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

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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

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

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

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

A DSS for business decisions in air transportation: a case study

The socio-economic development leads people to a great mobility. Thus the flights identification and management is becoming a key factor for the economic growth of the areas nearby the airports. The airport management is constantly looking for methods to improve its performance, both in terms of profitability and quality of service and the proper planning of passenger flows. To address these issues, scientific research provides methods and tools for decision support at all planning levels (i.e., strategic, tactical, operational, real time). In recent literature, it is now widely recognized that the hybridization of simulation and optimization systems is a very reliable technique for such decisions. This work intends to present an efficient Decision Support System framework based on the hybridization of a discrete event simulator and a Logit model. In order to show the effectiveness of the framework, we show the results of a real case study in North Ital

Waste collection in urban areas: a case study

The Optimization for Networked Data in Environmental Urban Waste Collection (ONDE-UWC) project is, to our knowledge, the first attempt to apply the Internet of Things (IoT) paradigm to the waste collection field. Sensors installed on dumpsters and garbage trucks share data, such as the number of user accesses and weight measures. In this study, we schedule the weekly waste collection activities of all the types of waste without imposing periodic routes. An important characteristic of this project considers the network presence of heterogeneous stakeholders with different background knowledge. In this context, we apply the GUEST OR methodology, highlighting how it can support the decision-making process in order to reduce this gap. This will bring positive consequences in terms of reduced time for solution implementation, followed by operational efficiency and economical savings

Bin Packing Problem with uncertainty on item availability: an application to Capacity Planning in Logistics

Most modern companies are part of international economic networks, where goods are produced under different strategies, then transported over long distances and stored for variable periods of time at different locations along the considered network. These activities are often performed by first consolidating goods in appropriate bins, which are then stored at warehouses and shipped using multiple vehicles through various transportation modes. Companies thus face the problem of planning for sufficient capacity, e.g., negotiating it with third party logistic firms (3PLs) that specify both the capacity to be used and the logistical services to be performed. Given the time lag that usually exists between the capacity-planning decisions and the operational decisions that define how the planned capacity is used, the common assumption that all information concerning the parameters of the model is known is unlikely to be observed. We therefore propose a new stochastic problem, named the Variable Cost and Size Bin Packing Problem with Stochastic Items. The problem considers a company making a tactical capacity plan by choosing a set of appropriate bins, which are defined according to their specific volume and fixed cost. Bins included in the capacity plan are chosen in advance without the exact knowledge of what items will be available for the dispatching. When, during the operational phase, the planned capacity is not sufficient, extra capacity must be purchased. An extensive experimental plan is used to analyze the impact that diversity in instance structure has on the capacity planning and the effect of considering different levels of variability and correlation of the stochastic parameters related to items

Endodontic and periodontal treatment of dens invaginatus: Report of 2 clinical cases

Abstract Objectives, materials and methods The purpose of this work is to describe the treatment of two lateral incisors affected by developmental abnormalities (Oehlers, types I and II) treated respectively through periodontal regenerative therapy associated with conservative correction of shape anomaly, and orthograde retreatment. Results Both therapies used resulted in complete remission of the initial symptoms and total healing of the lesions present. Conclusions "Dens invaginatus" is a dental development malformation that can predispose to the onset of caries, pulpal involvement and periodontal lesions, the treatment of which may require a specialized and often multidisciplinary approach. This malformation should therefore be recognized in time in order to establish effective prevention protocols, when possible, or prevent related consequences generating non-recoverable endodontic, periodontal or combined disease