Almost 20 Years of Combinatorial Optimization for Railway Planning: from Lagrangian Relaxation to Column Generation

We summarize our experience in solving combinatorial optimization problems arising in railway planning, illustrating all of these problems as integer multicommodity flow ones and discussing the main features of the mathematical programming models that were successfully used in the 1990s and in recent years to solve them

Properties of some ILP formulations of a class of partitioning problems

AbstractWe discuss possible integer linear programming formulations of a class of partitioning problems, which includes vertex (and edge) coloring and bin packing, and present some basic properties of the associated linear programming relaxations, possibly improved by means of valid inequalities. In particular, we show that these relaxations are sometimes easily solved without resorting to an LP solver, and derive the worst-case performance of the associated bound on the optimal solution value. We also show which is the contribution of each inequality to this bound. Our analysis provides a general framework to unify and generalize some results previously presented in the literature, and should be taken into account whenever one considers the possibility of using the formulations addressed

A Fast Heuristic Algorithm for the Train Unit Assignment Problem

In this paper we study a railway optimization problem known as the Train Unit Assignment Problem. A train unit consists of a self-contained train with an engine and a set of wagons with passenger seats. Given a set of timetabled train trips, each with a required number of passenger seats, and a set of train units, each with a given number of available seats, the problem calls for the best assignment of the train units to the trips, possibly combining more than one train unit for a given trip, that fulfills the seat requests. We propose a heuristic algorithm based on the computation of a lower bound obtained by solving an Integer Linear Programming model that gives the optimal solution in a "peak period" of the day. The performance of the heuristic algorithm is computationally evaluated on real-world instances provided by a regional Italian Train Operator. The results are compared with those of existing methods from the literature, showing that the new method is able to obtain solutions of good quality in much shorter computing times

Robust Train Routing and Online Re-scheduling

Train Routing is a problem that arises in the early phase of the passenger railway planning process, usually several months before operating the trains. The main goal is to assign each train a stopping platform and the corresponding arrival/departure paths through a railway station. It is also called Train Platforming when referring to the platform assignment task. Railway stations often represent bottlenecks and train delays can easily disrupt the routing schedule. Thereby railway stations are responsible for a large part of the delay propagation in the whole network. In this research we present different models to compute robust routing schedules and we study their power in an online context together with different re-scheduling strategies. We also design a simulation framework and use it to evaluate and compare the effectiveness of the proposed robust models and re-scheduling algorithms using real-world data from Rete Ferroviaria Italiana, the main Italian Railway Infrastructure Manager

Recoverable Robustness for Railway Rolling Stock Planning

In this paper we explore the possibility of applying the notions of Recoverable Robustness and Price of Recoverability (introduced by [5]) to railway rolling stock planning, being interested in recoverability measures that can be computed in practice, thereby evaluating the robustness of rolling stock schedules. In order to lower bound the Price of Recoverability for any set of recovery algorithms, we consider an "optimal" recovery algorithm and propose a Benders decomposition approach to assess the Price of Recoverability for this "optimal" algorithm. We evaluate the approach on real-life rolling stock planning problems of NS, the main operator of passenger trains in the Netherlands. The preliminary results show that, thanks to Benders decomposition, our lower bound can be computed within relatively short time for our case study

Railway Rolling Stock Planning: Robustness Against Large Disruptions

In this paper we describe a two-stage optimization model for determining robust rolling stock circulations for passenger trains. Here robustness means that the rolling stock circulations can better deal with large disruptions of the railway system. The two-stage optimization model is formulated as a large mixed-integer linear programming (MILP) model. We first use Benders decomposition to determine optimal solutions for the LP-relaxation of this model. Then we use the cuts that were generated by the Benders decomposition for computing heuristic robust solutions for the two-stage optimization model. We call our method Benders heuristic. We evaluate our approach on the real-life rolling stock-planning problem of Netherlands Railways, the main operator of passenger trains in the Netherlands. The computational results show that, thanks to Benders decomposition, the LP-relaxation of the two-stage optimization problem can be solved in a short time for a representative number of disruption scenarios. In addition, they demonstrate that the robust rolling stoc

Endothelin-1 drives invadopodia and interaction with mesothelial cells through ILK

Summary Cancer cells use actin-based membrane protrusions, invadopodia, to degrade stroma and invade. In serous ovarian cancer (SOC), the endothelin A receptor (ETAR) drives invadopodia by a not fully explored coordinated function of β-arrestin1 (β-arr1). Here, we report that β-arr1 links the integrin-linked kinase (ILK)/βPIX complex to activate Rac3 GTPase, acting as a central node in the adhesion-based extracellular matrix (ECM) sensing and degradation. Downstream, Rac3 phosphorylates PAK1 and cofilin and promotes invadopodium-dependent ECM proteolysis and invasion. Furthermore, ETAR/ILK/Rac3 signaling supports the communication between cancer and mesothelial cells, favoring SOC cell adhesion and transmigration. In vivo, ambrisentan, an ETAR antagonist, inhibits the adhesion and spreading of tumor cells to intraperitoneal organs, and invadopodium marker expression. As prognostic factors, high EDNRA/ILK expression correlates with poor SOC clinical outcome. These findings provide a framework for the ET-1R/β-arr1 pathway as an integrator of ILK/Rac3-dependent adhesive and proteolytic signaling to invadopodia, favoring cancer/stroma interactions and metastatic behavior

single nucleotide polymorphism profiles of patients with acute renal rejection to personalize immunosuppressive therapy preliminary results from an on going italian study

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Development of a cognitive robotic system for simple surgical tasks

The introduction of robotic surgery within the operating rooms has significantly improved the quality of many surgical procedures. Recently, the research on medical robotic systems focused on increasing the level of autonomy in order to give them the possibility to carry out simple surgical actions autonomously. This paper reports on the development of technologies for introducing automation within the surgical workflow. The results have been obtained during the ongoing FP7 European funded project Intelligent Surgical Robotics (I-SUR). The main goal of the project is to demonstrate that autonomous robotic surgical systems can carry out simple surgical tasks effectively and without major intervention by surgeons. To fulfil this goal, we have developed innovative solutions (both in terms of technologies and algorithms) for the following aspects: fabrication of soft organ models starting from CT images, surgical planning and execution of movement of robot arms in contact with a deformable environment, designing a surgical interface minimizing the cognitive load of the surgeon supervising the actions, intra-operative sensing and reasoning to detect normal transitions and unexpected events. All these technologies have been integrated using a component-based software architecture to control a novel robot designed to perform the surgical actions under study. In this work we provide an overview of our system and report on preliminary results of the automatic execution of needle insertion for the cryoablation of kidney tumours