Exact and Heuristic Hybrid Approaches for Scheduling and Clustering Problems

This thesis deals with the design of exact and heuristic algorithms for scheduling and clustering combinatorial optimization problems. All the works are linked by the fact that all the presented methods arebasically hybrid algorithms, that mix techniques used in the world of combinatorial optimization. The algorithms are all efficient in practice, but the one presented in Chapter 4, that has mostly theoretical interest. Chapter 2 presents practical solution algorithms based on an ILP model for an energy scheduling combinatorial problem that arises in a smart building context. Chapter 3 presents a new cutting stock problem and introduce a mathematical formulation and a heuristic solution approach based on a heuristic column generation scheme. Chapter 4 provides an exact exponential algorithm, whose importance is only theoretical so far, for a classical scheduling problem: the Single Machine Total Tardiness Problem. The relevant aspect is that the designed algorithm has the best worst case complexity for the problem, that has been studied for several decades. Furthermore, such result is based on a new technique, called Branch and Merge, that avoids the solution of several equivalent sub-problems in a branching algorithm that requires polynomial space. As a consequence, such technique embeds in a branching algorithm ideas coming from other traditional computer science techniques such as dynamic programming and memorization, but keeping the space requirement polynomial. Chapter 5 provides an exact approach based on semidefinite programming and a matheuristic approach based on a quadratic solver for a fractional clustering combinatorial optimization problem, called Max-Mean Dispersion Problem. The matheuristic approach has the peculiarity of using a non-linear MIP solver. The proposed exact approach uses a general semidefinite programming relaxation and it is likely to be extended to other combinatorial problems with a fractional formulation. Chapter 6 proposes practical solution methods for a real world clustering problem arising in a smart city context. The solution algorithm is based on the solution of a Set Cover model via a commercial ILP solver. As a conclusion, the main contribution of this thesis is given by several approaches of practical or theoretical interest, for two classes of important combinatorial problems: clustering and scheduling. All the practical methods presented in the thesis are validated by extensive computational experiments, that compare the proposed methods with the ones available in the state of the art

Editorial: Language Acquisition in Diverse Linguistic, Social and Cognitive Circumstances

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Linguistic and Cognitive Skills in Sardinian–Italian Bilingual Children

We report the results of a study which tested receptive Italian grammatical competence and general cognitive abilities in bilingual Italian–Sardinian children and age-matched monolingual Italian children attending the first and second year of primary school in the Nuoro province of Sardinia, where Sardinian is still widely spoken. The results show that across age groups the performance of Sardinian–Italian bilingual children is in most cases indistinguishable from that of monolingual Italian children, in terms of both Italian language skills and general cognitive abilities. However, where there are differences, these emerge gradually over time and are mostly in favor of bilingual children

RPAS AND TLS TECNIQUES FOR ARCHAEOLOGICAL SURVEY: THE CASE STUDY OF THE ARCHAEOLOGICAL SITE OF ERACLEA MINOA (ITALY)

Digital documentation and 3D modelling of archaeological sites are important for understanding, definition and recognition of the values of the sites and of the archaeological finds. The most part of archaeological sites are outdoor location, but a cover to preserve the ruins protects often parts of the sites. The possibility to acquire data with different techniques and merge them by using a single reference system allows creating multi-parties models in which 3D representations of the individual objects can be inserted. The paper presents the results of a recent study carried out by Geomatics Laboratory of University of Palermo for the digital documentation and 3D modelling of Eraclea Minoa archaeological site. This site is located near Agrigento, in the south of Sicily (Italy) and is one of the most famous ancient Greek colonies of Sicily. The paper presents the results of the integration of different data source to survey the Eraclea Minoa archaeological site. The application of two highly versatile recording systems, the TLS (Terrestrial Laser Scanning) and the RPAS (Remotely Piloted Aircraft System), allowed the Eraclea Minoa site to be documented in high resolution and with high accuracy. The integration of the two techniques has demonstrated the possibility to obtain high quality and accurate 3D models in archaeological survey

Localization from inertial data and sporadic position measurements

A novel estimation strategy for inertial navigation in indoor/outdoor environments is proposed with a specific attention to the sporadic nature of the non-periodic measurements. After introducing the inertial navigation model, we introduce an observer providing an asymptotic estimate of the plant state. We use a hybrid dynamical systems representation for our results, in order to provide an effective, and elegant theoretical framework. The estimation error dynamics with the proposed observer shows a peculiar cascaded interconnection of three subsystems (allowing for intuitive gain tuning), with perturbations occurring either on the jump or on the flow dynamics (depending on the specific subsystem under consideration). For this structure, we show global exponential stability of the error dynamics. Hardware-in-the-loop results confirm the effectiveness of the proposed solution

A hybrid observer for localization from noisy inertial data and sporadic position measurements

We propose an asymptotic position and speed observer for inertial navigation in the case where the position measurements are sporadic and affected by noise. We cast the problem in a hybrid dynamics framework where the continuous motion is affected by unknown continuous-time disturbances and the sporadic position measurements are affected by discrete-time noise. We show that the peculiar hybrid cascaded structure describing the estimation error dynamics is globally finite-gain exponentially ISS with gains depending intuitively on our tuning parameters. Experimental results, as well as the comparison with an Extended Kalman Filter (EKF), confirm the effectiveness of the proposed solution with an execution time two orders of magnitude faster and with a simplified observer tuning because our bounds are an explicit function of the observer tuning knob