As air traffic congestion grows, air traffic flow management (ATFM) is becoming a great concern. ATFM deals with air traffic and the efficient utilization of the airport and airspace. Air traffic efficiency is heavily influenced by unanticipated factors, or uncertainties, which can come from several sources such as mechanical breakdown; however, weather is the main unavoidable cause of uncertainty. Because weather is unpredictable, it poses a critical challenge for ATFM in current airport and airspace operations. Convective weather results in congestion at airports as well as in airspace sectors. During times of congestion, the decision as how and when to send aircraft toward an airspace sector in the presence of weather is difficult. To approach this problem, we first propose a two-stage stochastic integer program by emphasizing a given single sector. By considering ground delay, cancellation, and cruise speed for each flight on the ground in the first stage, as well as air holding and diversion recourse actions for each flight in the air in the second stage, our model determines how aircraft are sent toward a sector under the uncertainty of weather. However, due to the large number of weather scenarios, the model is intractable in practice. To overcome the intractability, we suggest a rolling horizon method to solve the problem to near optimal. Lagrangian relaxation and subgradient method are used to justify the rolling horizon method. Since the rolling horizon method can be solved in real time, we can apply it to actual aircraft schedules to reduce the costs incurred on the ground as well as in airspace. We then extend our two-stage model to a multistage stochastic program, which increases the number of possible weather realizations and results a more efficient schedule in terms of costs. The rolling horizon method as well as Lagrangian relaxation and subgradient method are applied to this multistage model. An overall comparison among the previously described methodologies are presented.

This dissertation is a collection of previously-published manuscript and conference papers. In this dissertation, we will deal with a stochastic unit commitment problem with cooling systems for gas generators, a robust unit commitment problem with demand response and uncertain wind generation, and a power grid vulnerability analysis with transmission line switching. The latter two problems correspond to our theoretical contributions in two-stage robust optimization, i.e., how to efficiently solve a two-stage robust optimization, and how to deal with mixed-integer recourse in robust optimization. Due to copyright issue, this dissertation does not include any methodology papers written by the author during his PhD study. Readers are referred to the author's website for a complete list of publications.

The efforts to improve health care delivery usually involve studies and analysis of patient populations and healthcare systems. In this dissertation, I present the research conducted in the following areas: identifying patient groups, improving treatments for specific conditions by using statistical as well as data mining techniques, and developing new operation research models to increase system efficiency from the health institutes’ perspective. The results provide better understanding of high risk patient groups, more accuracy in detecting disease’ correlations and practical scheduling tools that consider uncertain operation durations and real-life constraints.

This research is motivated by our collaborations with a large French university teaching hospital in order to reduce the Length of Stay (LoS) of stroke patients treated in the neurovascular department. Quick diagnosis is critical for stroke patients but relies on expensive and heavily used imaging facilities such as MRI (Magnetic Resonance Imaging) scanners. Therefore, it is very important for the neurovascular department to reduce the patient LoS by reducing their waiting time of imaging examinations. From the neurovascular department perspective, this thesis proposes a new MRI examinations reservation process in order to reduce patient waiting times without degrading the utilization of MRI. The service provider, i.e., the imaging department, reserves each week a certain number of appropriately distributed contracted time slots (CTS) for the neurovascular department to ensure quick MRI examination of stroke patients. In addition to CTS, it is still possible for stroke patients to get MRI time slots through regular reservation (RTS). This thesis first proposes a stochastic programming model to simultaneously determine the contract decision, i.e., the number of CTS and its distribution, and the patient assignment policy to assign patients to either CTS or RTS. To solve this problem, structure properties of the optimal patient assignment policy for a given contract are proved by an average cost Markov decision process (MDP) approach. The contract is determined by a Monte Carlo approximation approach and then improved by local search. Computational experiments show that the proposed algorithms can efficiently solve the model. The new reservation process greatly reduces the average waiting time of stroke patients. At the same time, some CTS cannot be used for the lack of patients.To reduce the unused CTS, we further explore the possibility of the advance cancellation of CTS. Structure properties of optimal control policies for one-day and two-day advance cancellation are established separately via an average-cost MDP approach with appropriate modeling and advanced convexity concepts used in control of queueing systems. Computational experiments show that appropriate advance cancellations of CTS greatly reduce the unused CTS with nearly the same waiting times.

Šio darbo tikslas – ištirti stochastinio programavimo energetikos uždavinį. Darbe tyrinėta Lietuvos energetikos struktūra, taip pat tyrinėti optimizavimo metodai. Sudarytas Lietuvos energetikos uždavinio modelis. Optimizavimo modelis realizuotas Java programa. Optimizavimo programoje panaudotos objektinio ir lygiagretaus programavimo paradigmos. Atlikti energetikos uždavinio parametrų tyrimai. / Primary goal of this work was to research stochastic programming model of the energetic system. In this paper we also studied Lithuanian energetic sector structure and made Lithuanian energetic model. This model was realized in Java programming language. Created program is written using object-oriented and parallel programming paradigms. Also we researched different parameters influence on the model.

The focus of this dissertation is solution strategies for stochastic mixed-integer programs with special structures. Motivation for the methods comes from the relatively sparse number of algorithms for solving stochastic mixed-integer programs. Two stage models with finite support are assumed throughout. The first contribution introduces the nodal decision framework under private information restrictions. Each node in the framework has control of an optimization model which may include stochastic parameters, and the nodes must coordinate toward a single objective in which a single optimal or close-to-optimal solution is desired. However, because of competitive issues, confidentiality requirements, incompatible database issues, or other complicating factors, no global view of the system is possible. An iterative methodology called the nodal decomposition-coordination algorithm (NDC) is formally developed in which each entity in the cooperation forms its own nodal deterministic or stochastic program. Lagrangian relaxation and subgradient optimization techniques are used to facilitate negotiation between the nodal decisions in the system without any one entity gaining access to the private information from other nodes. A computational study on NDC using supply chain inventory coordination problem instances demonstrates that the new methodology can obtain good solution values without violating private information restrictions. The results also show that the stochastic solutions outperform the corresponding expected value solutions. The next contribution presents a new algorithm called scenario Fenchel decomposition (SFD) for solving two-stage stochastic mixed 0-1 integer programs with special structure based on scenario decomposition of the problem and Fenchel cutting planes. The algorithm combines progressive hedging to restore nonanticipativity of the first-stage solution, and generates Fenchel cutting planes for the LP relaxations of the subproblems to recover integer solutions. A computational study SFD using instances with multiple knapsack constraint structure is given. Multiple knapsack constrained problems are chosen due to the advantages they provide when generating Fenchel cutting planes. The computational results are promising, and show that SFD is able to find optimal solutions for some problem instances in a short amount of time, and that overall, SFD outperforms the brute force method of solving the DEP.

Šio darbo tikslas ištirti mažų įmonių padėtį Lietuvos rinkoje, susipažinti su trumpalaikio finansų planavimo problemomis. Sukurti taikomąją programą, kuri padėtų šias problemas spręsti ir pasiūlyti optimalų finansų planą. Darbe nagrinėti rinkoje taikomi trumpalaikiai finansų planavimo modeliai mažoms įmonėms bei dažniausiai kylančias problemas. Pasirinkti tinkami matematinius optimizavimo metodai. Sudarytas matematinis uždavinio modelis. Parašytas kodas, skirtas optimizavimo uždaviniams spręsti C# kalba. Realizuota ir ištestuoti programa. / Primary goal of this work is to investigate the position of small business in the Lithuanian market, access to short-term financial planning issues. Create an application that will help solve these problems and propose an optimal financial plan. The paper analyze short-term financial planning models for small businesses which exists in the market and the most common issues. The appropriate mathematical optimization methods were chosen. Mathematical model of the task was described. Code for solving optimization problems was written in C # language. Program was realized and tested.

In view of the current situation of fluctuating high crude oil prices, it is now more important than ever for petroleum refineries to operate at an optimal level in the present dynamic global economy. Acknowledging the shortcomings of deterministic models, this work proposes a hybrid of stochastic programming formulations for an optimal midterm refinery planning that addresses three factors of uncertainties, namely price of crude oil and saleable products, product demand, and production yields. An explicit stochastic programming technique is utilized by employing compensating slack variables to account for violations of constraints in order to increase model tractability. Four approaches are considered to ensure both solution and model robustness: (1) the Markowitz???s mean???variance (MV) model to handle randomness in the objective coefficients of prices by minimizing variance of the expected value of the random coefficients; (2) the two-stage stochastic programming with fixed recourse approach via scenario analysis to model randomness in the right-hand side and left-hand side coefficients by minimizing the expected recourse penalty costs due to constraints??? violations; (3) incorporation of the MV model within the framework developed in Approach 2 to minimize both the expectation and variance of the recourse costs; and (4) reformulation of the model in Approach 3 by adopting mean-absolute deviation (MAD) as the risk metric imposed by the recourse costs for a novel application to the petroleum refining industry. A representative numerical example is illustrated with the resulting outcome of higher net profits and increased robustness in solutions proposed by the stochastic models.

The research subject of the dissertation is the analysis of the model of heterogenous agents and its application for modelling stochastic Nash and Stackelberg equilibriums, applying the Monte Carlo method. The aim of the dissertation is to identify the impact of heterogeneous agents on the formation of the economic bubble, to create and examine algorithms for special bilevel stochastic programming problems and for search of the stochastic Nash equilibrium, applying the Monte Carlo method. The thesis offers a mathematical model for identification of the beginning of the bubble. This model has been applied for the analysis of the real estate bubble in Lithuania. In cases of uncertainty, decisions are often made by several individuals whose interests do not coincide. In such situations one of the concepts of the equilibrium is the stochastic Nash equilibrium. The dissertation examines the stochastic Nash equilibrium and offers the algorithm for gradient search of this equilibrium. The algorithm for gradient search of the stochastic Nash equilibrium was examined by solving the problem of electricity market with precedent agreements. The dissertation offers the algorithm for solving the optimization problem where the objective function and constraints contain conditional value at risk and by solving the test problem the behaviour of the algorithm is investigated. The dissertation proposes the algorithm for solving the two stage stochastic linear problem, employing the method of... [to full text] / Disertacijos objektas – heterogeninių agentų modelio tyrimas ir taikymas stochastinėms Nešo ir Stakelbergo pusiausvyroms modeliuoti Monte Karlo metodu. Darbo tikslas – nustatyti heterogeninių agentų įtaką ekonominio burbulo susidarymui, sukurti ir ištirti dviejų lygių stochastinio programavimo specialių uždavinių bei stochastinės Nešo pusiausvyros paieškos Monte Karlo algoritmus. Netvarių būsenų (burbulų ir jų griūčių) identifikavimas labai svarbus ekonomikai bei finansams. Disertacijoje pateiktas burbulo pradžios identifikavimo matematinis modelis, kurį taikant buvo ištirtas Lietuvos nekilnojamojo turto burbulas. Esant neapibrėžtumui, sprendimus dažnai priima keli individai, kurių interesai nesutampa. Tokiose situacijose taikoma viena iš pusiausvyros koncepcijų, būtent, stochastinė Nešo pusiausvyra. Darbe ištirta stochastinė Nešo pusiausvyra ir pasiūlytas jos gradientinės paieškos algoritmas. Stochastinės Nešo pusiausvyros gradientinės paieškos algoritmas ištirtas sprendžiant elektros rinkos su išankstiniais sandoriais uždavinį. Optimizavimo uždavinys, kurio tikslo funkcijoje ir ribojimuose yra sąlyginės rizikos reikšmė yra dviejų lygių stochastinio programavimo uždavinys. Disertacijoje pasiūlytas tokio uždavinio sprendimo algoritmas ir testiniu uždaviniu ištirta jo elgsena. Jei stochastinis dviejų etapų tiesinis uždavinys sprendžiamas reikšmingų imčių metodu, tai gaunamas dviejų lygių stochastinio programavimo uždavinys. Disertacijoje pasiūlytas stochastinio dviejų etapų... [toliau žr. visą tekstą]

In an attempt to reduce costs and improve customer satisfaction, manufacturers have been adopting strategies such as Delayed Product Differentiation (DPD) while managing broader product lines. In this study, first a general framework on DPD is formed in the light of basic articles in the literature. The vanilla box assembly process which is a special form of modular design type of DPD is modeled and analyzed. In the vanilla box assembly process, inventory is stored in a special form of semi-finished products, called vanilla boxes, that can serve more than one final product. We model the vanilla box assembly process considering the costs of inventory and unsatisfied demand under the capacity limitations, stochastic demand and bill of material requirements. We formulate the model as an extensive form of stochastic integer program in which stochastic demand is modeled using a set of demand scenarios each of which is assigned a probability of occurrence. The model is solved as a standard integer programming model that minimizes the expected value of the objective function. The impact of product demand scenarios, common component levels, shortage penalty cost to holding cost ratio levels and capacity restrictions on the total cost and fill rates is studied. We compare the performance of vanilla box assembly process to assemble-to-order process and provide insights on their performances. Computational results indicate that the vanilla box assembly process is a promising alternative to the assemble-to-order process in most of the problem instances.