City logistics: trasporto merci in ambito urbano

Molteplici studi, portati a termine di recente in Europa ed oltreoceano, hanno focalizzato l’attenzione sulle problematiche indotte dal trasporto merci in ambito urbano e contribuito ad identificarne possibili soluzioni (city logistics). Le aree urbane, dovrebbero idealmente essere luoghi ove abitare, svolgere attività economiche, sociali e ricreative. Esse possono vedere compromessa la loro predisposizione a tali scopi anche a causa del crescente traffico delle merci, il cui trasporto è effettuato principalmente su gomma, per via delle brevi distanze da coprire e delle carenze infrastrutturali. I veicoli commerciali, ad eccezione di quelli di ultima generazione, incidono negativamente sulla qualità dell’ambiente urbano, generando inquinamento atmosferico e acustico. La politica del “just in time”, che prevede l’assenza di magazzini di stoccaggio delle merci, incrementa i movimenti commerciali. Nella presente tesi vengono trattati alcuni aspetti logistici di regolamentazione della sosta e degli accessi per i mezzi di trasporto merci, in grado di rendere più efficiente la distribuzione dei beni, mitigando le problematiche indotte dal traffico e, quindi, salvaguardando la qualità di vita nei centri cittadini.

Urban food planning, city logistics and sustainability: the role of the wholesale produce market. The cases of Parma and Bologna food hubs.

At global level, the population is increasingly concentrating in the cities. In Europe, around 75% of the population lives in urban areas and, according to the European Environmental Agency (2010), urban population is foreseen to increase up to 80 % by 2020. At the same time, the quality of life in the cities is declining and urban pollution keeps increasing in terms of carbon dioxide (CO2) emissions, waste, noise, and lack of greenery. Many of European cities struggle to cope with social, economic and environmental problems resulting from pressures such as overcrowding or decline, social inequity, health problems related to food security and pollution. Nowadays local authorities try to solve these problems related to the environmental sustainability through various urban logistics measures, which directly and indirectly affect the urban food supply system, thus an integrated approach including freight transport and food provisioning policies issues is needed. This research centres on the urban food transport system and its impact on the city environmental sustainability. The main question that drives the research analysis is "How the urban food distribution system affects the ecological sustainability in modern cities?" The research analyses the city logistics project for food transport implemented in Parma, Italy, by the wholesale produce market. The case study investigates the renewed role of the wholesale market in the urban food supply chain as commercial and logistic operator, referring to the concept of food hub. Then, a preliminary analysis on the urban food transport for the city of Bologna is presented. The research aims at suggesting a methodological framework to estimate the urban food demand, the urban food supply and to assess the urban food transport performance, in order to identify external costs indicators that help policymakers in evaluating the environmental sustainability of different logistics measures

Models and Algorihtm for the Optimization of Real-World Routing and Logistics Problems

Logistics involves planning, managing, and organizing the flows of goods from the point of origin to the point of destination in order to meet some requirements. Logistics and transportation aspects are very important and represent a relevant costs for producing and shipping companies, but also for public administration and private citizens. The optimization of resources and the improvement in the organization of operations is crucial for all branches of logistics, from the operation management to the transportation. As we will have the chance to see in this work, optimization techniques, models, and algorithms represent important methods to solve the always new and more complex problems arising in different segments of logistics. Many operation management and transportation problems are related to the optimization class of problems called Vehicle Routing Problems (VRPs). In this work, we consider several real-world deterministic and stochastic problems that are included in the wide class of the VRPs, and we solve them by means of exact and heuristic methods. We treat three classes of real-world routing and logistics problems. We deal with one of the most important tactical problems that arises in the managing of the bike sharing systems, that is the Bike sharing Rebalancing Problem (BRP). We propose models and algorithms for real-world earthwork optimization problems. We describe the 3DP process and we highlight several optimization issues in 3DP. Among those, we define the problem related to the tool path definition in the 3DP process, the 3D Routing Problem (3DRP), which is a generalization of the arc routing problem. We present an ILP model and several heuristic algorithms to solve the 3DRP.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.