Crowd-shipping and autonomous vehicles: an optimization model for the last mile delivery

Il seguente elaborato propone un modello innovativo per la gestione della logistica distributiva nell’ultimo miglio, congiungendo l’attività di crowd-shipping con la presenza di Autonomous Vehicles, per il trasporto di prodotti all’interno della città. Il crowd-shipping utilizza conducenti occasionali, i quali deviano il loro tragitto in cambio di una ricompensa per il completamento dell’attività. Dall’altro lato, gli Autonomous Vehicles sono veicoli elettrici a guida autonoma, in grado di trasportare un numero limitato di pacchi e dotati di un sistema di sicurezza avanzato per garantire la fiducia nel trasporto. In primo luogo, nel seguente elaborato verrà mostrato il modello di ottimizzazione che congiunge i due attori principali in un unico ambiente, dove sono presenti un numero determinato di prodotti da muovere. Successivamente, poiché il problema di ottimizzazione è molto complesso e il numero di istanze valutabili è molto basso, verranno presentate due soluzioni differenti. La prima riguarda la metaeuristica chiamata Ant System, che cerca di avvicinarsi alle soluzioni ottime del precedente modello, mentre la seconda riguarda l’utilizzo di operatori di Local Search, i quali permettono di valutare soluzioni per istanze molto più grandi rispetto alla metaeuristica. Infine, i due modelli euristici verranno utilizzati per analizzare uno scenario che cerca di riprodurre una situazione reale. Tale scenario tenta di allocare strategicamente le risorse presenti e permette di dimostrare che gli Autonomous Vehicles riescono a supportare gli Occasional Drivers anche quando il numero di prodotti trasportabili è elevato. Inoltre, le due entità proposte riescono a soddisfare la domanda, garantendo un servizio che nel futuro potrebbe sostituire il tradizionale sistema di logistica distributiva last mile.

La qualità erogata da un'azienda di trasporto collettivo in ambito urbano: calcolo del livello di servizio delle principali linee di ATC (BO)

Oggetto di questa tesi è lo studio della qualità del servizio di trasporto erogato che condiziona la qualità percepita dall’utente, poiché spesso proprio a causa di un errato processo di pianificazione e gestione della rete, molte aziende non sono in grado di consolidare un alto livello di efficienza che permetta loro di attrarre e servire la crescente domanda. Per questo motivo, si è deciso di indagare sugli aspetti che determinano la qualità erogata e sui fattori che la influenzano, anche attraverso la definizione di alcuni indicatori rappresentativi del servizio erogato. L’area di studio considerata è stata quella urbana di Bologna, e sono state prese in esame due linee di ATC, la 19 e la 27, caratterizzate entrambe da una domanda di trasporto molto elevata. L’interesse è ricaduto in modo particolare sugli aspetti legati alla regolarità del servizio, ovvero al rispetto della cadenza programmata delle corse e alla puntualità, ossia il rispetto dell’orario programmato delle stesse. Proprio da questi due aspetti, infatti, dipende in larga misura la percezione della qualità che gli utenti hanno del servizio di trasporto collettivo. Lo studio è stato condotto sulla base di dati raccolti attraverso due campagne di rilevamento, una effettuata nel mese di maggio dell’anno 2008 e l’altra nel mese di settembre dello stesso anno. La scelta del periodo, della zona e delle modalità di rilevamento è strettamente connessa all’obiettivo prefissato. Il servizio è influenzato dalle caratteristiche del sistema di trasporto: sia da quelle legate alla domanda che da quelle legate all’offerta. Nel caso della domanda di trasporto si considera l’influenza sul servizio del numero di passeggeri saliti e del tempo di sosta alle fermate. Nel caso dell’offerta di trasporto si osservano soprattutto gli aspetti legati alla rete di trasporto su cui si muovono gli autobus, analizzando quindi i tempi di movimento e le velocità dei mezzi, per vedere come le caratteristiche dell’infrastruttura possano condizionare il servizio. A tale proposito è opportuno dire che, mentre i dati della prima analisi ci sono utili per lo studio dell’influenza del tempo di sosta sull’intertempo, nella seconda analisi si vuole cercare di effettuare ulteriori osservazioni sull’influenza del tempo di movimento sulla cadenza, prendendo in esame altri elementi, come ad esempio tratti di linea differenti rispetto al caso precedente. Un’attenzione particolare, inoltre, verrà riservata alla verifica del rispetto della cadenza, dalla quale scaturisce la definizione del livello di servizio per ciò che riguarda la regolarità. Per quest’ultima verrà, inoltre, determinato anche il LOS relativo alla puntualità. Collegato al problema del rispetto della cadenza è il fenomeno dell’accodamento: questo si verifica quando i mezzi di una stessa linea arrivano contemporaneamente ad una fermata uno dietro l’altro. L’accodamento ha, infatti, origine dal mancato rispetto della cadenza programmata tra i mezzi ed è un’evidente manifestazione del mal funzionamento di un servizio di trasporto. Verrà infine condotta un’analisi dei fattori che possono influenzare le prestazioni del servizio di trasporto pubblico, così da collocare i dati ottenuti dalle operazioni di rilevamento in un quadro più preciso, capace di sottolineare alcuni elementi di criticità e possibili rapporti di causalità.

Design and management of a supply chain: a joint location-inventory problem under uncertainty (Part B)

In recent years, global supply chains have increasingly suffered from reliability issues due to various external and difficult to-manage events. The following paper aims to build an integrated approach for the design of a Supply Chain under the risk of disruption and demand fluctuation. The study is divided in two parts: a mathematical optimization model, to identify the optimal design and assignments customer-facility, and a discrete-events simulation of the resulting network. The first one describes a model in which plant location decisions are influenced by variables such as distance to customers, investments needed to open plants and centralization phenomena that help contain the risk of demand variability (Risk Pooling). The entire model has been built with a proactive approach to manage the risk of disruptions assigning to each customer two types of open facilities: one that will serve it under normal conditions and a back-up facility, which comes into operation when the main facility has failed. The study is conducted on a relatively small number of instances due to the computational complexity, a matheuristic approach can be found in part A of the paper to evaluate the problem with a larger set of players. Once the network is built, a discrete events Supply Chain simulation (SCS) has been implemented to analyze the stock flow within the facilities warehouses, the actual impact of disruptions and the role of the back-up facilities which suffer a great stress on their inventory due to a large increase in demand caused by the disruptions. Therefore, simulation follows a reactive approach, in which customers are redistributed among facilities according to the interruptions that may occur in the system and to the assignments deriving from the design model. Lastly, the most important results of the study will be reported, analyzing the role of lead time in a reactive approach for the occurrence of disruptions and comparing the two models in terms of costs.

"Influence of lower-limb joint models on subject-specific musculoskeletal model predictions during gait" ( "modelli muscoloscheletrici personalizzati dell'arto inferiore: Analisi dell'effetto della modellazione dei giunti sulla predizione dei carichi agenti sul sistema scheletrico durante il cammino").

The aim of the present thesis was to investigate the influence of lower-limb joint models on musculoskeletal model predictions during gait. We started our analysis by using a baseline model, i.e., the state-of-the-art lower-limb model (spherical joint at the hip and hinge joints at the knee and ankle) created from MRI of a healthy subject in the Medical Technology Laboratory of the Rizzoli Orthopaedic Institute. We varied the models of knee and ankle joints, including: knee- and ankle joints with mean instantaneous axis of rotation, universal joint at the ankle, scaled-generic-derived planar knee, subject-specific planar knee model, subject-specific planar ankle model, spherical knee, spherical ankle. The joint model combinations corresponding to 10 musculoskeletal models were implemented into a typical inverse dynamics problem, including inverse kinematics, inverse dynamics, static optimization and joint reaction analysis algorithms solved using the OpenSim software to calculate joint angles, joint moments, muscle forces and activations, joint reaction forces during 5 walking trials. The predicted muscle activations were qualitatively compared to experimental EMG, to evaluate the accuracy of model predictions. Planar joint at the knee, universal joint at the ankle and spherical joints at the knee and at the ankle produced appreciable variations in model predictions during gait trials. The planar knee joint model reduced the discrepancy between the predicted activation of the Rectus Femoris and the EMG (with respect to the baseline model), and the reduced peak knee reaction force was considered more accurate. The use of the universal joint, with the introduction of the subtalar joint, worsened the muscle activation agreement with the EMG, and increased ankle and knee reaction forces were predicted. The spherical joints, in particular at the knee, worsened the muscle activation agreement with the EMG. A substantial increase of joint reaction forces at all joints was predicted despite of the good agreement in joint kinematics with those of the baseline model. The introduction of the universal joint had a negative effect on the model predictions. The cause of this discrepancy is likely to be found in the definition of the subtalar joint and thus, in the particular subject’s anthropometry, used to create the model and define the joint pose. We concluded that the implementation of complex joint models do not have marked effects on the joint reaction forces during gait. Computed results were similar in magnitude and in pattern to those reported in literature. Nonetheless, the introduction of planar joint model at the knee had positive effect upon the predictions, while the use of spherical joint at the knee and/or at the ankle is absolutely unadvisable, because it predicted unrealistic joint reaction forces.

Design and optimization of turbo-expanders for organic rankine cycles

In a world focused on the need to produce energy for a growing population, while reducing atmospheric emissions of carbon dioxide, organic Rankine cycles represent a solution to fulfil this goal. This study focuses on the design and optimization of axial-flow turbines for organic Rankine cycles. From the turbine designer point of view, most of this fluids exhibit some peculiar characteristics, such as small enthalpy drop, low speed of sound, large expansion ratio. A computational model for the prediction of axial-flow turbine performance is developed and validated against experimental data. The model allows to calculate turbine performance within a range of accuracy of ±3%. The design procedure is coupled with an optimization process, performed using a genetic algorithm where the turbine total-to-static efficiency represents the objective function. The computational model is integrated in a wider analysis of thermodynamic cycle units, by providing the turbine optimal design. First, the calculation routine is applied in the context of the Draugen offshore platform, where three heat recovery systems are compared. The turbine performance is investigated for three competing bottoming cycles: organic Rankine cycle (operating cyclopentane), steam Rankine cycle and air bottoming cycle. Findings indicate the air turbine as the most efficient solution (total-to-static efficiency = 0.89), while the cyclopentane turbine results as the most flexible and compact technology (2.45 ton/MW and 0.63 m3/MW). Furthermore, the study shows that, for organic and steam Rankine cycles, the optimal design configurations for the expanders do not coincide with those of the thermodynamic cycles. This suggests the possibility to obtain a more accurate analysis by including the computational model in the simulations of the thermodynamic cycles. Afterwards, the performance analysis is carried out by comparing three organic fluids: cyclopentane, MDM and R245fa. Results suggest MDM as the most effective fluid from the turbine performance viewpoint (total-to-total efficiency = 0.89). On the other hand, cyclopentane guarantees a greater net power output of the organic Rankine cycle (P = 5.35 MW), while R245fa represents the most compact solution (1.63 ton/MW and 0.20 m3/MW). Finally, the influence of the composition of an isopentane/isobutane mixture on both the thermodynamic cycle performance and the expander isentropic efficiency is investigated. Findings show how the mixture composition affects the turbine efficiency and so the cycle performance. Moreover, the analysis demonstrates that the use of binary mixtures leads to an enhancement of the thermodynamic cycle performance.

Modelling, simulation and optimization of maintenance considerations on condition based maintenance

Globalization has increased the pressure on organizations and companies to operate in the most efficient and economic way. This tendency promotes that companies concentrate more and more on their core businesses, outsource less profitable departments and services to reduce costs. By contrast to earlier times, companies are highly specialized and have a low real net output ratio. For being able to provide the consumers with the right products, those companies have to collaborate with other suppliers and form large supply chains. An effect of large supply chains is the deficiency of high stocks and stockholding costs. This fact has lead to the rapid spread of Just-in-Time logistic concepts aimed minimizing stock by simultaneous high availability of products. Those concurring goals, minimizing stock by simultaneous high product availability, claim for high availability of the production systems in the way that an incoming order can immediately processed. Besides of design aspects and the quality of the production system, maintenance has a strong impact on production system availability. In the last decades, there has been many attempts to create maintenance models for availability optimization. Most of them concentrated on the availability aspect only without incorporating further aspects as logistics and profitability of the overall system. However, production system operator’s main intention is to optimize the profitability of the production system and not the availability of the production system. Thus, classic models, limited to represent and optimize maintenance strategies under the light of availability, fail. A novel approach, incorporating all financial impacting processes of and around a production system, is needed. The proposed model is subdivided into three parts, maintenance module, production module and connection module. This subdivision provides easy maintainability and simple extendability. Within those modules, all aspect of production process are modeled. Main part of the work lies in the extended maintenance and failure module that offers a representation of different maintenance strategies but also incorporates the effect of over-maintaining and failed maintenance (maintenance induced failures). Order release and seizing of the production system are modeled in the production part. Due to computational power limitation, it was not possible to run the simulation and the optimization with the fully developed production model. Thus, the production model was reduced to a black-box without higher degree of details.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

Economic and Environmental Optimization of Ornamental Stone Block Cutting with a 3D Algorithm

This thesis focuses on finding the optimum block cutting dimensions in terms of the environmental and economic factors by using a 3D algorithm for a limestone quarry in Foggia, Italy. The environmental concerns of quarrying operations are mainly: energy consumption, material waste, and pollution. The main economic concerns are the block recovery, the selling prices, and the production costs. Fractures adversely affect the block recovery ratio. With a fracture model, block production can be optimized. In this research, the waste volume produced by quarrying was minimised to increase the recovery ratio and ensure economic benefits. SlabCutOpt is a software developed at DICAM–University of Bologna for block cutting optimization which tests different cutting angles on the x-y-z planes to offer up alternative cutting methods. The program tests several block sizes and outputs the optimal result for each entry. By using SlabCutOpt, ten different block dimensions were analysed, the results indicated the maximum number of non-intersecting blocks for each dimension. After analysing the outputs, the block named number 1 with the dimensions ‘1mx1mx1m’ had the highest recovery ratio as 43% and the total Relative Money Value (RMV) with a value of 22829. Dimension number 1, also had the lowest waste volume, with a value of 3953.25 m3, for the total bench. For cutting the total bench volume of 6932.25m3, the diamond wire cutter had the lowest dust emission values for the block with the dimension ‘2mx2mx2m’, with a value of 24m3. When compared with the Eco-Label standards, block dimensions having surface area values lower than 15m2, were found to fit the natural resource waste criteria of the label, as the threshold required 25% of minimum recovery [1]. Due to the relativity of production costs, together with the Eco-Label threshold, the research recommends the selection of the blocks with a surface area value between 6m2 and 14m2.

Virtual Calibration of CO2 and Pollutant Emissions of a High-Performance PHEV using Model-in-the-Loop Methodology

The study analyses the calibration process of a newly developed high-performance plug-in hybrid electric passenger car powertrain. The complexity of modern powertrains and the more and more restrictive regulations regarding pollutant emissions are the primary challenges for the calibration of a vehicle’s powertrain. In addition, the managers of OEM need to know as earlier as possible if the vehicle under development will meet the target technical features (emission included). This leads to the necessity for advanced calibration methodologies, in order to keep the development of the powertrain robust, time and cost effective. The suggested solution is the virtual calibration, that allows the tuning of control functions of a powertrain before having it built. The aim of this study is to calibrate virtually the hybrid control unit functions in order to optimize the pollutant emissions and the fuel consumption. Starting from the model of the conventional vehicle, the powertrain is then hybridized and integrated with emissions and aftertreatments models. After its validation, the hybrid control unit strategies are optimized using the Model-in-the-Loop testing methodology. The calibration activities will proceed thanks to the implementation of a Hardware-in-the-Loop environment, that will allow to test and calibrate the Engine and Transmission control units effectively, besides in a time and cost saving manner.

Tolerance optimization with statistical approach in the CAT environment

Nowadays, product development in all its phases plays a fundamental role in the industrial chain. The need for a company to compete at high levels, the need to be quick in responding to market demands and therefore to be able to engineer the product quickly and with a high level of quality, has led to the need to get involved in new more advanced methods/ processes. In recent years, we are moving away from the concept of 2D-based design and production and approaching the concept of Model Based Definition. By using this approach, increasingly complex systems turn out to be easier to deal with but above all cheaper in obtaining them. Thanks to the Model Based Definition it is possible to share data in a lean and simple way to the entire engineering and production chain of the product. The great advantage of this approach is precisely the uniqueness of the information. In this specific thesis work, this approach has been exploited in the context of tolerances with the aid of CAD / CAT software. Tolerance analysis or dimensional variation analysis is a way to understand how sources of variation in part size and assembly constraints propagate between parts and assemblies and how that range affects the ability of a project to meet its requirements. It is critically important to note how tolerance directly affects the cost and performance of products. Worst Case Analysis (WCA) and Statistical analysis (RSS) are the two principal methods in DVA. The thesis aims to show the advantages of using statistical dimensional analysis by creating and examining various case studies, using PTC CREO software for CAD modeling and CETOL 6σ for tolerance analysis. Moreover, it will be provided a comparison between manual and 3D analysis, focusing the attention to the information lost in the 1D case. The results obtained allow us to highlight the need to use this approach from the early stages of the product design cycle.

A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.

Machine Learning in Automatic Tabulation for Constraint Model Reformulation

Combinatorial decision and optimization problems belong to numerous applications, such as logistics and scheduling, and can be solved with various approaches. Boolean Satisfiability and Constraint Programming solvers are some of the most used ones and their performance is significantly influenced by the model chosen to represent a given problem. This has led to the study of model reformulation methods, one of which is tabulation, that consists in rewriting the expression of a constraint in terms of a table constraint. To apply it, one should identify which constraints can help and which can hinder the solving process. So far this has been performed by hand, for example in MiniZinc, or automatically with manually designed heuristics, in Savile Row. Though, it has been shown that the performances of these heuristics differ across problems and solvers, in some cases helping and in others hindering the solving procedure. However, recent works in the field of combinatorial optimization have shown that Machine Learning (ML) can be increasingly useful in the model reformulation steps. This thesis aims to design a ML approach to identify the instances for which Savile Row’s heuristics should be activated. Additionally, it is possible that the heuristics miss some good tabulation opportunities, so we perform an exploratory analysis for the creation of a ML classifier able to predict whether or not a constraint should be tabulated. The results reached towards the first goal show that a random forest classifier leads to an increase in the performances of 4 different solvers. The experimental results in the second task show that a ML approach could improve the performance of a solver for some problem classes.