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.

Determinazione gascromatografica di composti organici volatili e semi-volatili (VOCs e SVOCs) in poliolefine ed effetti delle condizioni di lavorazione

A relevant problem of polyolefins processing is the presence of volatile and semi-volatile compounds (VOCs and SVOCs) such as linear chains alkanes found out in final products. These VOCs can be detected by customers from the unpleasant smelt and can be an environmental issue, at the same time they can cause negative side effects during process. Since no previously standardized analytical techniques for polymeric matrix are available in bibliography, we have implemented different VOCs extraction methods and gaschromatographic analysis for quali-quantitative studies of such compounds. In literature different procedures can be found including microwave extraction (MAE) and thermo desorption (TDS) used with different purposes. TDS coupled with GC-MS are necessary for the identification of different compounds in the polymer matrix. Although the quantitative determination is complex, the results obtained from TDS/GC-MS show that by-products are mainly linear chains oligomers with even number of carbon in a C8-C22 range (for HDPE). In order to quantify these linear alkanes by-products, a more accurate GC-FID determination with internal standard has been run on MAE extracts. Regardless the type of extruder used, it is difficult to distinguish the effect of the various processes, which in any case entails having a lower-boiling substance content, lower than the corresponding virgin polymer. The two HDPEs studied can be distinguished on the basis of the quantity of analytes found, therefore the production process is mainly responsible for the amount of VOCs and SVOCs observed. The extruder technology used by Sacmi SC allows to obtain a significant reduction in VOCs compared to the conventional screw system. Thus, the result is significantly important as a lower quantity of volatile substances certainly leads to a lower migration of such materials, especially when used for food packaging.

A comprehensive study on robotic manipulation of deformable linear objects

There are many deformable objects such as papers, clothes, ropes in a person’s living space. To have a robot working in automating the daily tasks it is important that the robot works with these deformable objects. Manipulation of deformable objects is a challenging task for robots because these objects have an infinite-dimensional configuration space and are expensive to model, making real-time monitoring, planning and control difficult. It forms a particularly important field of robotics with relevant applications in different sectors such as medicine, food handling, manufacturing, and household chores. In this report, there is a clear review of the approaches used and are currently in use along with future developments to achieve this task. My research is more focused on the last 10 years, where I have systematically reviewed many articles to have a clear understanding of developments in this field. The main contribution is to show the whole landscape of this concept and provide a broad view of how it has evolved. I also explained my research methodology by following my analysis from the past to the present along with my thoughts for the future.

Energy consumption of parallel algorithms for solving linear systems on HPC architecture

Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to the correspondent demand of larger simulations requiring more complicated tasks and higher accuracy. However, as side effects of the Dennard’s scaling approaching its ultimate power limit, the efficiency of software plays also an important role in increasing the overall performance of a computation. Tools to measure application performance in these increasingly complex environments provide insights into the intricate ways in which software and hardware interact. The monitoring of the power consumption in order to save energy is possible through processors interfaces like Intel Running Average Power Limit RAPL. Given the low level of these interfaces, they are often paired with an application-level tool like Performance Application Programming Interface PAPI. Since several problems in many heterogeneous fields can be represented as a complex linear system, an optimized and scalable linear system solver algorithm can decrease significantly the time spent to compute its resolution. One of the most widely used algorithms deployed for the resolution of large simulation is the Gaussian Elimination, which has its most popular implementation for HPC systems in the Scalable Linear Algebra PACKage ScaLAPACK library. However, another relevant algorithm, which is increasing in popularity in the academic field, is the Inhibition Method. This thesis compares the energy consumption of the Inhibition Method and Gaussian Elimination from ScaLAPACK to profile their execution during the resolution of linear systems above the HPC architecture offered by CINECA. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, that will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.