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In this paper, we analyze a perturbed formulation of bilevel optimization problems, which we refer to as δ-perturbed formulation. The δ-perturbed formulation allows to handle the lower level optimization problem efficiently when there are multiple lower level optimal solutions. By using an appropriate perturbation strategy for the optimistic or pessimistic formulation, one can ensure that the optimization problem at the lower level contains only a single (approximate) optimal solution for any given decision at the upper level. The optimistic or the pessimistic bilevel optimal solution can then be efficiently searched for by algorithms that rely on solving the lower level optimization problem multiple times during the solution search procedure. The δ-perturbed formulation is arrived at by adding the upper level objective function to the lower level objective function after multiplying the upper level objective by a small positive/negative δ. We provide a proof that the δ-perturbed formulation is approximately equivalent to the original optimistic or pessimistic formulation and give an error bound for the approximation. We apply this scheme to a class of algorithms that attempts to solve optimistic and pessimistic variants of bilevel optimization problems by repeatedly solving the lower level optimization problem.

Collecting complex information on the status of machinery is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Condition-Based Maintenance. This paper discusses the sensors that can be used to support maintenance, as of different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. This paper also presents four different real-world examples of project pilots that make use of the described sensors and draws a comparison between them. In particular, each scenario has unique characteristics requiring different families of sensors, but on the other hand provides similar characteristics on other aspects.

ABSTRACTThe general concept of MANTIS project is to provide a proactive maintenance service platform architecture that allows to monitor essential system parameters and schedule maintenance in order to predict and prevent imminent failures. Human–machine interaction (HMI) is an important integral part of the platform by providing the right information in the right modality to the users when needed. As MANTIS comprises 11 distinct industrial use cases, the design of such HMI presents a great challenge. The framework presented in this paper originates from the scenario-based design and can be treated as a part of the overall scenario-based usability engineering approach. The framework has been conceived from an extensive list of HMI features extracted from the descriptions of use-case scenarios provided by each industrial partner. Due to the broad range of representative industry environments including production asset maintenance, vehicle maintenance, energy production management and health equipment maintenance we believe that the resulting HMI framework can be applied in different cases in practice and the paper would also be of general interest to the readers.

The aim of the paper is to present a critical review of analytics and visualisation technology for big data, and propose future directions to overcome the shortcomings of the current technologies. The current machine learning and data-mining algorithms are operating mostly on predefined scales of aggregation, while in the vast amounts of data the problem arises at the level of aggregation which cannot be defined ahead of time. We therefore identify a novel and extended architecture to operate on flexible multi-resolution hypothesis space. With such architecture framework the goal is to open a space of possibly discovered models towards classes of data, which are by today's approaches discovered only for special cases. Furthermore, the multi-resolution approach to big-data analytics could allow scenarios like semi-supervised and unsupervised anomaly detection, detecting complex relationships from the heterogeneous data sources, and providing ground for visualisation of complex processes.

In this paper, we present a methodology for learning elementary-school mathematics online. It is supported by a decision making system, based on evolutionary computation, which leads a student in selecting an optimal subset of math items to effectively upgrade the knowledge. We evaluated the system’s effectiveness with the active participation of experienced teachers and students. The teachers evaluated solutions provided by the systems, judging the selection with respect to content areas and difficulty levels. The students were solving math items suggested by the system, gradually improving their knowledge. They both agreed that the system could effectively be used for e-learning.

When dealing with real-world problems, it turns out that there are many specifics of the problem we are trying to solve. Since many algorithms that are being developed are evaluated and compared on test benchmark problems, they can simulate real-world problems up to some degree and specifics are not presumed and tested. To make algorithms efficient, such specifics need to be considered and included in the problem solving. In this paper, a real-world production scheduling problem is addressed. A typical approach with genetic algorithm turned out to be insufficient due to added complexity of many specifics. To successfully solve this problem, a memetic algorithm, which uses problem-specific local search procedures to improve solutions acquired by genetic algorithm, is proposed. It is shown that the use of such local search procedures can significantly improve the effectiveness and efficiency of the algorithm.