Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 216-220). Also available via the Internet.

The diploma thesis deals with using discrete event simulation as a tool for supporting decision-making process in the company with usage of simulation software Plant Simulation. This work briefly introduces business process modeling and simulation problems. In the diploma thesis there two varieties two varieties of supplying parts, compared with each other in a process view and there is a particular proposal solution, including the economical evaluation.

The purpose of this work is to provide simulation-based support for making optimal (or near-optimal) decisions in situations where decision makers are faced with imperfect information. We develop several novel techniques and algorithms for simulation-based optimization and decision support and apply them to two categories of problems: (i) Unmanned Aerial Vehicle (UAV) path planning in search operations, and; (ii) optimization of business process models. Common features of these two problems for which analytical approaches are not available, are the presence of imperfect information and their inherent complexity. In the UAV path planning problem, the objective is to define the path of a UAV searching for a target on a known road network. It is assumed that the target is moving toward a goal and we have some uncertain information about the start point of the target, its velocity, and the final goal of the target. The target does not take evasive action to avoid being detected. The UAV is equipped with a sensor, which may detect the target once it is in the sensor’s scope. Nevertheless, the detection process is uncertain and the sensor is subject to both false-positive and false-negative errors. We propose three different solutions, two of which are simulation-based. The most promising solution is an on-line simulation-based method that estimates the location of the target by using a Sequential Monte Carlo (SMC) method. During the entire mission, different UAV paths are simulated and the one is chosen that most reduces the uncertainty about the location of the target. In the optimization of the business process models, several different but related problems are addressed: (i) we define a measure of performance for a business process model based on the value added by agents (employees) to the process; (ii) we use this model for optimization of the business process models. Different types of processes are distinguished and methods for finding the optimal or near-optimal solutions are provided; (iii) we propose a model for estimating the performance of collaborative agents. This model is used to solve a class of Assignment Problems (AP), where tasks are assigned to collaborative agents; (iv) we propose a model for team activity and the performance of a team of agents. We introduce different collaboration strategies between agents and a negotiation algorithm for resolving conflicts between agents. We compare the effect of different strategies on the output of the team. Most of the studied cases are complex problems for which no analytical solution is available. Simulation methods are successfully applied to these problems. They are shown to be more general than analytical models for handling uncertainty since they usually have fewer assumptions and impose no restrictions on the probability distributions involved. Our investigation confirms that simulation is a powerful tool for providing decision-making support. Moreover, our proposed algorithms and methods in the accompanying articles contribute to providing support for making optimal and in some cases near-optimal decisions: (i) our tests of the UAV simulation-based search methods on a simulator show that the on-line simulation method has generally a high performance and detects the target in a reasonable time. The performance of this method was compared with the detection time when the UAV had the exact information about the initial location of the target, its velocity, and its path (minimum detection time). This comparison indicated that the online simulation method in many cases achieved a near-optimal performance in the studied scenario; (ii) our business process optimization framework combines simulation with the Hungarian method and finds the optimal solution for all cases where the assignment of tasks does not change the workflow of the process. For the most general cases, where the assignment of tasks may change the workflow, we propose an algorithm that finds near-optimal solutions. In this algorithm, simulation, which deals with the uncertainty in the process, is combined with the Hungarian method and hill-climbing heuristics. In the study of assigning tasks to collaborative agents we suggest a Genetic Algorithm (GA) that finds near-optimal solutions with a high degree of accuracy, stability, scalability and robustness. While investigating the effect of different agent strategies on the output of a team, we find that the output of a team is near-optimal, when agents choose a collaboration strategy that follows the principle of least effort (Zipf’s law) and use our suggested algorithm for negotiation and resolving conflicts. / QC 20111202

Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Christos Alexopoulos; Committee Co-Chair: Mark Ferguson; Committee Member: Dave Goldsman; Committee Member: Hayriye Ayhan; Committee Member: Paul Griffin. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (M. Eng.)(Mechanical)--University of Pretoria, 2005. / Title from opening screen (viewed Mar. 20, 2006). Includes summary. Mode of access: World Wide Web.

Water quality management (WQM) significantly affects water use and ecosystem health, which is helpful for achieving sustainability in environmental and economic aspects. However, the implementation of water quality management is still challenging in practice due to the uncertainty and nonlinearity existing in water systems, as well as the difficulty of the integration of simulation and optimization analyses. Therefore, effective optimization frameworks for handling nonlinearity, various uncertainties, and integrated complex water quality simulation models are highly desired. This dissertation tries to address such challenges by proposing new efficient hybrid inexact optimization models for water quality management under uncertainty through: i) developing an interval quadratic programming (IQP) model for handling both nonlinearity and uncertainty expressed as intervals for water quality management, and solving the developed model by three algorithms to compare and investigate the most effective and straightforward solution algorithm for IQP-WQM problems; ii) developing a simulation-based interval chance-constrained quadratic programming model, which is able to deal with nonlinearity and uncertainties with multiple formats, and implementing a real-world case study of phosphorus control in the central Grand River, Ontario, Canada; iii) proposing a data-driven interval credibility constrained quadratic programming model for water quality management by utilizing a data-driven surrogate model (i.e., inexact linear regression) to incorporate a complex water quality simulation model with the optimization framework to overcome challenges from the integrated simulation-optimization. The performance of the proposed frameworks/models was tested by different case studies and various mathematical techniques (e.g., sensitivity analysis). The results indicate the proposed models are capable of dealing with nonlinearity and various uncertainties, and significantly reducing the computational burden from simulation-optimization analysis. Coupling such efforts in developing efficient hybrid inexact optimization models for water quality management under uncertainty can provide useful tools to solve large-scale complex water quality management problems in a robust manner, and further provide reliable and effective decision supports for water quality planning and management. / Thesis / Doctor of Philosophy (PhD) / Water quality management plays a key role in facilitating environmental and economic sustainability. However, many challenges still exist in practical water quality management problems, such as various uncertainties and complexities, as well as complicated integrated simulation-optimization analysis. Therefore, the goal of this dissertation is to address such challenges by developing a set of efficient hybrid inexact optimization models for water quality management under uncertainty through: i) developing an interval quadratic programming model for water quality management, and investigating its effective and straightforward solution algorithms; ii) leveraging the power of data-driven modeling and proposing efficient data-driven optimization models based on hybrid inexact programming for water quality management. Robust and effective water quality planning schemes for large-scale water quality management problems can be obtained based on the proposed frameworks/models.

Major League Baseball (MLB) is a 30-team North American professional baseball league and Minor League Baseball (MiLB) is the hierarchy of developmental professional baseball teams for MLB. Most MLB players first develop their skills in MiLB, and MLB teams employ scouts, experts who evaluate the strengths, weaknesses, and overall potential of these players. In this dissertation, we study the problem of designing a scouting network for a Major League Baseball (MLB) team. We introduce the problem to the operations research literature to help teams make strategic and operational level decisions when managing their scouting resources. The thesis consists of three chapters that aim to address decisions such as how the scouts should be assigned to the available MiLB teams, how the scouts should be routed around the country, how many scouts are needed to perform the major scouting tasks, are there any trade-off s between the scouting objectives, and if there are any, what are the outcomes and insights. In the first chapter, we study the problem of assigning and scheduling minor league scouts for Major League Baseball (MLB) teams. There are multiple objectives in this problem. We formulate the problem as an integer program, use decomposition and both column-generation-based and problem-specific heuristics to solve it, and evaluate policies on multiple objective dimensions based on 100 bootstrapped season schedules. Our approach can allow teams to improve operationally by finding better scout schedules, to understand quantitatively the strategic trade-offs inherent in scout assignment policies, and to select the assignment policy whose strategic and operational performance best meets their needs. In the second chapter, we study the problem under uncertainty. In reality we observe that there are always disruptions to the schedules: players are injured, scouts become unavailable, games are delayed due to bad weather, etc. We presented a minor league baseball season simulator that generates random disruptions to the scout's schedules and uses optimization based heuristic models to recover the disrupted schedules. We evaluated the strategic benefits of different policies for team-to-scout assignment using the simulator. Our results demonstrate that the deterministic approach is insufficient for evaluating the benefits and costs of each policy, and that a simulation approach is also much more effective at determining the value of adding an additional scout to the network. The real scouting network design instances we solved in the first two chapters have several detailed complexities that can make them hard to study, such as idle day constraints, varying season lengths, off days for teams in the schedule, days where some teams play and others do not, etc. In the third chapter, we analyzed a simplified version of the Single Scout Problem (SSP), stripping away much of the real-world complexities that complicate SSP instances. Even for this stylized, archetypal version of SSP, we find that even small instances can be computationally difficult. We showed by reduction from Minimum Cost Hamiltonian Path Problem that archetypal version of SSP is NP-complete, even without all of the additional complexity introduced by real scheduling and scouting operations.

ABSTRACT The aim of industrial production changed from mass production at the beginning of the 20th century. Today, production flexibility determines manufacturing companies' course of action. In this sense, Volvo Group Trucks Operations is interested in meeting customer demand in their assembly lines by adjusting manpower. Thus, this investigation attempts to analyze the effect of manning on the main final assembly line for thirteen-liter heavy-duty diesel engines at Volvo Group Trucks Operations in Skövde by means of discrete-event simulation. This project presents a simulation model that simulates the assembly line. With the purpose of building the model data were required. One the one hand, qualitative data were collected to improve the knowledge in the fields related to the project topic, as well as to solve the lack of information in certain points of the project. On the other hand, simulation model programming requires quantitative data. Once the model was completed, simulation results were obtained through simulation-based optimization. This optimization process tested 50,000 different workforce scenarios to find the most efficient solutions for three different sequences. Among all results, the most interesting one for Volvo is the one which render 80% of today’s throughput with the minimum number of workers. Consequently, as a case study, a bottleneck analysis and worker performance analysis was performed for this scenario. Finally, a flexible and fully functional model that delivers the desired results was developed. These results provide a comparison among different manning scenarios considering throughput as main measurement of the main final assembly line performance. After analyzing the results, system output behavior was revealed. This behavior allows predicting optimal system output for a given number of operators.

Underwater acoustic channel models have been studied in the context of communication and sonar applications. Acoustic propagation channel in an underwater environment exhibits multipath, time-variability and Doppler eects. In this thesis, multipath fading channel models, underwater physical properties and sound propagation characteristics are studied. An underwater channel model for sonar applications is proposed. In the proposed model, the physical characteristics of underwater environment are considered in a comprehensive manner. Experiments /simulations were carried out using real-life data. Model parameters are estimated for a specific location, scenario and physical conditions. The channel response is approximated by fitting the model output to the recorded data. The optimization and estimation are conducted in frequency domain using Mean Square Error criterion.

Critical areas in a hospital, such as Intensive Care Units (ICUs) and isolation rooms, are designed to strict health standards. More often than not, these areas operate continuously to maintain designed indoor conditions in order to ensure the safety of patients, making them energy intensive areas. Several attempts have been made to design them to be more energy-efficient. However, cases have emerged in hot and humid countries like Malaysia where combination of poor design, operation and maintenance practices, exacerbated by the humid outdoor conditions especially during night time, have led to occurrences of mould growth in these critical areas. A question arise whether energy efficient design of a critical area can be achieved without incurring a risk of mould growth due to factors like moisture transfer, or continuous part load operation of HVAC systems. The objective of research in this thesis is to investigate the trade-off between optimizing the building and HVAC systems and minimizing the risk of mould growth in hospital buildings located in hot and humid climates. The problem formulation is a single zone isolation room with dimensions based from a real-life isolation room of a district hospital in Malaysia. The design variables, namely HVAC systems and the details of building constructions were selected as input files for energy performance evaluation using EnergyPlus. The output from the simulation will be compared with the selected existing mould growth model during post processing to determine the optimum solution. Simulation and the generation of solutions will be repeated until the most optimum solution is achieved. A binary-encoded Genetic Algorithm (GA) was used as an approach to the minimisation of hospital building energy use. The GA is proven to be effective in performing multi-objective optimisation, since the objective functions for this research are more than one; namely, the minimum annual energy use in the isolation room and the critical indoor surface conditions, such as temperature and relative humidity, below which there would be no mould growth. The research has shown that the normal practice of isolation room design for Malaysian hospitals does not work in minimising energy use and minimising the risk of mould growth and a new design guideline for isolation rooms in Malaysia is recommended. The principal originality of the research will be the application of optimisation methods to investigate the relationship, or trade-off between energy use and the risk of mould growth, particularly for hospital buildings in a hot and humid climate. In this respect, the new knowledge will be on the optimisation procedure and required modelling/analysis components. This combinatorial approach would serve as decision making tool for building and HVAC systems designers in designing more energy-efficient overall environment systems in hospitals, with particular attention to critical areas that are operating continuously.