Schedulazione delle rotte marittime nel settore ortofrutticolo. Modelli e Metodi

Il trasporto marittimo è una delle modalità più utilizzate soprattutto per la movimentazione di grandi volumi di prodotti tra i continenti in quanto è a basso costo, sicuro e meno inquinante rispetto ad altri mezzi di movimentazione. Ai giorni nostri è responsabile di circa l’80% del commercio globale (in volume di carichi trasportati). Il settore del trasporto marittimo ha avuto una lunga tradizione di pianificazione manuale effettuata da progettisti esperti.
 L’obiettivo principale di questa trattazione è stato quello di implementare un modello matematico lineare (MILP, Mixed-Integer Linear Programming Model) per l’ottimizzazione delle rotte marittime nell’ambito del mercato orto-frutticolo che si sviluppa nel bacino del Mediterraneo (problema di Ship-Scheduling). Il modello fornito in questa trattazione è un valido strumento di supporto alle decisioni che può utilizzare uno spedizioniere nell’ambito della pianificazione delle rotte marittime della flotta di navi in suo possesso. Consente di determinare l’insieme delle rotte ottimali che devono essere svolte da un insieme di vettori al fine di massimizzare il profitto complessivo dello spedizioniere, generato nell’arco di tempo considerato. Inoltre, permette di ottenere, per ogni nave considerata, la ripartizione ottimale della merce (carico ottimale).

Exact Combinatorial Optimization with Graph Convolutional Neural Networks

Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose to learn a variable selection policy for branch-and-bound in mixed-integer linear programming, by imitation learning on a diversified variant of the strong branching expert rule. We encode states as bipartite graphs and parameterize the policy as a graph convolutional neural network. Experiments on a series of synthetic problems demonstrate that our approach produces policies that can improve upon expert-designed branching rules on large problems, and generalize to instances significantly larger than seen during training.

Computing primal solutions with exact arithmetics in SCIP.

The research for exact solutions of mixed integer problems is an active topic in the scientific community. State-of-the-art MIP solvers exploit a floating- point numerical representation, therefore introducing small approximations. Although such MIP solvers yield reliable results for the majority of problems, there are cases in which a higher accuracy is required. Indeed, it is known that for some applications floating-point solvers provide falsely feasible solutions, i.e. solutions marked as feasible because of approximations that would not pass a check with exact arithmetic and cannot be practically implemented. The framework of the current dissertation is SCIP, a mixed integer programs solver mainly developed at Zuse Institute Berlin. In the same site we considered a new approach for exactly solving MIPs. Specifically, we developed a constraint handler to plug into SCIP, with the aim to analyze the accuracy of provided floating-point solutions and compute exact primal solutions starting from floating-point ones. We conducted a few computational experiments to test the exact primal constraint handler through the adoption of two main settings. Analysis mode allowed to collect statistics about current SCIP solutions' reliability. Our results confirm that floating-point solutions are accurate enough with respect to many instances. However, our analysis highlighted the presence of numerical errors of variable entity. By using the enforce mode, our constraint handler is able to suggest exact solutions starting from the integer part of a floating-point solution. With the latter setting, results show a general improvement of the quality of provided final solutions, without a significant loss of performances.

Solvent regeneration of activated carbon mixed-matrix membranes for the removal of micropollutants from wastewater

The constantly increasing demand of clean water has become challenging to deal with over the past years, water being an ever more precious resource. In recent times, the existing wastewater treatments had to be integrated with new steps, due to the detection of so-called organic micropollutants (OMPs). These compounds have been shown to adversely affect the environment and possibly human health, even when found in very low concentrations. In order to remove OMPs from wastewater, one possible technique is a hybrid process combining filtration and adsorption. In this work, polyethersulfone multi-channel mixed-matrix membranes with embedded powdered activated carbon (PAC) were tested to investigate the membrane’s adsorption and desorption performance. Micropollutants retention was analyzed using the pharmaceutical compounds diclofenac (DCF), paracetamol (PARA) and carbamazepine (CBZ) in filtration mode, combining the PAC adsorption process with the membrane’s ultrafiltration. Desorption performance was studied through solvent regeneration, using seven different solvents: pure water, pure ethanol, mixture of ethanol and water in different concentration, sodium hydroxide and a mixture of ethanol and sodium hydroxide. Regeneration experiments were carried out in forward-flushing. At first regeneration efficiency was investigated using a single-solute solution (diclofenac in water). The mixture Ethanol/Water (50:50) was found to be the most efficient with long-term retention of 59% after one desorption cycle. It was, therefore, later tested on a membrane previously loaded with a multi-solute solution. Three desorption cycles were performed after which, retention (after 30 min) reached values of 87% for PARA and 72% for CBZ and 55% for DCF, which indicates decent regenerability. A morphological analysis on the membranes confirmed that, in any case, the regeneration cycles did not affect either the membranes’ structure, or the content and distribution of PAC in the matrix.

Asymmetries in Adversarial Settings

Even without formal guarantees of their effectiveness, adversarial attacks against Machine Learning models frequently fool new defenses. We identify six key asymmetries that contribute to this phenomenon and formulate four guidelines to build future-proof defenses by preventing such asymmetries. We also prove that attacking a classifier is NP-complete, while defending from such attacks is Sigma_2^P-complete. We then introduce Counter-Attack (CA), an asymmetry-free metadefense that determines whether a model is robust on a given input by estimating its distance from the decision boundary. Under specific assumptions CA can provide theoretical detection guarantees. Additionally, we prove that while CA is NP-complete, fooling CA is Sigma_2^P-complete. Even when using heuristic relaxations, we show that our method can reliably identify non-robust points. As part of our experimental evaluation, we introduce UG100, a new dataset obtained by applying a provably optimal attack to six limited-scale networks (three for MNIST and three for CIFAR10), each trained in three different manners.

Design and management of a supply chain: a joint location-inventory problem under uncertainty (Part A)

In this paper, a joint location-inventory model is proposed that simultaneously optimises strategic supply chain design decisions such as facility location and customer allocation to facilities, and tactical-operational inventory management and production scheduling decisions. All this is analysed in a context of demand uncertainty and supply uncertainty. While demand uncertainty stems from potential fluctuations in customer demands over time, supply-side uncertainty is associated with the risk of “disruption” to which facilities may be subject. The latter is caused by external factors such as natural disasters, strikes, changes of ownership and information technology security incidents. The proposed model is formulated as a non-linear mixed integer programming problem to minimise the expected total cost, which includes four basic cost items: the fixed cost of locating facilities at candidate sites, the cost of transport from facilities to customers, the cost of working inventory, and the cost of safety stock. Next, since the optimisation problem is very complex and the number of evaluable instances is very low, a "matheuristic" solution is presented. This approach has a twofold objective: on the one hand, it considers a larger number of facilities and customers within the network in order to reproduce a supply chain configuration that more closely reflects a real-world context; on the other hand, it serves to generate a starting solution and perform a series of iterations to try to improve it. Thanks to this algorithm, it was possible to obtain a solution characterised by a lower total system cost than that observed for the initial solution. The study concludes with some reflections and the description of possible future insights.

Fleet Management System for Search and Rescue Operations

Over one million people lost their lives in the last twenty years from natural disasters like wildfires, earthquakes and man-made disasters. In such scenarios the usage of a fleet of robots aims at the parallelization of the workload and thus increasing speed and capabilities to complete time sensitive missions. This work focuses on the development of a dynamic fleet management system, which consists in the management of multiple agents cooperating in order to accomplish tasks. We presented a Mixed Integer Programming problem for the management and planning of mission’s tasks. The problem was solved using both an exact and a heuristic approach. The latter is based on the idea of solving iteratively smaller instances of the complete problem. Alongside, a fast and efficient algorithm for estimation of travel times between tasks is proposed. Experimental results demonstrate that the proposed heuristic approach is able to generate quality solutions, within specific time limits, compared to the exact one.

Cryogenic optical readout for a liquid argon neutrino imaging detector

The GRAIN detector is part of the SAND Near Detector of the DUNE neutrino experiment. A new imaging technique involving the collection of the scintillation light will be used in order to reconstruct images of particle tracks in the GRAIN detector. Silicon photomultiplier (SiPM) matrices will be used as photosensors for collecting the scintillation light emitted at 127 nm by liquid argon. The readout of SiPM matrices inside the liquid argon requires the use of a multi-channel mixed-signal ASIC, while the back-end electronics will be implemented in FPGAs outside the cryogenic environment. The ALCOR (A Low-power Circuit for Optical sensor Readout) ASIC, developed by Torino division of INFN, is under study, since it is optimized to readout SiPMs at cryogenic temperatures. I took part in the realization of a demonstrator of the imaging system, which consists of a SiPM matrix connected to a custom circuit board, on which an ALCOR ASIC is mounted. The board communicates with an FPGA. The first step of the present project that I have accomplished was the development of an emulator for the ALCOR ASIC. This emulator allowed me to verify the correct functioning of the initial firmware before the real ASIC itself was available. I programmed the emulator using VHDL and I also developed test benches in order to test its correct working. Furthermore, I developed portions of the DAQ software, which I used for the acquisition of data and the slow control of the ASICs. In addition, I made some parts of the DAQ firmware for the FPGAs. Finally, I tested the complete SiPMs readout system at both room and cryogenic temperature in order to ensure its full functionality.

New integer programming models for balanced multi-country kep

Le persone che soffrono di insufficienza renale terminale hanno due possibili trattamenti da affrontare: la dialisi oppure il trapianto di organo. Nel caso volessero seguire la seconda strada, oltre che essere inseriti nella lista d'attesa dei donatori deceduti, possono trovare una persona, come il coniuge, un parente o un amico, disposta a donare il proprio rene. Tuttavia, non sempre il trapianto è fattibile: donatore e ricevente possono, infatti, presentare delle incompatibilità a livello di gruppo sanguigno o di tessuto organico. Come risposta a questo tipo di problema nasce il KEP (Kidney Exchange Program), un programma, ampiamente avviato in diverse realtà europee e mondiali, che si occupa di raggruppare in un unico insieme le coppie donatore/ricevente in questa stessa situazione al fine di operare e massimizzare scambi incrociati di reni fra coppie compatibili. Questa tesi approffondisce tale questione andando a valutare la possibilità di unire in un unico insieme internazionale coppie donatore/ricevente provenienti da più paesi. Lo scopo, naturalmente, è quello di poter ottenere un numero sempre maggiore di trapianti effettuati. Lo studio affronta dal punto di vista matematico problematiche legate a tale collaborazione: i paesi che eventualmente accettassero di partecipare a un simile programma, infatti, devono avere la garanzia non solo di ricavarne un vantaggio, ma anche che tale vantaggio sia equamente distribuito fra tutti i paesi partecipanti.

WINDOWS 10 IoT SU RASPBERRY PI 2: MULTI-PARADIGM PROGRAMMING TRA JAVA E C#

Questa tesi ha come obiettivo la sperimentazione del nuovo sistema operativo Windows 10 IoT Core su tecnologia Raspberry Pi 2, verificandone la compatibilita con alcuni sensori in commercio. Tale studio viene poi applicato in un contesto di Home Intelligence al fine di creare un agente per la gestione di luci LED, in prospettiva della sua integrazione nel sistema prototipale Home Manager.

Anaerobic oxidation of ethanol over spinel mixed oxides: the first step in the steam-iron process for H2 production

The future hydrogen demand is expected to increase, both in existing industries (including upgrading of fossil fuels or ammonia production) and in new technologies, like fuel cells. Nowadays, hydrogen is obtained predominantly by steam reforming of methane, but it is well known that hydrocarbon based routes result in environmental problems and besides the market is dependent on the availability of this finite resource which is suffering of rapid depletion. Therefore, alternative processes using renewable sources like wind, solar energy and biomass, are now being considered for the production of hydrogen. One of those alternative methods is the so-called “steam-iron process” which consists in the reduction of a metal-oxide by hydrogen-containing feedstock, like ethanol for instance, and then the reduced material is reoxidized with water to produce “clean” hydrogen (water splitting). This kind of thermochemical cycles have been studied before but currently some important facts like the development of more active catalysts, the flexibility of the feedstock (including renewable bio-alcohols) and the fact that the purification of hydrogen could be avoided, have significantly increased the interest for this research topic. With the aim of increasing the understanding of the reactions that govern the steam-iron route to produce hydrogen, it is necessary to go into the molecular level. Spectroscopic methods are an important tool to extract information that could help in the development of more efficient materials and processes. In this research, ethanol was chosen as a reducing fuel and the main goal was to study its interaction with different catalysts having similar structure (spinels), to make a correlation with the composition and the mechanism of the anaerobic oxidation of the ethanol which is the first step of the steam-iron cycle. To accomplish this, diffuse reflectance spectroscopy (DRIFTS) was used to study the surface composition of the catalysts during the adsorption of ethanol and its transformation during the temperature program. Furthermore, mass spectrometry was used to monitor the desorbed products. The set of studied materials include Cu, Co and Ni ferrites which were also characterized by means of X-ray diffraction, surface area measurements, Raman spectroscopy, and temperature programmed reduction.

Reuse Mechanisms and Concurrency: From Actors to Agent-Oriented Programming

La presente tesi è dedicata al riuso nel software. Eccettuata un'introduzione organica al tema, l'analisi è a livello dei meccanismi offerti dai linguaggi di programmazione e delle tecniche di sviluppo, con speciale attenzione rivolta al tema della concorrenza. Il primo capitolo fornisce un quadro generale nel quale il riuso del software è descritto, assieme alle ragioni che ne determinano l'importanza e ai punti cruciali relativi alla sua attuazione. Si individuano diversi livelli di riuso sulla base dell'astrazione e degli artefatti in gioco, e si sottolinea come i linguaggi contribuiscano alla riusabilità e alla realizzazione del riuso. In seguito, viene esplorato, con esempi di codice, il supporto al riuso da parte del paradigma ad oggetti, in termini di incapsulamento, ereditarietà, polimorfismo, composizione. La trattazione prosegue analizzando differenti feature – tipizzazione, interfacce, mixin, generics – offerte da vari linguaggi di programmazione, mostrando come esse intervengano sulla riusabilità dei componenti software. A chiudere il capitolo, qualche parola contestualizzata sull'inversione di controllo, la programmazione orientata agli aspetti, e il meccanismo della delega. Il secondo capitolo abbraccia il tema della concorrenza. Dopo aver introdotto l'argomento, vengono approfonditi alcuni significativi modelli di concorrenza: programmazione multi-threaded, task nel linguaggio Ada, SCOOP, modello ad Attori. Essi vengono descritti negli elementi fondamentali e ne vengono evidenziati gli aspetti cruciali in termini di contributo al riuso, con esempi di codice. Relativamente al modello ad Attori, viene presentata la sua implementazione in Scala/Akka come caso studio. Infine, viene esaminato il problema dell'inheritance anomaly, sulla base di esempi e delle tre classi principali di anomalia, e si analizza la suscettibilità del supporto di concorrenza di Scala/Akka a riscontrare tali problemi. Inoltre, in questo capitolo si nota come alcuni aspetti relativi al binomio riuso/concorrenza, tra cui il significato profondo dello stesso, non siano ancora stati adeguatamente affrontati dalla comunità informatica. Il terzo e ultimo capitolo esordisce con una panoramica dell'agent-oriented programming, prendendo il linguaggio simpAL come riferimento. In seguito, si prova ad estendere al caso degli agenti la nozione di riuso approfondita nei capitoli precedenti.

Groupme: city data and multi-preference activity allocation

Classic group recommender systems focus on providing suggestions for a fixed group of people. Our work tries to give an inside look at design- ing a new recommender system that is capable of making suggestions for a sequence of activities, dividing people in subgroups, in order to boost over- all group satisfaction. However, this idea increases problem complexity in more dimensions and creates great challenge to the algorithm’s performance. To understand the e↵ectiveness, due to the enhanced complexity and pre- cise problem solving, we implemented an experimental system from data collected from a variety of web services concerning the city of Paris. The sys- tem recommends activities to a group of users from two di↵erent approaches: Local Search and Constraint Programming. The general results show that the number of subgroups can significantly influence the Constraint Program- ming Approaches’s computational time and e�cacy. Generally, Local Search can find results much quicker than Constraint Programming. Over a lengthy period of time, Local Search performs better than Constraint Programming, with similar final results.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

Polyhydroxyalkanoates production from salted food waste using mixed microbial cultures.

Plastic is an essential asset for the modern lifestyle, given its superiority as a material from the points of view of cost, processability and functional properties. However, plastic-related environmental pollution has become nowadays a very significant problem that can no longer be overlooked. For this reason, in recent decades, the research for new materials that could replace fossil fuel-based plastics has been focused on biopolymers with similar physicochemical properties to fossil fuel-based plastics, such as Polyhydroxyalkanoates (PHA). PHAs are a family of biodegradable polyesters synthesized by many microorganisms as carbon and energy reserves. PHA appears as a good candidate to substitute conventional petroleum-based plastics since it has similar properties, but with the advantage of being biobased and biodegradable, and has a wide range of applications (e.g., packaging). However, the PHA production cost is almost four times higher (€5/kg) than conventional plastic manufacturing. The PHA production by mixed microbial cultures (MMC) allows to reduce production costs as it does not require aseptic conditions and it enables the use of inexpensive by-products or waste streams as these cultures are more amenable to deal with complex feedstocks. Saline wastewaters (WWs), generated by several industries such as seafood, leather and dairy, are often rich in organic compounds and, due to a strong salt inhibition, the biological treatments are inefficient, and their disposal is expensive. These saline WWs are a potential feedstock for PHA production, as they are an inexpensive raw material. Moreover, saline WWs could allow the utilization of seawater in the process as dilution and cleaning agent, further decreasing the operational costs and the environmental burden of the process. The main goal of the current project is to assess and optimize the PHA production from a mixture of food waste and brine wastewater from the fishery industry by MMC.