Holistic Statistical Open Data Integration Based On Integer Linear Programming

International audienceIntegrating several Statistical Open Data (SOD) tables is a very promising issue. Various analysis scenarios are hidden behind these statistical data, which makes it important to have a holistic view of them. However, as these data are scattered in several tables, it is a slow and costly process to use existing pairwise schema matching approaches to integrate several schemas of the tables. Hence, we need automatic tools that rapidly converge to a holistic integrated view of data and give a good matching quality. In order to accomplish this objective, we propose a new 0-1 linear program, which automatically resolves the problem of holistic OD integration. It performs global optimal solutions maximizing the profit of similarities between OD graphs. The program encompasses different constraints related to graph structures and matching setup, in particular 1:1 matching. It is solved using a standard solver (CPLEX) and experiments show that it can handle several input graphs and good matching quality compared to existing tools

Intelligent Data Storage and Retrieval for Design Optimisation – an Overview

This paper documents the findings of a literature review conducted by the Sir Lawrence Wackett Centre for Aerospace Design Technology at RMIT University. The review investigates aspects of a proposed system for intelligent design optimisation. Such a system would be capable of efficiently storing (and compressing if required) a range of types of design data into an intelligent database. This database would be accessed by the system during subsequent design processes, allowing for search of relevant design data for re-use in later designs, allowing it to become very efficient in reducing the time for later designs as the database grows in size. Extensive research has been performed, in both theoretical aspects of the project, and practical examples of current similar systems. This research covers the areas of database systems, database queries, representation and compression of design data, geometric representation and heuristic methods for design applications.

Towards a Multi-Subject Analysis of Neural Connectivity

Directed acyclic graphs (DAGs) and associated probability models are widely used to model neural connectivity and communication channels. In many experiments, data are collected from multiple subjects whose connectivities may differ but are likely to share many features. In such circumstances it is natural to leverage similarity between subjects to improve statistical efficiency. The first exact algorithm for estimation of multiple related DAGs was recently proposed by Oates et al. 2014; in this letter we present examples and discuss implications of the methodology as applied to the analysis of fMRI data from a multi-subject experiment. Elicitation of tuning parameters requires care and we illustrate how this may proceed retrospectively based on technical replicate data. In addition to joint learning of subject-specific connectivity, we allow for heterogeneous collections of subjects and simultaneously estimate relationships between the subjects themselves. This letter aims to highlight the potential for exact estimation in the multi-subject setting.Comment: to appear in Neural Computation 27:1-2

Modelling Dry Port Systems in the Framework of Inland Waterway Container Terminals

Overcoming the global sustainability challenges of logistics requires applying solutions that minimize the negative effects of logistics activities. The most efficient way of doing so is through intermodal transportation (IT). Current IT systems rely mostly on road, rail, and sea transport, not inland waterway transport. Developing dry port (DP) terminals has been proven as a sustainable means of promoting and utilizing IT in the hinterland of seaport container terminals. Conventional DP systems consolidate container flows from/to seaports and integrate road and rail transportation modes in the hinterland which improves the sustainability of the whole logistics system. In this article, to extend literature on the sustainable development of different categories of IT terminals, especially DPs, and their varying roles, we examine the possibility of developing DP terminals within the framework of inland waterway container terminals (IWCTs). Establishing combined road–rail–inland waterway transport for observed container flows is expected to make the IT systems sustainable. As such, this article is the first to address the modelling of such DP systems. After mathematically formulating the problem of modelling DP systems, which entailed determining the number and location of DP terminals for IWCTs, their capacity, and their allocation of container flows, we solved the problem with a hybrid metaheuristic model based on the Bee Colony Optimisation (BCO) algorithm and the measurement of alternatives and ranking according to compromise solution (i.e., MARCOS) multi-criteria decision-making method. The results from our case study of the Danube region suggest that planning and developing DP terminals in the framework of IWCTs can indeed be sustainable, as well as contribute to the development of logistics networks, the regionalisation of river ports, and the geographic expansion of their hinterlands. Thus, the main contributions of this article are in proposing a novel DP concept variant, mathematically formulating the problems of its modelling, and developing an encompassing hybrid metaheuristic approach for treating the complex nature of the problem adequately

Algunas aportaciones de la investigación operativa a los problemas de localización

La toma de decisiones sobre localizaciones atrae, por su impacto social y económico, creciente interés de geógrafos, economistas y matemáticos. En las páginas que siguen describimos algunas aportaciones que se están realizando desde las Matemáticas (más concretamente, desde la Investigación Operativa), tanto en el modelado, como en la resolución de los problemas de Análisis de Localizaciones

Quantum Computing in Logistics and Supply Chain Management an Overview

The work explores the integration of quantum computing into logistics and supply chain management, emphasising its potential for use in complex optimisation problems. The discussion introduces quantum computing principles, focusing on quantum annealing and gate-based quantum computing, with the Quantum Approximate Optimisation Algorithm and Quantum Annealing as key algorithmic approaches. The paper provides an overview of quantum approaches to routing, logistic network design, fleet maintenance, cargo loading, prediction, and scheduling problems. Notably, most solutions in the literature are hybrid, combining quantum and classical computing. The conclusion highlights the early stage of quantum computing, emphasising its potential impact on logistics and supply chain optimisation. In the final overview, the literature is categorised, identifying quantum annealing dominance and a need for more research in prediction and machine learning is highlighted. The consensus is that quantum computing has great potential but faces current hardware limitations, necessitating further advancements for practical implementation

What?s your theory of effective schematic map design?

Amongst designers, researchers, and the general public, there exists a diverse array of opinion about good practice in schematic map design, and a lack of awareness that such opinions are not necessarily universally held nor supported by evidence. This paper gives an overview of the range of opinion that can be encountered, the consequences that this has for published designs, and a framework for organising the various views

Scalable allocation of safety integrity levels in automotive systems

The allocation of safety integrity requirements is an important problem in modern safety engineering. It is necessary to find an allocation that meets system level safety integrity targets and that is simultaneously cost-effective. As safety-critical systems grow in size and complexity, the problem becomes too difficult to be solved in the context of a manual process. Although this thesis addresses the generic problem of safety integrity requirements allocation, the automotive industry is taken as an application example.Recently, the problem has been partially addressed with the use of model-based safety analysis techniques and exact optimisation methods. However, usually, allocation cost impacts are either not directly taken into account or simple, linear cost models are considered; furthermore, given the combinatorial nature of the problem, applicability of the exact techniques to large problems is not a given. This thesis argues that it is possible to effectively and relatively efficiently solve the allocation problem using a mixture of model-based safety analysis and metaheuristic optimisation techniques. Since suitable model-based safety analysis techniques were already known at the start of this project (e.g. HiP-HOPS), the research focuses on the optimisation task.The thesis reviews the process of safety integrity requirements allocation and presents relevant related work. Then, the state-of-the-art of metaheuristic optimisation is analysed and a series of techniques, based on Genetic Algorithms, the Particle Swarm Optimiser and Tabu Search are developed. These techniques are applied to a set of problems based on complex engineering systems considering the use of different cost functions. The most promising method is selected for investigation of performance improvements and usability enhancements. Overall, the results show the feasibility of the approach and suggest good scalability whilst also pointing towards areas for improvement

Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation

In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these to generate combinatorial attacks to damage the safety of the system, and to cause economic loss. Results were compared against random attacks, and the performance of the EMO algorithms were evaluated using hypervolume, spread and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using a number of machine learning algorithms. Designed approach was further tested against the developed detection methods. Results demonstrate that EMO algorithms are a promising tool in the identification of the most vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can be used by control and security engineers to design security aware control, and test the effectiveness of security mechanisms, both during design, and later during system operation.Comment: 25 page