Combining robustness and recovery for airline schedules

In this thesis, we address different aspects of the airline scheduling problem. The main difficulty in this field lies in the combinatorial complexity of the problems. Furthermore, as airline schedules are often faced with perturbations called disruptions (bad weather conditions, technical failures, congestion, crew illness…), planning for better performance under uncertainty is an additional dimension to the complexity of the problem. Our main focus is to develop better schedules that are less sensitive to perturbations and, when severe disruptions occur, are easier to recover. The former property is known as robustness and the latter is called recoverability. We start the thesis by addressing the problem of recovering a disrupted schedule. We present a general model, the constraint-specific recovery network, that encodes all feasible recovery schemes of any unit of the recovery problem. A unit is an aircraft, a crew member or a passenger and its recovery scheme is a new route, pairing or itinerary, respectively. We show how to model the Aircraft Recovery Problem (ARP) and the Passenger Recovery Problem (PRP), and provide computational results for both of them. Next, we present a general framework to solve problems subject to uncertainty: the Uncertainty Feature Optimization (UFO) framework, which implicitly embeds the uncertainty the problem is prone to. We show that UFO is a generalization of existing methods relying on explicit uncertainty models. Furthermore, we show that by implicitly considering uncertainty, we not only save the effort of modeling an explicit uncertainty set: we also protect against possible errors in its modeling. We then show that combining existing methods using explicit uncertainty characterization with UFO leads to more stable solutions with respect to changes in the noise's nature. We illustrate these concepts with extensive simulations on the Multi-Dimensional Knapsack Problem (MDKP). We then apply the UFO to airline scheduling. First, we study how robustness is defined in airline scheduling and then compare robustness of UFO models against existing models in the literature. We observe that the performance of the solutions closely depend on the way the performance is evaluated. UFO solutions seem to perform well globally, but models using explicit uncertainty have a better potential when focusing on a specific metric. Finally, we study the recoverability of UFO solutions with respect to the recovery algorithm we develop. Computational results on a European airline show that UFO solutions are able to significantly reduce recovery costs

Design, synthesis, and evaluation of poly(1,2-glycerol carbonate)-paclitaxel conjugate nanoparticles for the tunable delivery of paclitaxel

Since their initial conceptualization, polymer-drug conjugate nanocarriers have been a mainstay of the drug delivery field. The conjugation of therapeutic agents to polymeric carriers offers several critical advantages including improved drug solubilization, controlled release, and enhanced safety. Accordingly, polymer-drug conjugate nanocarriers are uniquely positioned to remedy some of the limitations of conventional small molecule chemotherapeutics, namely their narrow window of therapeutic efficacy, rapid clearance, and limited tumor exposure. This dissertation describes the design, synthesis, and evaluation of a novel sustained release, biodegradable polymeric nanocarrier as a single administration replacement of multi-dose paclitaxel (PTX) treatment regimens. The synthesis of poly(1,2-glycerol carbonate)-graft-succinic acid-paclitaxel (PGC-PTX) is presented, and its use enables high, controlled PTX loadings. Moreover, the polymer backbone is composed of biocompatible building blocks—glycerol and carbon dioxide. When formulated as nanoparticles (NPs), PGC-PTX NPs exhibit high aqueous PTX concentrations, sub-100 nm diameters, narrow dispersity, prolonged storage stability, and sustained and controlled PTX release kinetics. In murine models of peritoneal carcinomatosis, in which the clinical implementation of multi-dose intraperitoneal (IP) treatment regimens is limited by catheter-related complications, PGC-PTX NPs exhibit improved safety at high doses, tumor localization, and efficacy even after a single IP injection, with comparable therapeutic effect to multi-dose IP PTX treatment regimens. The PGC-PTX NP platform is additionally amenable to optimization via modulation of nanocarrier properties. Specifically, the dual conjugation and physical entrapment of PTX in the NPs harnesses the physicochemical interactions between free and conjugated PTX to achieve unprecedented ultra-high drug loadings as well as facile control of nanomechanical properties and release kinetics. Optimization of these programmable carriers consequently enables the safe delivery of high drug doses as well as sustained therapeutic efficacy. In a murine model of peritoneal carcinomatosis, a single high dose of dual-loaded PGC-PTX nanocarriers affords significantly improved survival compared to weekly, multi-dose PTX treatment. Modulation of nanocarrier properties via the incorporation of poly(lactide-co-glycolide) (PLGA) is additionally explored. Although the integration of PLGA does not significantly alter NP physical properties, the polymer blend nanocarriers exhibit improved in vitro potency relative to PGC-PTX NPs, warranting the continued evaluation of the mechanism by which PLGA modulates nanocarrier efficacy.2020-07-02T00:00:00

A multi-objective, decomposition-based algorithm design methodology and its application to runaway operations planning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.Includes bibliographical references (p. 283-296).(cont.) to the design of a heuristic decomposed algorithm for solving the ROP problem. This decomposition methodology offers an original paradigm potentially applicable to the design of solution algorithms for a class of problems with functions and parameters that, similar to those of the ROP problem, can be parsed in subsets. The potential merit in decomposing the ROP problem in two stages and the resulting utility of the two-stage solution algorithm are evaluated by performing benefits analysis across specific dimensions related to airport efficiency, as well as stability and robustness analysis of the algorithm output.Significant delays and resulting environmental impacts are commonly observed during departure operations at major US and European airports. One approach for mitigating airport congestion and delays is to exercise tactical operations planning and control with an objective to improve the efficiency of surface and terminal area operations. As a subtask of planning airport surface operations, this thesis presents a thorough study of the structure and properties of the Runway Operations Planning (ROP) problem. Runway Operations Planning is a workload-intensive task for controllers because airport operations involve many parameters, such as departure demand level and timing that are typically characterized by a highly dynamic behavior. This research work provides insight to the nature of this task, by analyzing the different parameters involved in it and illuminating how they interact with each other and how they affect the main functions in the problem of planning operations at the runway, such as departure runway throughput and runway queuing delays. Analysis of the Runway Operations Planning problem revealed that there is a parameter of the problem, namely the demand "weight class mix", which: a) is more "dominant" on the problem performance functions that other parameters, b) changes value much slower than other parameters and c) its value is available earlier and with more certainty than the value of other parameters. These observations enabled the parsing of the set of functions and the set of parameters in subsets, so that the problem can be addressed sequentially in more than one stage where different parameter subsets are treated in different stages. Thus, a decomposition-based algorithm design technique was introduced and appliedby Ioannis D. Anagnostakis.Ph.D

A Multi-Objective, Decomposition-Based Algorithm Design Methodology and its Application to Runway Operations Planning

Significant delays and resulting environmental impacts are commonly observed during departure operations at major US and European airports. One approach for mitigating airport congestion and delays is to exercise tactical operations planning and control with an objective to improve the efficiency of surface and terminal area operations. As a subtask of planning airport surface operations, this thesis presents a thorough study of the structure and properties of the Runway Operations Planning (ROP) problem. Runway Operations Planning is a workload-intensive task for controllers because airport operations involve many parameters, such as departure demand level and timing that are typically characterized by a highly dynamic behavior. This research work provides insight to the nature of this task, by analyzing the different parameters involved in it and illuminating how they interact with each other and how they affect the main functions in the problem of planning operations at the runway, such as departure runway throughput and runway queuing delays. Analysis of the Runway Operations Planning problem revealed that there is a parameter of the problem, namely the demand “weight class mix”, which: a) is more “dominant” on the problem performance functions that other parameters, b) changes value much slower than other parameters and c) its value is available earlier and with more certainty than the value of other parameters. These observations enabled the parsing of the set of functions and the set of parameters in subsets, so that the problem can be addressed sequentially in more than one stage where different parameter subsets are treated in different stages. Thus, a decompositionbased algorithm design technique was introduced and applied to the design of a heuristic decomposed algorithm for solving the ROP problem. This decomposition methodology offers an original paradigm potentially applicable to the design of solution algorithms for a class of problems with functions and parameters that, similar to those of the ROP problem, can be parsed in subsets. The potential merit in decomposing the ROP problem in two stages and the resulting utility of the two-stage solution algorithm are evaluated by performing benefits analysis across specific dimensions related to airport efficiency, as well as stability and robustness analysis of the algorithm output

Grazing time: the missing link : a study of the plant-animal interface by integration of experimental and modelling approaches

A series of grazing (chapters 2, 3, 5 and 6) in-vitro (chapter 4) and modelling trials (chapters 1 and 7) were combined with the following objectives: a) to gain insight in the main mechanisms controlling dry matter intake (DMI), intake rate (IR) and grazing time (GT), during the first grazing session after a.m. milking, b) to judge the relative importance of rumen fill and the concentration of fermentation products in the rumen liquor as candidates to signal the end of the grazing sessions and c) to develop new and modify and evaluate existing simulation model, to operate under non-steady state conditions with the aim to predict DMI, rumen fermentation and supply of nutrients.Increasing the length of the allowed grazing time significantly increased DMI (PThe interaction between starvation time and rumen fill before grazing on GT, although not significant (PThis research offered valuable information about the relative importance of several factors in the control of GT. Clearly it is necessary to understand the way in which the different signals produced at different places are integrated for the animal to modulate eating and other behaviour. In this sense the combination of analytical and synthetic research was proven to be an effective strategy.PhD Thesis, Animal Nutrition Group, Wageningen Agricultural University, Marijkeweg 40, 6709 PG, Wageningen, The Netherlands.</p

Algorithmes d'approximation pour des programmes linéaires et les problèmes de Packing avec des contraintes géometriques

In this thesis we approach several problems with approximation algorithms; these are feasibility problems as well as optimization problems. In Chapter 1 we give a brief introduction into the general paradigm of approximation algorithms, motivate the problems, and give an outline of the thesis. In Chapter 2, we discuss two algorithms to approximately generate a feasible solution of the mixed packing and covering problem which is a model from convex optimization. This problem includes a large class of linear programs. The algorithms generate approximately feasible solutions within O(M(ln M+epsilon^{-2} ln epsilon^{-1})) and O(M epsilon{-2} ln (M epsilon^{-1}))$iterations, respectively, where in each iteration a block problem which depends on the specific application has to be solved. Both algorithms, applied to linear programs, can result in column generation algorithms. In Chapter 3, we implement an algorithm for the so-called max-min-resource sharing problem. This is a certain convex optimization problem which, similar to the problem in Chapter 1, includes a large class of linear programs. The implementation, which is included in the appendix, is done in C++. We use the implementation in the context of an AFPTAS for Strip Packing in order to evaluate dynamic optimization of a parameter in the algorithm, namely the step length used for interpolation. We compare our choice to the static step length proposed in the analysis of the algorithm and conclude that dynamic optimization of the step length significantly reduces the number of iterations. In Chapter 4, we study two closely related scheduling problems, namely non-preemptive scheduling with fixed jobs and scheduling with non-availability for sequential jobs on m identical machines under the makespan objective, where m is constant. For the first problem, which does not admit an FPTAS unless P=NP, we obtain a new PTAS. For the second problem, we show that a suitable restriction (namely the permanent availability of one machine) is necessary to obtain a bounded approximation ratio. For this restriction, which does not admit an FPTAS unless P=NP, we present a PTAS; we also discuss the complexity of various special cases. In total, the results are basically best possible. In Chapter 5, we continue the studies from Chapter 4 where now the number m of machines is part of the input, which makes the problem algorithmically harder. Scheduling with fixed jobs does not admit an approximation ratio better than 3/2, unless P=NP; here we obtain an approximation ratio of 3/2+epsilon for any epsilon>0. For scheduling with non-availability, we require a constant percentage of the machines to be permanently available. This restriction also does not admit an approximation ratio better than 3/2 unless P=NP; we also obtain an approximation ratio of$3/2+\epsilon$ for any epsilon>0. With an interesting argument, the approximation ratio for both problems is refined to exactly 3/2. We also point out an interesting relation of scheduling with fixed jobs to Bin Packing. As in Chapter 4, the results are in a certain sense best possible. Finally, in Chapter 6, we conclude with some remarks and open research problems

Mathematical programming models for livestock production

SIGLELD:D50436/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Improvement of anterior and posterior segment ocular drug delivery: application in ocular cystinosis and age-related macular degeneration

In this doctoral thesis, hydrogel-based formulations containing cysteamine of great potential for the treatment of ocular cystinosis have been developed. The hydrogel based on hyaluronic acid polymer has been extensively characterised and its preparation has been translated to Hospital Pharmacy Departments. On the other hand, regarding the study of intravitreal injections in AMD, this thesis has demonstrated the usefulness of the PET methodology for the study of intravitreal pharmacokinetics in rats, which has been subsequently used for the evaluation of the ocular pharmacokinetics after intravitreal administration of aflibercept and bevacizumab. Moreover, intravitreal chitosan-based implants containing aflibercept which permit its extended delivery have been developed

Algorithmes d'approximation pour des programmes linéaires et les problèmes de Packing avec des contraintes géometriques

In this thesis we approach several problems with approximation algorithms; these are feasibility problems as well as optimization problems. In Chapter 1 we give a brief introduction into the general paradigm of approximation algorithms, motivate the problems, and give an outline of the thesis. In Chapter 2, we discuss two algorithms to approximately generate a feasible solution of the mixed packing and covering problem which is a model from convex optimization. This problem includes a large class of linear programs. The algorithms generate approximately feasible solutions within O(M(ln M+epsilon^{-2} ln epsilon^{-1})) and O(M epsilon{-2} ln (M epsilon^{-1}))$iterations, respectively, where in each iteration a block problem which depends on the specific application has to be solved. Both algorithms, applied to linear programs, can result in column generation algorithms. In Chapter 3, we implement an algorithm for the so-called max-min-resource sharing problem. This is a certain convex optimization problem which, similar to the problem in Chapter 1, includes a large class of linear programs. The implementation, which is included in the appendix, is done in C++. We use the implementation in the context of an AFPTAS for Strip Packing in order to evaluate dynamic optimization of a parameter in the algorithm, namely the step length used for interpolation. We compare our choice to the static step length proposed in the analysis of the algorithm and conclude that dynamic optimization of the step length significantly reduces the number of iterations. In Chapter 4, we study two closely related scheduling problems, namely non-preemptive scheduling with fixed jobs and scheduling with non-availability for sequential jobs on m identical machines under the makespan objective, where m is constant. For the first problem, which does not admit an FPTAS unless P=NP, we obtain a new PTAS. For the second problem, we show that a suitable restriction (namely the permanent availability of one machine) is necessary to obtain a bounded approximation ratio. For this restriction, which does not admit an FPTAS unless P=NP, we present a PTAS; we also discuss the complexity of various special cases. In total, the results are basically best possible. In Chapter 5, we continue the studies from Chapter 4 where now the number m of machines is part of the input, which makes the problem algorithmically harder. Scheduling with fixed jobs does not admit an approximation ratio better than 3/2, unless P=NP; here we obtain an approximation ratio of 3/2+epsilon for any epsilon>0. For scheduling with non-availability, we require a constant percentage of the machines to be permanently available. This restriction also does not admit an approximation ratio better than 3/2 unless P=NP; we also obtain an approximation ratio of$3/2+\epsilon$ for any epsilon>0. With an interesting argument, the approximation ratio for both problems is refined to exactly 3/2. We also point out an interesting relation of scheduling with fixed jobs to Bin Packing. As in Chapter 4, the results are in a certain sense best possible. Finally, in Chapter 6, we conclude with some remarks and open research problems

Strategic network planning in biomass-based supply chains

Fossil resources are limited and will run short. Moreover, the extensive usage of fossil resources is discussed as a key driver for climate change which means that a changeover in basic economic and ecological thinking is necessary. Especially for energy production, there has to be a movement away from the usage of fossil resources and towards renewable resources like wind, water, sun, or biomass. Within the first part of this work a structured review of recent literature on the long-term, strategic planning of biomass-based supply chains is provided. Therefore, in the first step, the overall research field bioeconomy by means of the various utilization pathways of biomass is structured and the demand-oriented view of supply chain management models and the supply-oriented view of bioeconomy are combined. In the second step, a literature review of operations research models and methods for strategic supply chain planning in biomass-based industries are provided. Thirdly, trends are identified and conclusions about research gaps are drawn. One of the identified research gaps is to make biomass-based supply chains profitable on their own, i.e., without governmental subsidies. Therefore, new optimization models are necessary, which should be as close to reality as possible, by for example considering risks and actual surrounding constraints concerning the legal framework. Within the second part of this work, an approach for strategic optimization of biogas plants considering increased flexibility is developed. Biogas plants can produce their energy flexibly and on-demand if their design is adjusted adequately. In order to achieve a flexibly schedulable biogas plant, the design of this plant has to be adapted to decouple the biogas and electricity production. Therefore, biogas storage possibilities and additional electrical capacity are necessary. The investment decision about the size of the biogas storage and the additional electrical capacity depends on the fluctuation of energy market prices and the availability of governmental subsidies. This work presents an approach supporting investment decisions to increase the flexibility of a biogas plant by installing gas storages and additional electrical capacities under consideration of revenues out of direct marketing at the day-ahead market. In order to support the strategic, long-term investment decisions, an operative plant schedule for the future, considering different plant designs given as investment strategies, using a mixed-integer linear programming (MILP) model in an uncertain environment is optimized. The different designs can be evaluated by calculating the net present value (NPV). Moreover, an analysis concerning current dynamics and uncertainties within spot market prices is executed. Furthermore, the influences concerning the variation of spot market prices compared to the influence of governmental subsidies, in particular, the flexibility premium, are revealed by computational results. Besides, the robustness of the determined solution is analyzed concerning uncertainties. The focus of the third part of the work is to consider variable substrate feeding in the mentioned optimization approach because it is expected that variable substrate feeding and thus a demand-oriented biogas production can influence the optimized plant design. In order to support this extension, an operative plant schedule for the future, considering (non-) linear technical characteristics of the biogas plant and the legal framework is optimized. Therefore, mixed-integer linear programming models with integrated approximation approaches of non-linear parts, representing the biogas production rates, are constructed. Furthermore, the influences of fluctuating spot market prices, governmental subsidies, and biomass feedstock prices on the decisions are analyzed for a fictional case example, which is based on a biogas plant in southern Germany. These numerical experiments show that variable substrate feeding can play a decisive role during the optimization of a biogas plant schedule as part of a long-term design optimization. However, the size of the strategic optimization problem makes the use of a heuristic solution algorithm necessary.Fossile Ressourcen sind begrenzt und werden zur Neige gehen. Darüber hinaus wird über die extensive Nutzung fossiler Ressourcen als wesentlicher Treiber des Klimawandels diskutiert, so dass ein Umdenken in der ökonomischen und ökologischen Grundhaltung notwendig ist. Insbesondere bei der Energieerzeugung muss eine Abkehr von der Nutzung fossiler Ressourcen und eine Ausrichtung auf erneuerbare Ressourcen wie Wind, Wasser, Sonne oder Biomasse erfolgen. Im ersten Teil dieser Arbeit wird ein strukturierter Überblick über die aktuelle Fachliteratur zur langfristigen, strategischen Planung von biomassebasierten Supply Chains gegeben. Dazu wird in einem ersten Schritt das gesamte Forschungsfeld "Bioökonomie" anhand der verschiedenen Nutzungspfade von Biomasse strukturiert und die nachfrageorientierte Sichtweise von Supply Chain Management Modellen und die angebotsorientierte Sichtweise der Bioökonomie zusammengeführt. Im zweiten Schritt wird ein Literaturüberblick über Operations-Research-Modelle und Methoden zur strategischen Supply-Chain-Planung in biomassebasierten Branchen gegeben. Im dritten Schritt werden Trends identifiziert und Schlussfolgerungen über Forschungslücken gezogen. Eine der identifizierten Forschungslücken besteht darin, biomassebasierte Supply Chains selbständig, d.h. ohne staatliche Subventionen, profitabel zu machen. Hierfür sind neue Optimierungsmodelle notwendig, die möglichst realitätsnah sein sollten, indem sie z.B. Risiken und tatsächliche Rahmenbedingungen bezüglich der rechtlichen Vorgaben berücksichtigen. Im zweiten Teil dieser Arbeit wird ein Ansatz zur strategischen Optimierung von Biogasanlagen unter Berücksichtigung einer Flexibilitätserhöhung entwickelt. Biogasanlagen können bei geeigneter Auslegung ihre Energie flexibel und bedarfsgerecht produzieren. Um eine Biogasanlage flexibel planbar zu betreiben, muss das Design dieser Anlage so angepasst werden, dass die Biogas- und Stromproduktion entkoppelt werden. Dazu sind Biogasspeichermöglichkeiten und zusätzliche elektrische Kapazität notwendig. Die Investitionsentscheidung über die Größe des Biogasspeichers und der zusätzlichen elektrischen Kapazität hängt von der Schwankung der Energiemarktpreise und der Verfügbarkeit staatlicher Fördermittel ab. Diese Arbeit stellt einen Ansatz zur Unterstützung von Investitionsentscheidungen zur Erhöhung der Flexibilität einer Biogasanlage durch die Installation von Gasspeichern und zusätzlichen elektrischen Kapazitäten unter Berücksichtigung von Erlösen aus der Direktvermarktung am Day-Ahead-Markt vor. Um die strategischen, langfristigen Investitionsentscheidungen zu unterstützen, wird ein operativer Anlagenfahrplan für die Zukunft unter Berücksichtigung verschiedener Anlagendesigns, die als Investitionsstrategien vorgegeben sind, mit Hilfe eines gemischt-ganzzahligen linearen Optimierungsmodells (MILP), unter Berücksichtigung von Unsicherheit, optimiert. Die verschiedenen Designs können durch die Berechnung des Kapitalwerts (NPV) bewertet werden. Darüber hinaus wird eine Analyse der aktuellen Dynamik und der Unsicherheiten der Spotmarktpreise durchgeführt. Darüber hinaus werden die Einflüsse der Varianz der Spotmarktpreise im Vergleich zum Einfluss staatlicher Subventionen, insbesondere der Flexibilitätsprämie, durch Berechnungsergebnisse aufgezeigt. Außerdem wird die Robustheit der ermittelten Lösung hinsichtlich der Unsicherheiten analysiert. Der Fokus des dritten Teils der Arbeit liegt auf der Berücksichtigung eines variablen Substratmanagements in dem entwickelten Optimierungsansatz, da erwartet wird, dass eine variable Substrateinspeisung und damit eine bedarfsgerechte Biogasproduktion das optimierte Anlagendesign beeinflussen kann. Um diese Erweiterung umzusetzen, wird ein operativer Anlagenfahrplan für die Zukunft unter Berücksichtigung (nicht-) linearer technischer Eigenschaften der Biogasanlage und der gesetzlichen Rahmenbedingungen optimiert. Dazu werden gemischt-ganzzahlige lineare Optimierungsmodelle mit integrierten Approximationsansätzen der nichtlinearen Anteile, welche die Biogasproduktionsraten repräsentieren, konstruiert. Des Weiteren werden die Einflüsse von schwankenden Spotmarktpreisen, staatlichen Förderungen und Biomasse-Rohstoffpreisen auf die Entscheidungen für ein fiktives Fallbeispiel, das auf einer Biogasanlage aus Süddeutschland basiert, analysiert. Die numerischen Experimente zeigen, dass die variable Substrateinspeisung bei der Optimierung des Fahrplans einer Biogasanlage im Rahmen einer langfristigen Anlagenoptimierung eine entscheidende Rolle spielen kann. Die Größe des strategischen Optimierungsproblems macht jedoch den Einsatz eines heuristischen Lösungsalgorithmus notwendig