The graph edit distance (GED) is among the most widely used graph similarity measures in practice. It asks for a minimum cost edit path between two given labeled graphs G and H , where the edit path is defined as a sequence of operations (e.g., node and edge insertions, deletions or substitutions) that successively transform the graph G into H. In this work, we suggest a new ILP formulation (FORI) based on orienting the corresponding edge variables. Moreover, we suggest enhancing two state-of-the-art ILP formulations by incorporating additional inequalities. We theoretically compare the strength of the formulations with respect to their Linear Programming relaxations. The result is a hierarchy with (FORI) at the top. Our extensive evaluation on widely used benchmark sets shows that our improved formulations run significantly faster than the previous ones. These allow to solve to proven optimality all the reference instances from common databases, such as the IAM Graph Database, many of which were prohibitive with state-of-the-art methods. Moreover, we are able to compute the GED of a small pattern and a large graph such as CORA and PUBMED, having up to 19,717 nodes and 44,327 edges.

In this paper we analyze different Mixed Integer Linear Programming (MILP) models in order to produce 1D and 3D Spread-Out Bragg peaks (SOBP) for protons in water. Our techniques do not use much computational resources; in particular, all our experiments have been performed by a standard personal computer. As main result we give the proof of concept that the techniques that we use to create parameterized uniform SOBP can be fruitfully used in Treatment Planning Systems (TPS) for Intensity Modulated Proton Therapy (IMPT). As technical result we show, for the first time to our best knowledge, that there is a trade-off between the minimum number of energies (or layers) to be used to have a SOBP peak within a uniformity tolerance parameter Dtand the same parameter Dt. Minimizing the number of energies also has the advantage of reducing the delivery time using the facilities in operation nowadays.

Migrating from a legacy monolithic system to a microservice architecture is a complex and time-consuming process. Software engineers may strongly benefit from automated support to identify a high-cohesive and loose-coupled set of microservices with proper granularity. The automated approach proposed in this paper extracts microservices by using graph clustering and combinatorial optimization to maximize cohesion and minimize coupling. The approach performs static analysis of the code to obtain a graph representation of the monolithic system. Then, it uses graph clustering to detect high-cohesive communities of nodes using the Louvain community algorithm. In parallel, the tool clusters the domain entities (i.e., classes representing uniquely identifiable concepts in a system domain) within bounded contexts to identify the required service granularity. Finally, it uses combinatorial optimization to minimize the coupling, hence deriving the microservice architecture. The approach is fully implemented. We applied it over four different monolithic systems and found valuable results. We evaluated the identified architectures through cohesion and coupling metrics, along with a comparison with other state-of-the-art approaches based on features such as granularity level, number of produced services, and methods applied. The approach implementation and the experimental results are publicly available.

Abstract Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Machine Learning deep architecture known as Echo State Network, is compared with a completely different rolling horizon shallow Neural Network strategy, in which the lack of recurrent connections is compensated by a careful input selection. The comparison, carried out on three different real world datasets, reveals better predictive performance for the shallow approach. The latter appears also more robust and less demanding, reducing the inference time by a factor of 2.5 to 4.5 compared to Echo State Networks.

Proton therapy is a cancer therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Since there is also a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesman Problem (TSP) solver, Concorde, and an efficient Vehicle Routing Problem (VRP) heuristic, VROOM.

In the recent years InSAR (Synthetic Aperture Radar Interferometry) technique showed its wide potentialities to detect the surface displacement field due to an earthquake. Of great interest and usefulness in this context is the solution of the inverse problem that means to recover the source parameters from the knowledge of InSAR surface displacement field. In this work a novel approach for the solution of such a problem is presented.

Abstract An important problem arising in the management of logistic networks is the following: given a set of activities to be performed, each requiring a set of resources, select the optimal set of resources compatible with the system capacity constraints. This problem is called Batch Selection Problem (BSP) from traditional applications to flexible manufacturing. BSP is known to be NP‐hard, and is also considered a difficult integer programming problem. In fact, polyhedral approaches to BSP suffer from the poor quality of the bound provided by the linear relaxation, even after strengthening. We devise an implicit enumeration scheme that attempts to overcome this difficulty. It is based on a formulation of BSP, which is equivalent to a stable set problem with one side constraint. This allows to exploit the upper bound, not only for pruning subproblems, but also for controlling the number and the quality of the subproblems generated. We show the effectiveness of the approach by an extensive computational experience on real‐sized randomly generated instances. © 2004 Wiley Periodicals, Inc. NETWORKS, Vol. 44(2), 151–159 2004