Microfluidic device and interfacial transport: application to biomolecules and nanostructures

The aim of my dissertation is to provide new knowledge and applications of microfluidics in a variety of problems, from materials science, devices, and biomedicine, where the control on the fluid dynamics and the local concentration of the solutions containing the relevant molecules (either materials, precursors, or biomolecules) is crucial. The control of interfacial phenomena occurring in solutions at dierent length scales is compelling in nanotechnology for devising new sensors, molecular electronics devices, memories. Microfluidic devices were fabricated and integrated with organic electronics devices. The transduction involves the species in the solution which infills the transistor channel and confined by the microfluidic device. This device measures what happens on the surface, at few nanometers from the semiconductor channel. Soft-lithography was adopted to fabricate platinum electrodes, starting from platinum carbonyl precursor. I proposed a simple method to assemble these nanostructures in periodic arrays of microstripes, and form conductive electrodes with characteristic dimension of 600 nm. The conductivity of these sub-microwires is compared with the values reported in literature and bulk platinum. The process is suitable for fabricating thin conductive patterns for electronic devices or electrochemical cells, where the periodicity of the conductive pattern is comparable with the diusion length of the molecules in solution. The ordering induced among artificial nanostructures is of particular interest in science. I show that large building blocks, like carbon nanotubes or core-shell nanoparticles, can be ordered and self-organised on a surface in patterns due to capillary forces. The eective probability of inducing order with microfluidic flow is modeled with finite element calculation on the real geometry of the microcapillaries, in soft-lithographic process. The oligomerization of A40 peptide in microconfined environment represents a new investigation of the extensively studied peptide aggregation. The added value of the approach I devised is the precise control on the local concentration of peptides together with the possibility to mimick cellular crowding. Four populations of oligomers where distinguished, with diameters ranging from 15 to 200 nm. These aggregates could not be addresses separately in fluorescence. The statistical analysis on the atomic force microscopy images together with a model of growth reveal new insights on the kinetics of amyloidogenesis as well as allows me to identify the minimum stable nucleus size. This is an important result owing to its implications in the understanding and early diagnosis and therapy of the Alzheimer’s disease

Integrating Crew Scheduling and Rostering Problems

Crew scheduling and crew rostering are similar and related problems which can be solved by similar procedures. So far, the existing solution methods usually create a model for each one of these problems (scheduling and rostering), and when they are solved together in some cases an interaction between models is considered in order to obtain a better solution. A single set covering model to solve simultaneously both problems is presented here, where the total quantity of drivers needed is directly considered and optimized. This integration allows to optimize all of the depots at the same time, while traditional approaches needed to work depot by depot, and also it allows to see and manage the relationship between scheduling and rostering, which was known in some degree but usually not easy to quantify as this model permits. Recent research in the area of crew scheduling and rostering has stated that one of the current challenges to be achieved is to determine a schedule where crew fatigue, which depends mainly on the quality of the rosters created, is reduced. In this approach rosters are constructed in such way that stable working hours are used in every week of work, and a change to a different shift is done only using free days in between to make easier the adaptation to the new working hours. Computational results for real-world-based instances are presented. Instances are geographically diverse to test the performance of the procedures and the model in different scenarios.

Decomposition and reformulation of integer linear programming problems

This thesis deals with an investigation of Decomposition and Reformulation to solve Integer Linear Programming Problems. This method is often a very successful approach computationally, producing high-quality solutions for well-structured combinatorial optimization problems like vehicle routing, cutting stock, p-median and generalized assignment . However, until now the method has always been tailored to the specific problem under investigation. The principal innovation of this thesis is to develop a new framework able to apply this concept to a generic MIP problem. The new approach is thus capable of auto-decomposition and autoreformulation of the input problem applicable as a resolving black box algorithm and works as a complement and alternative to the normal resolving techniques. The idea of Decomposing and Reformulating (usually called in literature Dantzig and Wolfe Decomposition DWD) is, given a MIP, to convexify one (or more) subset(s) of constraints (slaves) and working on the partially convexified polyhedron(s) obtained. For a given MIP several decompositions can be defined depending from what sets of constraints we want to convexify. In this thesis we mainly reformulate MIPs using two sets of variables: the original variables and the extended variables (representing the exponential extreme points). The master constraints consist of the original constraints not included in any slaves plus the convexity constraint(s) and the linking constraints(ensuring that each original variable can be viewed as linear combination of extreme points of the slaves). The solution procedure consists of iteratively solving the reformulated MIP (master) and checking (pricing) if a variable of reduced costs exists, and in which case adding it to the master and solving it again (columns generation), or otherwise stopping the procedure. The advantage of using DWD is that the reformulated relaxation gives bounds stronger than the original LP relaxation, in addition it can be incorporated in a Branch and bound scheme (Branch and Price) in order to solve the problem to optimality. If the computational time for the pricing problem is reasonable this leads in practice to a stronger speed up in the solution time, specially when the convex hull of the slaves is easy to compute, usually because of its special structure.

Hybrid constraint programming and metaheuristic methods for large scale optimization problems

This work presents hybrid Constraint Programming (CP) and metaheuristic methods for the solution of Large Scale Optimization Problems; it aims at integrating concepts and mechanisms from the metaheuristic methods to a CP-based tree search environment in order to exploit the advantages of both approaches. The modeling and solution of large scale combinatorial optimization problem is a topic which has arisen the interest of many researcherers in the Operations Research field; combinatorial optimization problems are widely spread in everyday life and the need of solving difficult problems is more and more urgent. Metaheuristic techniques have been developed in the last decades to effectively handle the approximate solution of combinatorial optimization problems; we will examine metaheuristics in detail, focusing on the common aspects of different techniques. Each metaheuristic approach possesses its own peculiarities in designing and guiding the solution process; our work aims at recognizing components which can be extracted from metaheuristic methods and re-used in different contexts. In particular we focus on the possibility of porting metaheuristic elements to constraint programming based environments, as constraint programming is able to deal with feasibility issues of optimization problems in a very effective manner. Moreover, CP offers a general paradigm which allows to easily model any type of problem and solve it with a problem-independent framework, differently from local search and metaheuristic methods which are highly problem specific. In this work we describe the implementation of the Local Branching framework, originally developed for Mixed Integer Programming, in a CP-based environment. Constraint programming specific features are used to ease the search process, still mantaining an absolute generality of the approach. We also propose a search strategy called Sliced Neighborhood Search, SNS, that iteratively explores slices of large neighborhoods of an incumbent solution by performing CP-based tree search and encloses concepts from metaheuristic techniques. SNS can be used as a stand alone search strategy, but it can alternatively be embedded in existing strategies as intensification and diversification mechanism. In particular we show its integration within the CP-based local branching. We provide an extensive experimental evaluation of the proposed approaches on instances of the Asymmetric Traveling Salesman Problem and of the Asymmetric Traveling Salesman Problem with Time Windows. The proposed approaches achieve good results on practical size problem, thus demonstrating the benefit of integrating metaheuristic concepts in CP-based frameworks.

Dynamic stabilization of Rayleigh-Taylor instability of ablation fronts in inertial confinement fusion

One of the most important problems in inertial confinement fusion is how to find a way to mitigate the onset of the Rayleigh-Taylor instability which arises in the ablation front during the compression. In this thesis it is studied in detail the possibility of using for such a purpose the well-known mechanism of dynamic stabilization, already applied to other dynamical systems such as the inverted pendulum. In this context, a periodic acceleration superposed to the background gravity generates a vertical vibration of the ablation front itself. The effects of different driving modulations (Dirac deltas and square waves) are analyzed from a theoretical point of view, with a focus on stabilization of ion beam driven ablation fronts, and a comparison is made, in order to look for optimization.

Analysis of optimal control problems for the incompressible MHD equations and implementation in a finite element multiphysics code

This thesis deals with the study of optimal control problems for the incompressible Magnetohydrodynamics (MHD) equations. Particular attention to these problems arises from several applications in science and engineering, such as fission nuclear reactors with liquid metal coolant and aluminum casting in metallurgy. In such applications it is of great interest to achieve the control on the fluid state variables through the action of the magnetic Lorentz force. In this thesis we investigate a class of boundary optimal control problems, in which the flow is controlled through the boundary conditions of the magnetic field. Due to their complexity, these problems present various challenges in the definition of an adequate solution approach, both from a theoretical and from a computational point of view. In this thesis we propose a new boundary control approach, based on lifting functions of the boundary conditions, which yields both theoretical and numerical advantages. With the introduction of lifting functions, boundary control problems can be formulated as extended distributed problems. We consider a systematic mathematical formulation of these problems in terms of the minimization of a cost functional constrained by the MHD equations. The existence of a solution to the flow equations and to the optimal control problem are shown. The Lagrange multiplier technique is used to derive an optimality system from which candidate solutions for the control problem can be obtained. In order to achieve the numerical solution of this system, a finite element approximation is considered for the discretization together with an appropriate gradient-type algorithm. A finite element object-oriented library has been developed to obtain a parallel and multigrid computational implementation of the optimality system based on a multiphysics approach. Numerical results of two- and three-dimensional computations show that a possible minimum for the control problem can be computed in a robust and accurate manner.

Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.

Oxygen: problems and solutions in electrochemistry

Different aspects of the electrochemistry of oxygen are examined through four experimental examples: corrosion, passivation via organic thin films, oxygen reduction and water oxidation catalysis are outlined in order to outline the very different ways and circumstances in which oxygen plays a major role in electrochemistry.

Decomposition Methods and Network Design Problems

Decomposition based approaches are recalled from primal and dual point of view. The possibility of building partially disaggregated reduced master problems is investigated. This extends the idea of aggregated-versus-disaggregated formulation to a gradual choice of alternative level of aggregation. Partial aggregation is applied to the linear multicommodity minimum cost flow problem. The possibility of having only partially aggregated bundles opens a wide range of alternatives with different trade-offs between the number of iterations and the required computation for solving it. This trade-off is explored for several sets of instances and the results are compared with the ones obtained by directly solving the natural node-arc formulation. An iterative solution process to the route assignment problem is proposed, based on the well-known Frank Wolfe algorithm. In order to provide a first feasible solution to the Frank Wolfe algorithm, a linear multicommodity min-cost flow problem is solved to optimality by using the decomposition techniques mentioned above. Solutions of this problem are useful for network orientation and design, especially in relation with public transportation systems as the Personal Rapid Transit. A single-commodity robust network design problem is addressed. In this, an undirected graph with edge costs is given together with a discrete set of balance matrices, representing different supply/demand scenarios. The goal is to determine the minimum cost installation of capacities on the edges such that the flow exchange is feasible for every scenario. A set of new instances that are computationally hard for the natural flow formulation are solved by means of a new heuristic algorithm. Finally, an efficient decomposition-based heuristic approach for a large scale stochastic unit commitment problem is presented. The addressed real-world stochastic problem employs at its core a deterministic unit commitment planning model developed by the California Independent System Operator (ISO).

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.

1st and 2nd Generation Ethanol from Biomass Crops

In chapter 1 and 2 calcium hydroxide as impregnation agent before steam explosion of sugarcane bagasse and switchgrass, respectively, was compared with auto-hydrolysis, assessing the effects on enzymatic hydrolysis and simultaneous saccharification and fermentation (SSF) at high solid concentration of pretreated solid fraction. In addition, anaerobic digestion of pretreated liquid fraction was carried out, in order to appraise the effectiveness of calcium hydroxide before steam explosion in a more comprehensive way. In As water is an expensive input in both cultivation of biomass crops and subsequent pretreatment, Chapter 3 addressed the effects of variable soil moisture on biomass growth and composition of biomass sorghum. Moreover, the effect of water stress was related to the characteristics of stem juice for 1st generation ethanol and structural carbohydrates for 2nd generation ethanol. In the frame of chapter 1, calcium hydroxide was proven to be a suitable catalyst for sugarcane bagasse before steam explosion, in order to enhance fibre deconstruction. In chapter 2, effect of calcium hydroxide on switchgrass showed a great potential when ethanol was focused, whereas acid addition produced higher methane yield. Regarding chapter 3, during crop cycle the amount of cellulose, hemicellulose and AIL changed causing a decrease of 2G ethanol amount. Biomass physical and chemical properties involved a lower glucose yield and concentration at the end of enzymatic hydrolysis and, consequently, a lower 2G ethanol concentration at the end of simultaneous saccharification and fermentation, proving that there is strong relationship between structure, chemical composition, and fermentable sugar yield. The significantly higher concentration of ethanol at the early crop stage could be an important incentive to consider biomass sorghum as second crop in the season, to be introduced into some agricultural systems, potentially benefiting farmers and, above all, avoiding the exacerbation of the debate about fuel vs food crops.