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MULTIOBJECTIVE SIMULATION OPTIMIZATION USING ENHANCED EVOLUTIONARY ALGORITHM APPROACHES
Title
MULTIOBJECTIVE SIMULATION OPTIMIZATION USING ENHANCED EVOLUTIONARY ALGORITHM APPROACHES.
Creator
Eskandari, Hamidreza, Geiger, Christopher, University of Central Florida
Abstract / Description
In today's competitive business environment, a firm's ability to make the correct, critical decisions can be translated into a great competitive advantage. Most of these critical real-world decisions involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective may degrade the performance of one or more of the other objectives. Traditional approaches for solving multiobjective optimization problems typically try...
Show moreIn today's competitive business environment, a firm's ability to make the correct, critical decisions can be translated into a great competitive advantage. Most of these critical real-world decisions involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective may degrade the performance of one or more of the other objectives. Traditional approaches for solving multiobjective optimization problems typically try to scalarize the multiple objectives into a single objective. This transforms the original multiple optimization problem formulation into a single objective optimization problem with a single solution. However, the drawbacks to these traditional approaches have motivated researchers and practitioners to seek alternative techniques that yield a set of Pareto optimal solutions rather than only a single solution. The problem becomes much more complicated in stochastic environments when the objectives take on uncertain (or "noisy") values due to random influences within the system being optimized, which is the case in real-world environments. Moreover, in stochastic environments, a solution approach should be sufficiently robust and/or capable of handling the uncertainty of the objective values. This makes the development of effective solution techniques that generate Pareto optimal solutions within these problem environments even more challenging than in their deterministic counterparts. Furthermore, many real-world problems involve complicated, "black-box" objective functions making a large number of solution evaluations computationally- and/or financially-prohibitive. This is often the case when complex computer simulation models are used to repeatedly evaluate possible solutions in search of the best solution (or set of solutions). Therefore, multiobjective optimization approaches capable of rapidly finding a diverse set of Pareto optimal solutions would be greatly beneficial. This research proposes two new multiobjective evolutionary algorithms (MOEAs), called fast Pareto genetic algorithm (FPGA) and stochastic Pareto genetic algorithm (SPGA), for optimization problems with multiple deterministic objectives and stochastic objectives, respectively. New search operators are introduced and employed to enhance the algorithms' performance in terms of converging fast to the true Pareto optimal frontier while maintaining a diverse set of nondominated solutions along the Pareto optimal front. New concepts of solution dominance are defined for better discrimination among competing solutions in stochastic environments. SPGA uses a solution ranking strategy based on these new concepts. Computational results for a suite of published test problems indicate that both FPGA and SPGA are promising approaches. The results show that both FPGA and SPGA outperform the improved nondominated sorting genetic algorithm (NSGA-II), widely-considered benchmark in the MOEA research community, in terms of fast convergence to the true Pareto optimal frontier and diversity among the solutions along the front. The results also show that FPGA and SPGA require far fewer solution evaluations than NSGA-II, which is crucial in computationally-expensive simulation modeling applications.
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Date Issued
2006
Identifier
CFE0001283, ucf:46905
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001283
A METHODOLOGY TO STABILIZE THE SUPPLY CHAIN
Title
A METHODOLOGY TO STABILIZE THE SUPPLY CHAIN.
Creator
Sarmiento, Alfonso, Rabelo, Luis, University of Central Florida
Abstract / Description
In todayÂÂ's world, supply chains are facing market dynamics dominated by strong global competition, high labor costs, shorter product life cycles, and environmental regulations. Supply chains have evolved to keep pace with the rapid growth in these business dynamics, becoming longer and more complex. As a result, supply chains are systems with a great number of network connections among their multiple components. The interactions of the network components with respect...
Show moreIn todayÂÂ's world, supply chains are facing market dynamics dominated by strong global competition, high labor costs, shorter product life cycles, and environmental regulations. Supply chains have evolved to keep pace with the rapid growth in these business dynamics, becoming longer and more complex. As a result, supply chains are systems with a great number of network connections among their multiple components. The interactions of the network components with respect to each other and the environment cause these systems to behave in a highly nonlinear dynamic manner. Ripple effects that have a huge, negative impact on the behavior of the supply chain (SC) are called instabilities. They can produce oscillations in demand forecasts, inventory levels, and employment rates and, cause unpredictability in revenues and profits. Instabilities amplify risk, raise the cost of capital, and lower profits. To reduce these negative impacts, modern enterprise managers must be able to change policies and plans quickly when those consequences can be detrimental. This research proposes the development of a methodology that, based on the concepts of asymptotic stability and accumulated deviations from equilibrium (ADE) convergence, can be used to stabilize a great variety of supply chains at the aggregate levels of decision making that correspond to strategic and tactical decision levels. The general applicability and simplicity of this method make it an effective tool for practitioners specializing in the stability analysis of systems with complex dynamics, especially those with oscillatory behavior. This methodology captures the dynamics of the supply chain by using system dynamics (SD) modeling. SD was the chosen technique because it can capture the complex relationships, feedback processes, and multiple time delays that are typical of systems in which oscillations are present. If the behavior of the supply chain shows instability patterns, such as ripple effects, the methodology solves an optimization problem to find a stabilization policy to remove instability or minimize its impact. The policy optimization problem relies upon a theorem which states that ADE convergence of a particular state variable of the system, such as inventory, implies asymptotic stability for that variable. The stabilization based on the ADE requires neither linearization of the system nor direct knowledge of the internal structure of the model. Moreover, the ADE concept can be incorporated easily in any SD modeling language. The optimization algorithm combines the advantage of particle swarm optimization (PSO) to determine good regions of the search space with the advantage of local optimization to quickly find the optimal point within those regions. The local search uses a Powell hill-climbing (PHC) algorithm as an improved procedure to the solution obtained from the PSO algorithm, which assures a fast convergence of the ADE. The experiments showed that solutions generated by this hybrid optimization algorithm were robust. A framework built on the premises of this methodology can contribute to the analysis of planning strategies to design robust supply chains. These improved supply chains can then effectively cope with significant changes and disturbances, providing companies with the corresponding cost savings.
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Date Issued
2010
Identifier
CFE0002986, ucf:47977
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002986
Coordinated Optimal Power Planning of Wind Turbines in an Offshore Wind Farm
Title
Coordinated Optimal Power Planning of Wind Turbines in an Offshore Wind Farm.
Creator
Vishwakarma, Puneet, Xu, Yunjun, Kapat, Jayanta, Kauffman, Jeffrey, Behal, Aman, University of Central Florida
Abstract / Description
Wind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased...
Show moreWind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased loading on wind turbines. In wind farms, wind turbines' wake affects each other depending on their positions and operation modes. Therefore it becomes essential to optimize the wind farm power production as a whole than to just focus on individual wind turbines. The work presented here develops a hierarchical power optimization algorithm for wind farms. The algorithm includes a cooperative level (or higher level) and an individual level (or lower level) for power coordination and planning in a wind farm. The higher level scheme formulates and solves a quadratic constrained programming problem to allocate power to wind turbines in the farm while considering the aerodynamic effect of the wake interaction among the turbines and the power generation capabilities of the wind turbines. In the lower level, optimization algorithm is based on a leader-follower structure driven by the local pursuit strategy. The local pursuit strategy connects the cooperative level power allocation and the individual level power generation in a leader-follower arrangement. The leader, could be a virtual entity and dictates the overall objective, while the followers are real wind turbines considering realistic constraints, such as tower deflection limits. A nonlinear wind turbine dynamics model is adopted for the low level study with loading and other constraints considered in the optimization. The stability of the algorithm in the low level is analyzed for the wind turbine angular velocity. Simulations are used to show the advantages of the method such as the ability to handle non-square input matrix, non-homogenous dynamics, and scalability in computational cost with rise in the number of wind turbines in the wind farm.
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Date Issued
2015
Identifier
CFE0005899, ucf:50896
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005899
OPTIMIZED MARKET INTRODUCTION OF LARGE CAPITAL PRODUCTS WITH LONG DEVELOPMENT AND LEARNING CYCLES
Title
OPTIMIZED MARKET INTRODUCTION OF LARGE CAPITAL PRODUCTS WITH LONG DEVELOPMENT AND LEARNING CYCLES.
Creator
Lembcke, Antje, Malone, Linda, University of Central Florida
Abstract / Description
Any product sold is expected to be reliable and available when the customer wants to operate it. Companies that produce large capital products (LCP), such as rockets, satellites, or large gas turbines to generate electrical energy, tend to shy away from extending their testing and validation method above the requirements by law, mainly due to the very high costs of each additional test and the uncertain return on investment. This research shows that todayÂÂ's state of...
Show moreAny product sold is expected to be reliable and available when the customer wants to operate it. Companies that produce large capital products (LCP), such as rockets, satellites, or large gas turbines to generate electrical energy, tend to shy away from extending their testing and validation method above the requirements by law, mainly due to the very high costs of each additional test and the uncertain return on investment. This research shows that todayÂÂ's state of the art validation methods for LCP, required by law, or suggested in literature, and adapted by these industries, are not capable of capturing all significant failure modes (or even enough failure modes), with the consequence that the subsequently sold commercial products will still experience failures with significant effects on product reliability, and subsequently on the companiesÂÂ' bottom line earnings projections. The research determines the type of data (significant variables) necessary to correlate a companyÂÂ's validation policy to product failures after commercialization, and predicts the financial impact of the current validation policy on the companyÂÂ's profitability. A systems dynamics model to assess a company's testing policy is developed and an optimized product validation plan is suggested, and its impact on a companyÂÂ's profitability is demonstrated through simulation. A generic methodology is derived and its viability is illustrated using a specific product and a dynamic model developed with data available to the researcher. The generic method can be applied by any company to develop its own model for optimizing product reliability prior to market introduction.
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Date Issued
2010
Identifier
CFE0003413, ucf:48404
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003413
On Distributed Estimation for Resource Constrained Wireless Sensor Networks
Title
On Distributed Estimation for Resource Constrained Wireless Sensor Networks.
Creator
Sani, Alireza, Vosoughi, Azadeh, Rahnavard, Nazanin, Wei, Lei, Atia, George, Chatterjee, Mainak, University of Central Florida
Abstract / Description
We study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically...
Show moreWe study Distributed Estimation (DES) problem, where several agents observe a noisy version of an underlying unknown physical phenomena (which is not directly observable), and transmit a compressed version of their observations to a Fusion Center (FC), where collective data is fused to reconstruct the unknown. One of the most important applications of Wireless Sensor Networks (WSNs) is performing DES in a field to estimate an unknown signal source. In a WSN battery powered geographically distributed tiny sensors are tasked with collecting data from the field. Each sensor locally processes its noisy observation (local processing can include compression,dimension reduction, quantization, etc) and transmits the processed observation over communication channels to the FC, where the received data is used to form a global estimate of the unknown source such that the Mean Square Error (MSE) of the DES is minimized. The accuracy of DES depends on many factors such as intensity of observation noises in sensors, quantization errors in sensors, available power and bandwidth of the network, quality of communication channels between sensors and the FC, and the choice of fusion rule in the FC. Taking into account all of these contributing factors and implementing a DES system which minimizes the MSE and satisfies all constraints is a challenging task. In order to probe into different aspects of this challenging task we identify and formulate the following three problems and address them accordingly:1- Consider an inhomogeneous WSN where the sensors' observations is modeled linear with additive Gaussian noise. The communication channels between sensors and FC are orthogonal power and bandwidth-constrained erroneous wireless fading channels. The unknown to be estimated is a Gaussian vector. Sensors employ uniform multi-bit quantizers and BPSK modulation. Given this setup, we ask: what is the best fusion rule in the FC? what is the best transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the MSE? In order to answer these questions, we derive some upper bounds on global MSE and through minimizing those bounds, we propose various resource allocation schemes for the problem, through which we investigate the effect of contributing factors on the MSE.2- Consider an inhomogeneous WSN with an FC which is tasked with estimating a scalar Gaussian unknown. The sensors are equipped with uniform multi-bit quantizers and the communication channels are modeled as Binary Symmetric Channels (BSC). In contrast to former problem the sensors experience independent multiplicative noises (in addition to additive noise). The natural question in this scenario is: how does multiplicative noise affect the DES system performance? how does it affect the resource allocation for sensors, with respect to the case where there is no multiplicative noise? We propose a linear fusion rule in the FC and derive the associated MSE in closed-form. We propose several rate allocation schemes with different levels of complexity which minimize the MSE. Implementing the proposed schemes lets us study the effect of multiplicative noise on DES system performance and its dynamics. We also derive Bayesian Cramer-Rao Lower Bound (BCRLB) and compare the MSE performance of our porposed methods against the bound.As a dual problem we also answer the question: what is the minimum required bandwidth of thenetwork to satisfy a predetermined target MSE?3- Assuming the framework of Bayesian DES of a Gaussian unknown with additive and multiplicative Gaussian noises involved, we answer the following question: Can multiplicative noise improve the DES performance in any case/scenario? the answer is yes, and we call the phenomena as 'enhancement mode' of multiplicative noise. Through deriving different lower bounds, such as BCRLB,Weiss-Weinstein Bound (WWB), Hybrid CRLB (HCRLB), Nayak Bound (NB), Yatarcos Bound (YB) on MSE, we identify and characterize the scenarios that the enhancement happens. We investigate two situations where variance of multiplicative noise is known and unknown. Wealso compare the performance of well-known estimators with the derived bounds, to ensure practicability of the mentioned enhancement modes.
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Date Issued
2017
Identifier
CFE0006913, ucf:51698
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006913