A Parallel Hamiltonian Eigensolver for Passivity Characterization and Enforcement of Large Interconnect Macromodels

Best Paper Award Nominatio

The Cagliari Airport impact on Sardinia tourism: a Logit-based analysis

In the field of air transportation management, traditionally, airlines have been the main actors in the process for deciding which new flights open in a given airport, while airports acted only as the managers of the operations. The changes in the market due to the introduction of low cost companies, with consequent reduction of the airports' fares, as well as the increment of the density of regional airports in several European countries are modifying the mutual roles of airlines and airports. The final decision on new flight to be opened, in fact, is nowadays the result of a negotiation between airlines and airports. The airports must prove the sustainability on the new routes and forecast the economic impact on their catchment area. This paper contributes to advance the current state-of-the-art along two axes. From the pure transportation literature point of view, we introduce a Logit model able to predict the passengers flow in an airport when the management introduces a change in the flight schedule. The model is also able to predict the impact of this change on the airports in the surrounding areas. The second contribution is a case study on the tourist market of the Sardinia region, where we show how to use the results of the model to deduce the economic impact of the decisions of the management of the Cagliari airport on its catchment area in terms of tourists and economic growt

Analysis of mechanisms underlying EDS1-PAD4 cooperation in Arabidopsis immune signaling

Plants have evolved a multilayered immune system to counter pathogen attacks. EDS1 (Enhanced Disease Susceptibility 1) and PAD4 (Phytoalexin Deficient 4) are two plant- specific lipase-like proteins that function as essential regulators of plant innate immunity. They are crucial for basal defence that restricts growth of virulent pathogens and for race-specific resistance to avirulent pathogens triggered by TIR (Toll-Interleukin 1) type NBS-LRR (Nucleotide Binding Site � Leucine Rich Repeats) immune receptors. Moreover, EDS1 and PAD4 generate and perceive (a) signal(s) needed to induce systemic immunity. These regulators stimulate accumulation of the phenolic defence signaling molecule salicylic acid (SA) and SA, in turn, induces their expression creating a positive feedback loop in defence potentiation. EDS1 and PAD4 transcript and correspondent protein levels increase upon pathogen challenge. However, earlier changes in expression of a set of distinct genes which are EDS1- and PAD4-dependent imply the activation of pre-existing EDS1/PAD4 complexes through post-translational mechanism(s). In this work I investigated the relative importance of transcriptional regulation and post-transcriptional processes for EDS1 and PAD4 protein functions. I characterized Arabidopsis thaliana transgenic lines overexpressing either EDS1, PAD4 or both. Only lines cooverexpressing EDS1 and PAD exhibited growth retardation associated with constitutive activation of the SA pathway and increased resistance to virulent pathogens resulting from a faster SA pathway activation. These lines exhibit also increased tolerance to chemically induced oxidative stress consistent with a known role of EDS1 and PAD4 in processing reactive oxygen species (ROS) - derived signals. The insufficiency of EDS1-PAD4 cooverexpression to fully recapitulate defence activation implies the existence of post-translational mechanisms of regulation. The existence of regulatory post-translational modifications of the EDS1 protein was investigated and lines expressing constitutively or conditionally activated functional epitope-tagged EDS1 were generated. The data presented here demonstrate that EDS1 and PAD4 operate as a signaling unit. The basis of the observed dramatic biotic and abiotic stress phenotypes will be further investigated as it should provide important insight into EDS1 and PAD4 functions

High-Performance Passive Macromodeling Algorithms for Parallel Computing Platforms

This paper presents a comprehensive strategy for fast generation of passive macromodels of linear devices and interconnects on parallel computing hardware. Starting from a raw characterization of the structure in terms of frequency-domain tabulated scattering responses, we perform a rational curve fitting and a postprocessing passivity enforcement. Both algorithms are parallelized and cast in a form that is suitable for deployment on shared-memory multicore platforms. Particular emphasis is placed on the passivity characterization step, which is performed using two complementary strategies. The first uses an iterative restarted and deflated rational Arnoldi process to extract the imaginary Hamiltonian eigenvalues associated with the model. The second is based on an accuracy-controlled adaptive sampling. Various parallelization strategies are discussed for both schemes, with particular care on load balancing between different computing threads and memory occupation. The resulting parallel macromodeling flow is demonstrated on a number of medium- and large-scale structures, showing good scalability up to 16 computational core

Stochastic programming for City Logistics: new models and methods

The need for mobility that emerged in the last decades led to an impressive increase in the number of vehicles as well as to a saturation of transportation infrastructures. Consequently, traffic congestion, accidents, transportation delays, and polluting emissions are some of the most recurrent concerns transportation and city managers have to deal with. However, just building new infrastructures might be not sustainable because of their cost, the land usage, which usually lacks in metropolitan regions, and their negative impact on the environment. Therefore, a different way of improving the performance of transportation systems while enhancing travel safety has to be found in order to make people and good transportation operations more efficient and support their key role in the economic development of either a city or a whole country. The concept of City Logistics (CL) is being developed to answer to this need. Indeed, CL focus on reducing the number of vehicles operating in the city, controlling their dimension and characteristics. CL solutions do not only improve the transportation system but the whole logistics system within an urban area, trying to integrate interests of the several. This global view challenges researchers to develop planning models, methods and decision support tools for the optimization of the structures and the activities of the transportation system. In particular, this leads researchers to the definition of strategic and tactical problems belonging to well-known problem classes, including network design problem, vehicle routing problem (VRP), traveling salesman problem (TSP), bin packing problem (BPP), which typically act as sub-problems of the overall CL system optimization. When long planning horizons are involved, these problems become stochastic and, thus, must explicitly take into account the different sources of uncertainty that can affect the transportation system. Due to these reasons and the large-scale of CL systems, the optimization problems arising in the urban context are very challenging. Their solution requires investigations in mathematical and combinatorial optimization methods as well as the implementation of efficient exact and heuristic algorithms. However, contributions answering these challenges are still limited number. This work contributes in filling this gap in the literature in terms of both modeling framework for new planning problems in CL context and developing new and effective heuristic solving methods for the two-stage formulation of these problems. Three stochastic problems are proposed in the context of CL: the stochastic variable cost and size bin packing problem (SVCSBPP), the multi-handler knapsack problem under uncertainty (MHKPu) and the multi-path traveling salesman problem with stochastic travel times (mpTSPs). The SVCSBPP arises in supply-chain management, in which companies outsource the logistics activities to a third-party logistic firm (3PL). The procurement of sufficient capacity, expressed in terms of vehicles, containers or space in a warehouse for varying periods of time to satisfy the demand plays a crucial role. The SVCSBPP focuses on the relation between a company and its logistics capacity provider and the tactical-planning problem of determining the quantity of capacity units to secure for the next period of activity. The SVCSBPP is the first attempt to introduce a stochastic variant of the variable cost and size bin packing problem (VCSBPP) considering not only the uncertainty on the demand to deliver, but also on the renting cost of the different bins and their availability. A large number of real-life situations can be satisfactorily modeled as a MHKPu, in particular in the last mile delivery. Last mile delivery may involve different sequences of consolidation operations, each handled by different workers with different skill levels and reliability. The improper management of consolidation operations can cause delay in the operations reducing the overall profit of the deliveries. Thus, given a set of potential logistics handlers and a set of items to deliver, characterized by volume and random profit, the MHKPu consists in finding a subset of items which maximizes the expected total profit. The profit is given by the sum of a deterministic profit and a stochastic profit oscillation, with unknown probability distribution, due to the random handling costs of the handlers.The mpTSPs arises mainly in City Logistics applications. Cities offer several services, such as garbage collection, periodic delivery of goods in urban grocery distribution and bike sharing services. These services require the planning of fixed and periodic tours that will be used from one to several weeks. However, the enlarged time horizon as well as strong dynamic changes in travel times due to traffic congestion and other nuisances typical of the urban transportation induce the presence of multiple paths with stochastic travel times. Given a graph characterized by a set of nodes connected by arcs, mpTSPs considers that, for every pair of nodes, multiple paths between the two nodes are present. Each path is characterized by a random travel time. Similarly to the standard TSP, the aim of the problem is to define the Hamiltonian cycle minimizing the expected total cost. These planning problems have been formulated as two-stage integer stochastic programs with recourse. Discretization methods are usually applied to approximate the probability distribution of the random parameters. The resulting approximated program becomes a deterministic linear program with integer decision variables of generally very large dimensions, beyond the reach of exact methods. Therefore, heuristics are required. For the MHKPu, we apply the extreme value theory and derive a deterministic approximation, while for the SVCSBPP and the mpTSPs we introduce effective and accurate heuristics based on the progressive hedging (PH) ideas. The PH mitigates the computational difficulty associated with large problem instances by decomposing the stochastic program by scenario. When effective heuristic techniques exist for solving individual scenario, that is the case of the SVCSBPP and the mpTSPs, the PH further reduces the computational effort of solving scenario subproblems by means of a commercial solver. In particular, we propose a series of specific strategies to accelerate the search and efficiently address the symmetry of solutions, including an aggregated consensual solution, heuristic penalty adjustments, and a bundle fixing technique. Yet, although solution methods become more powerful, combinatorial problems in the CL context are very large and difficult to solve. Thus, in order to significantly enhance the computational efficiency, these heuristics implement parallel schemes. With the aim to make a complete analysis of the problems proposed, we perform extensive numerical experiments on a large set of instances of various dimensions, including realistic setting derived by real applications in the urban area, and combinations of different levels of variability and correlations in the stochastic parameters. The campaign includes the assessment of the efficiency of the meta-heuristic, the evaluation of the interest to explicitly consider uncertainty, an analysis of the impact of problem characteristics, the structure of solutions, as well as an evaluation of the robustness of the solutions when used as decision tool. The numerical analysis indicates that the stochastic programs have significant effects in terms of both the economic impact (e.g. cost reduction) and the operations management (e.g. prediction of the capacity needed by the firm). The proposed methodologies outperform the use of commercial solvers, also when small-size instances are considered. In fact, they find good solutions in manageable computing time. This makes these heuristics a strategic tool that can be incorporated in larger decision support systems for CL

Decision support system for collaborative freight transportation management: a tool for mixing traditional and green logistics.

In recent years, freight transportation emerged as a key factor in the development and dynamicity of countries, although it has a considerably impact on urban areas, due to the environmental issues. In this context, several stakeholders have implemented City Logistics solutions in order to make transportation more sustainable and efficient. This paper proposes a case study concerning the collaborative transportation system involving traditional and green couriers, in the city of Turin. This freight pooling is supported by a decision support system that combines the ERP “Odoo” with an algorithm for the optimization planning of routes. This decision support system is described in the second section and finally, some results obtained from its application are discussed

Performance Analysis of a Producer Gas-fuelled Heavy-duty SI Engine at Full-load Operation

Abstract Biomass gasification converts a solid fuel into a gaseous mixture (syngas or producer gas) which can be burnt in reciprocating internal combustion engines (ICEs) to produce electrical power. A wide variety of bio-residues can be processed to obtain syngas, making biomass gasification a very interesting way to exploit the energy content of industrial by-products and agricultural wastes. This paper focuses on the operation of a spark ignition (SI) ICE burning low-heating value gas produced in a fixed-bed downdraft gasifier. The biomass gasification power plant has collected more than nine months of operation till now without need of any extraordinary maintenance of the engine. Engine performance is calculated using experimental data acquired at different air-to-fuel ratios and spark timings, and then compared with results of test performed by other authors. The work is mainly aimed at analysing the effect of PG fuelling on brake power, efficiency and emissions of heavy-duty engines