Dagstuhl News January - December 2000

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Temporal reasoning in a logic programming language with modularity

Actualmente os Sistemas de Informação Organizacionais (SIO) lidam cada vez mais com informação que tem dependências temporais. Neste trabalho concebemos um ambiente de trabalho para construir e manter SIO Temporais. Este ambiente assenta sobre um linguagem lógica denominada Temporal Contextua) Logic Programming que integra modularidade com raciocínio temporal fazendo com que a utilização de um módulo dependa do tempo do contexto. Esta linguagem é a evolução de uma outra, também introduzida nesta tese, que combina Contextua) Logic Programming com Temporal Annotated Constraint Logic Programming, na qual a modularidade e o tempo são características ortogonais. Ambas as linguagens são formalmente discutidas e exemplificadas. As principais contribuições do trabalho descrito nesta tese incluem: • Optimização de Contextua) Logic Programming (CxLP) através de interpretação abstracta. • Sintaxe e semântica operacional para uma linguagem que combina de um modo independente as linguagens Temporal Annotated Constraint Logic Programming (TACLP) e CxLP. É apresentado um compilador para esta linguagem. • Linguagem (sintaxe e semântica) que integra de um modo inovador modularidade (CxLP) com raciocínio temporal (TACLP). Nesta linguagem a utilização de um dado módulo está dependente do tempo do contexto. É descrito um interpretador e um compilador para esta linguagem. • Ambiente de trabalho para construir e fazer a manutenção de SIO Temporais. Assenta sobre uma especificação revista da linguagem ISCO, adicionando classes e manipulação de dados temporais. É fornecido um compilador em que a linguagem resultante é a descrita no item anterior. ABSTRACT- Current Organisational Information Systems (OIS) deal with more and more Infor-mation that, is time dependent. In this work we provide a framework to construct and maintain Temporal OIS. This framework builds upon a logical language called Temporal Contextual. Logic Programming that deeply integrates modularity with tem-poral reasoning making the usage of a module time dependent. This language is an evolution of another one, also introduced in this thesis, that combines Contextual Logic Programming with Temporal Annotated Constraint Logic Programming where modularity and time are orthogonal features. Both languages are formally discussed and illustrated. The main contributions of the work described in this thesis include: • Optimisation of Contextual Logic Programming (CxLP) through abstract interpretation. • Syntax and operational semantics for an independent combination of the temporal framework Temporal Annotated Constraint Logic Programming (TACLP) and CxLP. A compiler for this language is also provided. • Language (syntax and semantics) that integrates in a innovative way modularity (CxLP) with temporal reasoning (TACLP). In this language the usage of a given module depends of the time of the context. An interpreter and a compiler for this language are described. • Framework to construct and maintain Temporal Organisational Information Systems. It builds upon a revised specification of the language ISCO, adding temporal classes and temporal data manipulation. A compiler targeting the language presented in the previous item is also given

Dagstuhl Annual Report January - December 2011

The International Conference and Research Center for Computer Science is a non-profit organization. Its objective is to promote world-class research in computer science and to host research seminars which enable new ideas to be showcased, problems to be discussed and the course to be set for future development in this field. The work being done to run this informatics center is documented in this report for the business year 2011

Analyse des connaissances mises en œuvre dans l’aide à la décision en maintenance d'hélicoptères

Ce rapport traite d'une étude réalisée dans le cadre du projet de recherche HELIMaintenance. L'objectif du projet HELIMaintenance est d'optimiser la maintenance des hélicoptères en réduisant les coûts de maintenance. Le but de ce projet de concevoir un Système Logistique Intégré capable d'analyser les données critiques de pièces en vol et de piloter l'activité de l'atelier de maintenance afin de réduire l'inactivité de l'hélicoptère. Dans le cadre de ce projet, l'un des workpackages vise à proposer des approches et des outils d'aide à décision pour la maintenance d'hélicoptères en vue d'améliorer la qualité et les performances de ce processus. L'axe principal de recherche s'appuie sur la gestion des connaissances, le retour d'expérience, les problèmes de satisfaction de contraintes et les différentes façons d'associer ces méthodes. En raison de l'avancement du projet, notre travail vise à identifier certains cas de maintenance typiques que nous pourrions assister par des outils d'aide à la décision en vue d'atteindre les objectifs du workpackage. Afin de réaliser ce projet, nous avons commencé par faire un état de l'art autour des axes de recherche. Ensuite, nous avons informés les partenaires industriels aux approches d'aide à la décision utilisables et nous avons modélisé le processus de maintenance d'hélicoptères avec le formalisme de modélisation de processus BPMN (Business Process Modeling Notation). Enfin, nous avons proposé quelques outils d'aide à la décision qui pourraient être développés pour continuer ce projet

Addressing Challenges in Healthcare Provider Scheduling

The goal when solving scheduling problems is to generate a high-quality schedule that satisfies every scheduling requirement. When scheduling healthcare providers, the quality of a schedule is often measured through provider satisfaction, a crucial issue that affects provider morale and patient safety. Manually generating a schedule for healthcare providers, as is often done in practice, can require a significant amount of time and effort. Additionally, since identifying a schedule that satisfies every scheduling requirement is challenging, it may not be practical to also consider all of the additional scheduling preferences that lead to improved provider satisfaction. Using computer-based mathematical programming to solve scheduling problems can dramatically decrease the time required to generate a schedule while also greatly improving the quality of the schedule. However, there are additional challenges associated with solving scheduling problems with computer-aided scheduling methods. This dissertation addresses some of these scheduling challenges in relation to scheduling healthcare providers. Specifically, we study three healthcare provider scheduling problems in this dissertation and propose methods for overcoming challenges associated with solving them. In the first problem, surgeons must be assigned to both operating and clinical rooms while satisfying many scheduling requirements. For this problem, we elaborate on the challenges we experienced while developing a mathematical scheduling model and show how the use of alternative variable definitions allowed us to overcome those challenges. In doing so, we explore the art of modeling and its impacts on solving a real-world scheduling problem. In the second scheduling problem we address, medical residents must be scheduled for their training rotations. For this problem, we expand on the previously discussed concept of using alternative decision variables by showing how different decision variable definitions can be used to simplify complex scheduling rules and improve computational performance. In both of the first two problems, it is desirable to maximize the number of individual scheduling requests that can be satisfied. Satisfying every scheduling request, however, is typically not possible. For solving the third scheduling problem we address, a resident shift scheduling problem, we develop a novel approach for resolving conflicting scheduling requests. Our approach identifies the exhaustive collection of maximally-feasible and minimally-infeasible request sets which can then be used by the decision maker to determine their preferred schedule.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138662/1/blemay_1.pd

Hyperconnected Fresh Supply Chains: Logistics & Market Expansion Frameworks

This thesis contributes novel frameworks that utilize transdisciplinary approaches to Fresh Supply Chain and Logistics Problems via Operations Research, GIS and Strategic Management. These fresh supply chain frameworks help build market deployment roadmaps, hub location in local supply chains and sustainable logistics strategies. Our study helps to provide solution approaches that are directly implementable in Industry.Ph.D

DISRUPTION RECOVERY IN COMMERCIAL AVIATION

This thesis presents three major contributions for commercial aviation planning and disruption recovery in commercial aviation. The first contribution presented in this thesis consists of a flight planning model to calculate Block Time and Fuel (BTF) consumed for an aircraft model during the flight. The BTF model computes the ground distance between the origin and destination airports, derives the flight’s cruise altitude, and by integrating two institutional data sets calculates the duration and the fuel consumed for the whole of taxi-out, take-off, climb, cruise, descent, approach, landing, and taxi-in phases. The model renders very good results for block time and consumed fuel however, it does not consider aircraft weight loss neither the influence of the wind. The second contribution of this thesis consists of a recovery procedure for disrupted aircraft rotations, the Constructive Heuristic for the Aircraft Recovery Problem (CHARP). The CHARP recovers the infeasible rotation combining a meta-heuristic that performs a pincer movement over the search space and Constraint Programming (CP). Additionally, the CHARP uses Constraint Propagation to reduce the size of the search therefore reducing computing. The initial experiments demonstrated that if Constraint Propagation was not used computing time would double. The recovery strategy included flight creation delays and cancellations however it did not include aircraft swap. The third contribution of this thesis combines the BTF model and the CHARP. Since the BTF model returns lower block time flights than those used by the CHARP this thesis investigates six disruption scenarios with shorter block time

Solving hard subgraph problems in parallel

This thesis improves the state of the art in exact, practical algorithms for finding subgraphs. We study maximum clique, subgraph isomorphism, and maximum common subgraph problems. These are widely applicable: within computing science, subgraph problems arise in document clustering, computer vision, the design of communication protocols, model checking, compiler code generation, malware detection, cryptography, and robotics; beyond, applications occur in biochemistry, electrical engineering, mathematics, law enforcement, fraud detection, fault diagnosis, manufacturing, and sociology. We therefore consider both the ``pure'' forms of these problems, and variants with labels and other domain-specific constraints. Although subgraph-finding should theoretically be hard, the constraint-based search algorithms we discuss can easily solve real-world instances involving graphs with thousands of vertices, and millions of edges. We therefore ask: is it possible to generate ``really hard'' instances for these problems, and if so, what can we learn? By extending research into combinatorial phase transition phenomena, we develop a better understanding of branching heuristics, as well as highlighting a serious flaw in the design of graph database systems. This thesis also demonstrates how to exploit two of the kinds of parallelism offered by current computer hardware. Bit parallelism allows us to carry out operations on whole sets of vertices in a single instruction---this is largely routine. Thread parallelism, to make use of the multiple cores offered by all modern processors, is more complex. We suggest three desirable performance characteristics that we would like when introducing thread parallelism: lack of risk (parallel cannot be exponentially slower than sequential), scalability (adding more processing cores cannot make runtimes worse), and reproducibility (the same instance on the same hardware will take roughly the same time every time it is run). We then detail the difficulties in guaranteeing these characteristics when using modern algorithmic techniques. Besides ensuring that parallelism cannot make things worse, we also increase the likelihood of it making things better. We compare randomised work stealing to new tailored strategies, and perform experiments to identify the factors contributing to good speedups. We show that whilst load balancing is difficult, the primary factor influencing the results is the interaction between branching heuristics and parallelism. By using parallelism to explicitly offset the commitment made to weak early branching choices, we obtain parallel subgraph solvers which are substantially and consistently better than the best sequential algorithms