An operating room (OR) is considered to be one of the most costly functional areas within hospitals as well as its major profit center. It is known that managing an OR department is a challenging task, which requires the integration of many actors (e.g., patients, surgeons, nurses, technicians) who may have conflicting interests and priorities. Considering these aspects, this dissertation focuses on developing a simulation based methodology for scheduling operating rooms under uncertainty, which reflects the complexity, uncertainty and variability associated with surgery. We split the process of scheduling ORs under uncertainty into two main components. First, we designed a research roadmap for modeling surgical procedure duration (from incision to wound closure) based on the surgery volume and time variability. Then, using a real surgical dataset we modeled the procedure duration using parametric and distribution-free predictive methods. We found that Support Vector Regression performs better that Generalized Linear Models increasing the prediction accuracy on unseen data by at least 5.5%. Next, we developed a simulation based methodology for scheduling ORs through a case study. For that purpose, we initially built one day feasible schedules using the 60th, 70th, 80th, and 90th percentiles to allocate surgical procedures to ORs using four different allocation policies. We then used a discrete event simulation model to evaluate the robustness of these initial feasible schedules considering the stochastic duration of all the OR activities and the arrival of surgical emergency cases. We found that on average elective waiting almost doubled the time for the emergency cases. In addition, we observed that there is not a clear effect of how being more conservative in scheduling within each scheduling policy impacts the elective waiting times. By contrast, there is a clear effect of how the scheduling policy and scheduling percentile impact the emergency waiting times. Thus, as we increase the percentile, the waiting times for emergency cases remarkably increases under half of the scheduling policies but reflects a lesser impact under scheduling the other half. OR utilization and OR overtime in a "virtual" eight operating room hospital fluctuate between 67% and 88% and 97 and 111 minutes respectively. Moreover, we noticed that both performance metrics depend not only on the levels of the scheduling policy and scheduling percentile but also are strongly affected by the increase of the emergency arrival rate. Finally, we fit a multivariate-multiple-regression model using the output of the simulation model to assess the robustness of the model and the extent to which these results can be generalized to a single, aggregate hospital goal. Further research should include a true stochastic optimization model to integrate optimization techniques into simulation analysis.

Major challenges in the management of blood supply chain are related to the shortage and wastage of the blood products. Given the perishability characteristics of blood which can be stored up to a limited number of days, if hospitals and blood centers keep an excessive number of blood units on inventory, wastages may occur. On the other hand, if sufficient number of blood units are not stored on inventory, shortages of this resource may cause the cancellations of important activities and increase the fatality rates at hospitals. Three mathematical models have been developed with the goal to improve the efficiency of blood related activities at blood centers and hospitals. The first model uses an integer programming (IP) approach to identify the optimal order levels that minimizes the total cost, shortage and wastage levels of blood products at a hospital within a specified planning horizon. The IP model explicitly considers the age of blood inventory, uncertain demand, the demand for two types of patients and crossmatch-to-transfusion ratio. The second model formulates the different shortage and inventory distribution strategies of a blood center supplying blood products to multiple hospitals. The third model develops a vehicle routing problem for blood centers to minimize the daily distance travelled by bloodmobiles during the blood collection process. Optimal routing for each bloodmobiles is identified using CPLEX solver, branch \& bound and column generation algorithms and their solution times are compared.

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.

Στην παρούσα Διπλωματική Εργασία, παρουσιάζεται η πολύ γνωστή μέθοδος Simplex. Με τη βοήθεια της μεθόδου Simplex, μπορούμε να επιλύσουμε προβλήματα γραμμικού προγραμματισμού, ακέραιου γραμμικού προγραμματισμού καθώς και διάφορες παραλλαγές των παραπάνω. Ειδικότερα για τον ακέραιο γραμμικό προγραμματισμό, παρουσιάζονται κάποιες από τις πιο γνωστές μεθόδους αναζήτησης, οι οποίες ανήκουν στην οικογένεια μεθόδων “Branch And Bound”. Επίσης κάποιες τεχνικές αναζήτησης των βέλτιστων λύσεων στο δένδρο που δημιουργείται από τις προηγούμενες τεχνικές. Τα παραπάνω υλοποιήθηκαν σε ένα λογισμικό με γραφικό περιβάλλον (GUI), το οποίο είναι συμβατό με τις περισσότερες εκδόσεις του Λειτουργικού Συστήματος, Windows της Microsoft και χωρίς να χρειάζονται κάτι επιπλέον σε έναν Προσωπικό Υπολογιστή. / This thesis presents the well-known method Simplex. With method Simplex, we can solve problems of linear programming, integer linear programming and several variants of the above. Especially for the integer linear programming, presented some of the most known search methods, which belong to the family of methods "Branch And Bound". Also presented some search techniques for optimal solutions in the tree, generated by the same techniques. These were implemented in a software with graphical interface (GUI), which is compatible with most versions of the Microsoft Windows OS, with a simple installation.

Network design decisions, especially those pertaining to urban infrastructure, are made by a central authority or network leader, and taking into consideration the network users or followers. These network decision problems are formulated as non-linear bi-level programming problems. In this work, a continuous network design problem (CNDP) and discrete network design problem (DNDP) bi-level optimization programs are proposed and solved in the context of transportation planning. The solution strategy involved reformulation and linearization as a single-level program by introducing the optimality conditions of the lower level problem into the upper level problem. For the CNDP, an alternative linearization algorithm (modified least squares partitioning, MLSPA) is proposed. MLSPA takes into consideration the current arc capacity and potential expansion to find a reduced set of planes to generalize the flow-capacity surface behavior. The concepts of flow capacity surface was introduced as a way to model of congested network and capture the effect of capacity on travel time/cost. It was found that the quality of the linear approximation depends on the goodness of fit the bottleneck arcs. The proposed approach was tested with well-known benchmark problems in transportation which yielded promising results in terms of efficiency, without sacrificing solution quality.

In manufacturing systems subject to machine and operator resource constraints the effects of rework can be profound. High levels of rework burden the resources unnecessarily and as the utilization of these resources increases the expected queuing time of work in process increases exponentially. Queuing models can help managers to understand and control the effects of rework, but often this tool is overlooked in part because of concerns over accuracy in complex environments and/or the need for limiting assumptions. One aim of this work is to increase understanding of system variables on the accuracy of simple queuing models. A queuing model is proposed that combines G/G/1 modeling techniques for rework with effective processing time techniques for machine availability and the accuracy of this model is tested under varying levels of rework, external arrival variability, and machine availability. Results show that the model performs best under exponential arrival patterns and can perform well even under high rework conditions. Generalizations are made with regards to the use of this tool for allocation of jobs to specific workers and/or machines based on known rework rates with the ultimate aim of queue time minimization.

A primary role of the Incident Command System is to learn from past incidents, as illustrated by its origins in the wildland firefighting community. Successful emergency response operations under the Incident Command System has prompted its nationwide spread, this promulgation critically relies on the system’s capability to stabilize and continuously improve various aspects of emergency response through effective organizational learning. The objective of this study is to evaluate the potential to apply fundamental principles of the Toyota Production System (Lean manufacturing) to improve learning effectiveness within the Incident Command System. An in-depth review of literature and training documents regarding both systems revealed common goals and functional similarities, including the importance of continuous improvement. While these similarities point to the validity of applying Lean principles to the Incident Command System, a focus on the systematic learning function of the Incident Command System culminated in the discovery of gaps in approaches proposed by the Incident Command System framework. As a result, recommendations are made for adjustments in systematic problem solving to adapt Lean principles of root cause analysis and emphasis on standardization of successful countermeasures to benefit the system. Future recommendations are also proposed based on the author’s understanding of the system.

This thesis studies a problem that has been proposed as a novel way to disrupt communication networks: the load maximization problem. The load on a member of a network represents the amount of communication that the member is forced to be involved in. By maximizing the load on an important member of the network, we hope to increase that member's visibility and susceptibility to capture. In this thesis we characterize load as a combinatorial property of graphs and expose possible connections between load and spectral graph theory. We specifically describe the load and how it changes in several canonical classes of graphs and determine the range of values that the load can take on. We also consider a connection between load and liquid paint flow and use this connection to build a heuristic solver for the load maximization problem. We conclude with a detailed discussion of open questions for future work.

In a dynamically monitored environment the analyst team need timely and accurate information to conduct proactive action over complex situations. Typically, there are thousands of reported activities in a real time operation, therefore steps are taken to direct the analyst’s attention to the most important activity. The data fusion community have introduced the information fusion model, with multiple situational assessments. Each process lends itself to ranking the most important activities into a predetermined order. Unfortunately, the capability of a real time system can be hindered by the knowledge limitation problem, particularly when the underlying system is processing multiple sensor information. Consequently, the situational awareness domains may not rank the identified situation as perfect, as desired by the decision-making resources. This thesis presents advanced research carried out to evaluate the ranking capability of information from the situational awareness domains: perception, comprehension and projection. The Ranking Capability Score (RCS) has been designed for evaluating the prioritisation process. The enhanced (RCS) has been designed for addressing the knowledge representation problem in the user system relation under a situational assessment where the proposed number of tracking activities are dynamically shifted. Finally, the Scheduling Capability Score was designed for evaluating the scheduling capability of the situational awareness system. The proposed performance metrics have been successful in fulfilling their objectives. Furthermore, they have been validated and evaluated using an analytical approach, through conducting a rigorous analysis of the prioritisation and scheduling processes, despite any constraints related to a domain-specific configuration.

Made available in DSpace on 2016-12-23T13:51:54Z (GMT). No. of bitstreams: 1 Cristiane Coutinho Meneguzzi.pdf: 2106158 bytes, checksum: 65c537220893be6e9c9d64b3001fef07 (MD5) Previous issue date: 2011-10-04 / On Forest field, studies in development of forest harvesting and transport still being the most emphasized subject, for being directly responsible for the final cost of wood. However, other different phases are a big potential for studies, as Forest Inventory. Information provided by the Forest Inventory are important for all planning of Forest Enterprise, as it bases any decision making involving forest resources. On this present research, was based on vehicle routing problem for planning this task. The vehicle routing problem and its variants has being largely studied on the last years, mainly for its applicability and efficiency for given solutions resulting in cost and distance reduction. The general objective of the present study is optimize the Inventory Forest planning from a vehicle routing problem and evaluate the importance of this technique on its productivity. Among the factors that influence this productivity, the spatial dispersion , basic feature of forest stands, it is one controllable factor from the use of technique that makes possible matches with planning. Studies shows that this match brings out significant results / Na área florestal, ainda é dada maior ênfase ao desenvolvimento de estudos envolvendo as etapas de colheita e transporte florestal, por serem diretamente responsáveis pelo custo final da madeira. Entretanto, diversas outras etapas possuem grande potencial para estudos, como é o caso do inventário florestal. Informações fornecidas pelo inventário florestal são importantes no planejamento de todo empreendimento florestal, pois subsidiam qualquer tomada de decisão envolvendo recursos florestais. Nesta pesquisa, utilizou-se o modelo de roteamento de veículos (PRV) no planejamento dessa atividade. O PRV e suas variantes vêm sendo amplamente estudados nos últimos anos, principalmente pela sua aplicabilidade e eficiência em gerar soluções apresentando redução de custo e/ou distâncias. O objetivo geral foi otimizar o planejamento da atividade de inventário florestal a partir de um modelo PRV e avaliar a importância do uso desta técnica no rendimento das atividades. Dentre os fatores que influenciam neste rendimento, a dispersão espacial, característica básica dos povoamentos florestais, é um fator controlável a partir do uso de técnicas que possibilitem associá-lo ao planejamento. Estudos mostram que essa associação traz resultados significativos