order to survive and remain competitive. In this climate, efficient computerised-decision support tools play an essential role. Especially, for freight transportation, e efficiently solving a Pickup and Delivery Problem (PDP) and its variants by an optimisation engine is the core capability required in making operational planning and decisions. For PDPs, it is required to determine minimum-cost routes to serve a number of requests, each associated with paired pickup and delivery points. A robust solution method for solving PDPs is crucial to the success of implementing decision support tools, which are integrated with Geographic Information System (GIS) and Fleet Telematics so that the flexibility, agility, visibility and transparency are fulfilled. If these tools are effectively implemented, competitive advantage can be gained in the area of cost leadership and service differentiation. In this research, variants of PDPs, which multiple depots or providers are considered, are investigated. These are so called Multi-depot Pickup and Delivery Problems (MDPDPs). To increase geographical coverage, continue growth and encourage horizontal collaboration, efficiently solving the MDPDPs is vital to operational planning and its total costs. This research deals with designing optimisation algorithms for solving a variety of real-world applications. Mixed Integer Linear Programming (MILP) formulations of the MDPDPs are presented. Due to being NP-hard, the computational time for solving by exact methods becomes prohibitive. Several metaheuristics and hybrid metaheuristics are investigated in this thesis. The extensive computational experiments are carried out to demonstrate their speed, preciseness and robustness.Open Acces

Essays on Resource Allocation Efficiency and Behavior

This dissertation is comprised of three papers in the field of microeconomics. The first examines bidder’s choice auctions using both field and laboratory experiments. The field experiments demonstrate that traditional bidder’s choice auction theory does not always hold; the laboratory experiments subsequently isolate several characteristics of this auction format to explain why. We find that while price revelation does not impact the revenue superiority of the auction mechanism, multi-good demand significantly reduces the revenue premium. Intuitively, risk aversion plays less of a role when bidders have the opportunity to win multiple goods. The second chapter is theoretical and presents a dynamic Markov labor market tournament in which the manager does not have the ability to incentivize agents using money. Instead, the manager can use task assignment to reward and punish agents who are in and out of favor with him. This situation frequently characterizes public organizations such as schools and government agencies. The prize of the tournament is the difference between groups in the present value of the agent’s expected utility. We show that when the manager must delegate a certain number of tasks and when agents’ cost of contractible effort is a convex function, the manager can incentivize optimal non-contractible effort by agents. However, the total cost to the manager is higher than if the manager was able to use monetary incentives. The third chapter is an experimental paper that elicits consumer willingness to pay for food products labelled “natural”. The “natural” label is not regulated in the United States; however, several manufacturers are currently under lawsuit for selling “natural”-labelled food that contains genetically modified ingredients. This study uses an incentive-compatible mechanism and a survey to connect consumers’ beliefs to the premium that they associate with the “natural” label. Primarily, we find that consumers who believe “natural” means “no genetically modified organisms” (42% of our sample) are willing to pay a premium for “natural” food, whereas consumers who do not have this belief actually exhibit a negative premium. The overall effect is near zero, although the identified heterogeneity suggests that “natural” labels are potentially misleading

Coordinated budget allocation in multi-district highway agencies

Ph.DDOCTOR OF PHILOSOPH

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field