Thesis (M.S.)--Ohio University, June, 1996. / Title from PDF t.p.

<p>The harsh competition in the airline industry continuously forces airline carriers to streamline their production and cut back on costs. Manpower constitutes the largest expense in Scandinavian Airline System, closely followed by fuel costs. Thus effective crew planning is vital to face the competition from international actors and low cost carriers.</p><p>Creating efficient schedules for airline crew is a very complex combinatorial task and the process is heavily dependent on optimization. A large set of constraints comprised of union- and governmental rules as well as company policies and quality factors must be taken into consideration when the schedules are created.</p><p>This master thesis examines how the distribution of rank in the SAS international pilot corps affects the total cost associated with flight deck crew.</p><p>Long haul flights at SAS intercontinental are manned with a captain, a first officer and a relief pilot. Pilots may man lower ranking positions on any given flight in order to make efficient use of the pilot corps and to minimize the need of full time equivalents.</p><p>This work discusses the development and evaluation of a simulation environment developed in order to create and analyze fictitious crew populations with different distributions of rank. Furthermore the solution methods to the scheduling problem implemented at SAS and the optimization theory associated with them are discussed.</p><p>The project has resulted in an evaluation of the developed simulation environment and a discussion about the difficulties of analyzing crew populations with the systems currently in use at SAS.</p>

The harsh competition in the airline industry continuously forces airline carriers to streamline their production and cut back on costs. Manpower constitutes the largest expense in Scandinavian Airline System, closely followed by fuel costs. Thus effective crew planning is vital to face the competition from international actors and low cost carriers. Creating efficient schedules for airline crew is a very complex combinatorial task and the process is heavily dependent on optimization. A large set of constraints comprised of union- and governmental rules as well as company policies and quality factors must be taken into consideration when the schedules are created. This master thesis examines how the distribution of rank in the SAS international pilot corps affects the total cost associated with flight deck crew. Long haul flights at SAS intercontinental are manned with a captain, a first officer and a relief pilot. Pilots may man lower ranking positions on any given flight in order to make efficient use of the pilot corps and to minimize the need of full time equivalents. This work discusses the development and evaluation of a simulation environment developed in order to create and analyze fictitious crew populations with different distributions of rank. Furthermore the solution methods to the scheduling problem implemented at SAS and the optimization theory associated with them are discussed. The project has resulted in an evaluation of the developed simulation environment and a discussion about the difficulties of analyzing crew populations with the systems currently in use at SAS.

Thesis (M.S.)--Mississippi State University. Department of Industrial Engineering. / Title from title screen. Includes bibliographical references.

Renewable energy (RE) resources are relatively unpredictable and dependent on climatic conditions. The negative effects of existing randomness in RE resources can be reduced by the integration of RE resources into what is called Hybrid Renewable Energy Systems (HRES). The design of HRES remains as a complicated problem since there is uncertainty in energy prices, demand, and RE sources. In addition, it is a multi-objective design since several conflicting objectives must be considered. In this thesis, an optimal sizing approach has been proposed to aid decision makers in sizing and performance analysis of this kind of energy supply systems. First, a straightforward methodology based on ε-constraint method is proposed for optimal sizing of HRESs containing RE power generators and two storage devices. The ε-constraint method has been applied to minimize simultaneously the total net present cost of the system, unmet load, and fuel emission. A simulation-based particle swarm optimization approach has been used to tackle the multi-objective optimization problem. In the next step, a Pareto-based search technique, named dynamic multi-objective particle swarm optimization, has been performed to improve the quality of the Pareto front (PF) approximated by the ε-constraint method. The proposed method is examined for a case study including wind turbines, photovoltaic panels, diesel generators, batteries, fuel cells, electrolyzers, and hydrogen tanks. Well-known metrics from the literature are used to evaluate the generated PF. Afterward, a multi-objective approach is presented to consider the economic, reliability and environmental issues at various renewable energy ratio values when optimizing the design of building energy supply systems. An existing commercial apartment building operating in a cold Canadian climate has been described to apply the proposed model. In this test application, the model investigates the potential use of RE resources for the building. Furthermore, the application of plug-in electric vehicles instead of gasoline car for transportation is studied. Comparing model results against two well-known reported multi-objective algorithms has also been examined. Finally, the existing uncertainties in RE and load are explicitly incorporated into the model to give more accurate and realistic results. An innovative and easy to implement stochastic multi-objective approach is introduced for optimal sizing of an HRES. / February 2016

Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. There is usually too much information to process to make the best decisions. Decision support systems can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. We study these complex emergency management, approaching the problem with discrete optimization. Using our operational research knowledge to model mass casualty incidents, we seek to provide solutions and insights for the emergency managers. This dissertation proposes a novel deterministic model to optimize the casualty transportation and treatment decisions in response to a MCI. This deterministic model expands on current state of the art by; (1) including multiple dynamic resources that impact the various interconnected decisions, (2) further refining a survival function to measure expected survivors, (3) defining novel objective functions that consider competing priorities, including maximizing survivors and balancing equity, and finally (4) developing a MCI response simulation that provides insights to how optimization models could be used as decision-support mechanisms. / Doctor of Philosophy / Emergency managers are often faced with the toughest decisions that can ever be made, people's lives hang in the balance. Nevertheless, these tough decisions have to be made, and made quickly. But to make the best decisions, there is usually too much information to process. Computers and support tools can relieve a significant amount of this onus, making decision while considering the complex interweaving of constraints and resources that define the boundary of the problem. This dissertation provides a mathematical model that relates the important decisions made during a MCI response with the limited resources of the surrounding area. This mathematical model can be used to determine the best response decisions, such as where to send casualties and when to treat them. This model is also used to explore ideas of fairness and equity in casualty outcomes and examine what may lead in unfair response decisions. Finally, this dissertation uses a simulation to understand how this model could be used to not only plan the response, but also update the plan as you learn new information during the response roll-out.

abstract: Flooding is a critical issue around the world, and the absence of comprehension of watershed hydrologic reaction results in lack of lead-time for flood forecasting and expensive harm to property and life. It happens when water flows due to extreme rainfall storm, dam breach or snowmelt exceeds the capacity of river system reservoirs and channels. The objective of this research was to develop a methodology for determining a time series operation for releases through control gates of river-reservoir systems during flooding events in a real-time using one- and/or two-dimensional modeling of flows through river-reservoir systems. The optimization-simulation methodology interfaces several simulation-software coupled together with an optimization model solved by genetic algorithm coded in MATLAB. These software include the U.S. Army Corps of Engineers HEC-RAS linked the genetic algorithm in MATLAB to come up with an optimization-simulation model for time series gate openings to control downstream elevations. The model involves using the one- and two-dimensional ability in HEC-RAS to perform hydrodynamic routing with high-resolution raster Digital Elevation Models. Also, the model uses both real-time gridded- and gaged-rainfall data in addition to a model for forecasting future rainfall-data. This new model has been developed to manage reservoir release schedules before, during, and after an extraordinary rainfall event that could cause extreme flooding. Further to observe and control downstream water surface elevations to avoid exceedance of threshold of flood levels in target cells in the downstream area of study, and to minimize the damage and direct effects in both the up and downstream. The application of the complete optimization-simulation model was applied to a portion of the Cumberland River System in Nashville, Tennessee for the flooding event of May 2010. The objective of this application is to demonstrate the applicability of the model for minimizing flood damages for an actual flood event in real-time on an actual river basin. The purpose of the application in a real-time framework would be to minimize the flood damages at Nashville, Tennessee by keeping the flood stages under the 100-year flood stage. This application also compared the three unsteady flow simulation scenarios: one-dimensional, two-dimensional and combined one- and two-dimensional unsteady flow. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019

This doctoral thesis investigates the application of simulation-based optimization (SBO) as an alternative to conventional optimization techniques when the inherent uncertainty and complex features of real manufacturing systems need to be considered. Inspired by a real-world production planning setting, we provide a general formulation of the situation as an extended knapsack problem. We proceed by proposing a solution approach based on single and multi-objective SBO models, which use simulation to capture the uncertainty and complexity of the manufacturing system and employ meta-heuristic optimizers to search for near-optimal solutions. Moreover, we consider the design of matheuristic approaches that combine the advantages of population-based meta-heuristics with mathematical programming techniques. More specifically, we consider the integration of mathematical programming techniques during the initialization stage of the single and multi-objective approaches as well as during the actual search process. Using data collected from a manufacturing company, we provide evidence for the advantages of our approaches over conventional methods (integer linear programming and chance-constrained programming) and highlight the synergies resulting from the combination of simulation, meta-heuristics and mathematical programming methods. In the context of the same real-world problem, we also analyse different single and multi-objective SBO models for robust optimization. We demonstrate that the choice of robustness measure and the sample size used during fitness evaluation are crucial considerations in designing an effective multi-objective model.

In this dissertation, we focus on developing sampling-based algorithms for solving stochastic linear programs. The work covers both two stage and multistage versions of stochastic linear programs. In particular, we first study the two stage stochastic decomposition (SD) algorithm and present some extensions associated with SD. Specifically, we study two issues: a) are there conditions under which the regularized version of SD generates a unique solution? and b) in cases where a user is willing to sacrifice optimality, is there a way to modify the SD algorithm so that a user can trade-off solution times with solution quality? Moreover, we present our preliminary approach to address these questions. Secondly, we investigate the multistage stochastic linear programs and propose a new approach to solving multistage stochastic decision models in the presence of constraints. The motivation for proposing the multistage stochastic decomposition algorithm is to handle large scale multistage stochastic linear programs. In our setting, the deterministic equivalent problems of the multistage stochastic linear program are too large to be solved exactly. Therefore, we seek an asymptotically optimum solution by simulating the SD algorithmic process, which was originally designed for two-stage stochastic linear programs (SLPs). More importantly, when SD is implemented in a time-staged manner, the algorithm begins to take the flavor of a simulation leading to what we refer to as optimization simulation. As for multistage stochastic decomposition, there are a couple of advantages that deserve mention. One of the benefits is that it can work directly with sample paths, and this feature makes the new algorithm much easier to be integrated within a simulation. Moreover, compared with other sampling-based algorithms for multistage stochastic programming, we also overcome certain limitations, such as a stage-wise independence assumption.

Das zu entwickelnde Konzept zielt darauf ab, ausgehend von definierten Produktionsverfahren, -prozessen und den dafür qualitativ bestimmten Maschinen und Anlagen Erkenntnisse zur notwendigen Anzahl und den resultierenden Kosten zu ermitteln. Mit der Entwicklung des Konzepts soll ein Beitrag zur Schaffung eines unternehmensziel- und strategiekonformen Wertschöpfungsprozesses über alle Bereiche des Produktionsmanagements produzierender Unternehmen geleistet werden. Den Kern des Konzepts bildet die dynamische Dimensionierung, die Belastungsänderungen des Produktionssystems über die Zeit berücksichtigt. Der Schwerpunkt liegt dabei auf der Gestaltung eines wirtschaftlich sinnvollen Maßes an Flexibilität und Wandlungsfähigkeit. Weiterhin wird eine Automatisierung des Planungsprozesses in Verbindung mit dem Einsatz von Optimierungstechniken und Kostensimulation angestrebt. Anhand unterschiedlicher Szenarien erfolgt eine Gegenüberstellung des neu entwickelten Konzepts mit bestehenden Verfahren. / The concept which has been developed is based on defined production methods and production processes which are necessary for quality machinery and equipment in order to identify specifically, the number of machines required and the resulting costs. The developed concept contributes to creating a business goal and strategy-driven value creation process in all areas of production management of the manufacturing company. Since the core concept is dynamic dimensioning, it is imperative to take into account the load of the production system and how it changes over time; and is therefore both flexible and adaptable. Furthermore, automation of the planning process in conjunction with the use of optimization techniques and simulation cost is sought. Different scenarios allow the comparison of newly developed concepts with a variety of procedures which already exist.