Operations Research, Systems Engineering and Industrial Engineering Commons^™

Adaptive Sampling Trust-Region Methods For Derivative-Based And Derivative-Free Simulation Optimization Problems, Sara Shashaani

Open Access Dissertations

We consider unconstrained optimization problems where only “stochastic” estimates of the objective function are observable as replicates from a Monte Carlo simulation oracle. In the first study we assume that the function gradients are directly observable through the Monte Carlo simulation. We propose ASTRO, which is an adaptive sampling based trust-region optimization method where a stochastic local model is constructed, optimized, and updated iteratively. ASTRO is a derivative-based algorithm and provides almost sure convergence to a first-order critical point with good practical performance. In the second study the Monte Carlo simulation is assumed to provide no direct observations of the …

Simulation Modeling For Energy Consumption Of Residential Consumers In Response To Demand Side Management., Prajwal Khadgi

Electronic Theses and Dissertations

Energy efficiency in the electricity distribution system continues to gain importance as demand for electricity keeps rising and resources keep diminishing. Achieving higher energy efficiency by implementing control strategies and demand response (DR) programs has always been a topic of interest in the electric utility industry. The advent of smart grids with enhanced data communication capabilities propels DR to be an essential part of the next generation power distribution system. Fundamentally, DR has the ability to charge a customer the true price of electricity at the time of use, and the general perception is that consumers would shift their load …

Predicting The Performance Of Queues: A Data Analytic Approach, Kum Khiong Yang, Cayirli Tugba, Mei Wan Low

Research Collection Lee Kong Chian School Of Business

Existing models of multi-server queues with system transience and non-standard assumptions are either too complex or restricted in their assumptions to be used broadly in practice. This paper proposes using data analytics, combining computer simulation to generate the data and an advanced non-linear regression technique called the Alternating Conditional Expectation (ACE) to construct a set of easy-to-use equations to predict the performance of queues with a scheduled start and end time. Our results show that the equations can accurately predict the queue performance as a function of the number of servers, mean arrival load, session length and service time variability. …

Automatic Sleep Stages Classification, Maryam Zokaeinikoo

Masters Theses

In this thesis, we first develop an efficient automated classification algorithm for sleep stages identification. Polysomnography recordings (PSGs) from twenty subjects were used in this study and features were extracted from the time{frequency representation of the electroencephalography (EEG) signal. The classification of the extracted features was done using random forest classifier. The performance of the new approach is tested by evaluating the accuracy of each sleep stages and total accuracy. The results shows improvement in all five sleep stages compared to previous works.

Then, we present a simulation decision algorithm for switching between sleep interventions. This method improves the percentage …

Optimal Disposition Decisions For A Remanufacturing System Considering Time Value Of Products, Mostafa Pazoki, Walid Abdul-Kader

Industrial and Manufacturing Systems Engineering Publications

This paper studies disposition decisions of cores where the value of returns deteriorates over time. Mainly in disposition decisions, a remanufacturer is interested to determine how many units to remanufacture and to salvage. To address this research problem along with value deterioration of returns, a rough-cut mathematical model is developed by considering various parameters of interest such as selling price, salvage value and remanufacturing rate with the aim to maximize total profit. Due to uncertainty limitations, the model can provide decision-makers with relevant insights about disposition decisions. Simulation modeling techniques are used to validate the proposed model. Numerical examples are …

A System-Of-Systems Framework For Assessment Of Resilience In Complex Construction Projects, Jin Zhu

FIU Electronic Theses and Dissertations

Uncertainty is a major reason of low efficiency in construction projects. Traditional approaches in dealing with uncertainty in projects focus on risk identification, mitigation, and transfer. These risk-based approaches may protect projects from identified risks. However, they cannot ensure the success of projects in environments with deep uncertainty. Hence, there is a need for a paradigm shift from risk-based to resilience-based approaches. A resilience-based approach focuses on enhancing project resilience as a capability to cope with known and unknown uncertainty. The objective of this research is to fill the knowledge gap and create the theory of resilience in the context …

Operational Decision Making For Medical Clinics Through The Use Of Simulation And Multi-Attribute Utility Theory., Bo Sun

Electronic Theses and Dissertations

Currently, health care is a large industry that concerns everyone. Outpatient health care is an important part of the American health care system and is one of the strongest growth areas in the health care system. Many people pay attention to how to keep basic health care available to as many people as possible. A large health care system is usually evaluated by many performance measures. For example, the managers of a medical clinic are concerned about increasing staff utilization; both managers and patients are concerned about patient waiting time. In this dissertation, we study decision making for clinics in …

A Simulation Model For Decision Support In Business Continuity Planning, Marissa Anne Mosunich

Master's Theses

Enterprises with a global supply network are at risk of lost revenue as a result of disruptive disasters at supplier locations. Various strategies exist for addressing this risk, and a variety of types of research has been done regarding the identification, assessment and response to the risk of disruption in a supply chain network.

This thesis establishes a decision model to support Business Continuity Planning at the first-tier supplier level. The decision model incorporates discrete-event simulation of supply chain networks (through Simio software), Monte Carlo simulation, and risk index optimization. After modeling disruption vulnerability in a supply chain network, costs …

Electronic Warfare Receiver Resource Management And Optimization, William Metz

Walden Dissertations and Doctoral Studies

Optimization of electronic warfare (EW) receiver scan strategies is critical to improving the probability of surviving military missions in hostile environments. The problem is that the limited understanding of how dynamic variations in radar and EW receiver characteristics has influenced the response time to detect enemy threats. The dependent variable was the EW receiver response time and the 4 independent variables were EW receiver revisit interval, EW receiver dwell time, radar scan time, and radar illumination time. Previous researchers have not explained how dynamic variations of independent variables affected response time. The purpose of this experimental study was to develop …

Applications Of Simulation And Optimization Techniques In Optimizing Room And Pillar Mining Systems, Angelina Konadu Anani

Doctoral Dissertations

"The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) …

Optimal Energy Management System For A Net Zero Building Using Multi-Agent Systems Approach, Arzhang Ghassemi Pashakalaei

Open Access Theses & Dissertations

Buildings have a significant impact on energy usage and the environment. Much of the research in architectural sustainability has centered on economically advanced countries because they consume the most energy and have the most resources. However, sustainable architecture is important in developing countries, where the energy consumption of the building sector is increasing significantly. The aims of this Thesis are investigating using the hybrid system, which formed by interconnecting two source of renewable energy as solar (PV arrays) and wind (turbine) along with city grid for compensation purposes wherever it is required. In this Thesis, after literature review, we consider …

A Meta-Architecture Analysis For A Coevolved System-Of-Systems, George Anthony Muller Iv

Masters Theses

"Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments.

Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity …

A Recommendation System For Meta-Modeling: A Meta-Learning Based Approach, Can Cui, Mengqi Hu, Jeffery D. Weir, Teresa Wu

Faculty Publications

Various meta-modeling techniques have been developed to replace computationally expensive simulation models. The performance of these meta-modeling techniques on different models is varied which makes existing model selection/recommendation approaches (e.g., trial-and-error, ensemble) problematic. To address these research gaps, we propose a general meta-modeling recommendation system using meta-learning which can automate the meta-modeling recommendation process by intelligently adapting the learning bias to problem characterizations. The proposed intelligent recommendation system includes four modules: (1) problem module, (2) meta-feature module which includes a comprehensive set of meta-features to characterize the geometrical properties of problems, (3) meta-learner module which compares the performance of instance-based …