Operations Research, Systems Engineering and Industrial Engineering Commons^™

Resources Based Planning Framework For Infrastructure Maintenance And Rehabilitation Projects, Heba Gad

Theses and Dissertations

Infrastructure maintenance and rehabilitation projects involve activities scattered over a large geographical area (e.g., scattered road segments maintenance, telecom towers maintenance program, etc.). Planning such projects require a resource-based approach that accounts for the implications of resource mobility between activities’ locations in terms of time & cost. Existing scheduling techniques fall short of addressing the unique challenges of the scattered nature of these projects in combination with organization's limited resources availability. To address this need, this research presents a resources-based planning framework for infrastructure maintenance and rehabilitation scattered projects with the objective of enhancing resources utilization achieving time and cost …

The Product Test Scheduling Problem, Megan Wydick Martin, Cliff Ragsdale, John Fico, Carlos G. Cajica-Sierra, Richard M. Fetcenko

International Journal of Applied Management and Technology

This research focused on product test scheduling in the presence of in-process and at-completion inspection constraints. Such testing arises in the context of the manufacture of products that must perform reliably in extreme environmental conditions. Often, these products must receive a certification from prescribed regulatory agencies at the successful completion of a predetermined series of tests. Operational efficiency is enhanced by determining the optimal order and start times of tests so as to minimize the makespan while ensuring that technicians are available when needed to complete in-process and at-completion inspections. We refer to this as the product test scheduling problem. …

Advances And Applications In High-Dimensional Heuristic Optimization, Samuel Alexander Vanfossan

Doctoral Dissertations

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or …

Sos Explorer Application With Fuzzy-Genetic Algorithms To Assess An Enterprise Architecture -- A Healthcare Case Study, Josh Goldschmid, Vinayaka Gude, Steven Corns

Engineering Management and Systems Engineering Faculty Research & Creative Works

Kevin Dooley (1997), defined Complex Adaptive System (CAS) as a group of semi-autonomous agents who interact in interdependent ways to produce system-wide patterns, such that those patterns then influence behavior of the agents. A healthcare system is considered as a Complex Adaptive System of system (SoS) with agents composed of strategies, people, process, and technology. Healthcare systems are fragmented with independent systems and information. The enterprise architecture (EA) aims to address these fragmentations by creating boundaries around the business strategy and key performance attributes that drive integration across multiple systems of processes, people, and technology. This paper uses a SoS …

Applications Of A New Genetic Algorithm To Solve The Centralized Carrier Collaboration And Multihub Location Problem Considering Environmental Impacts, Eduardo Jose Castillo Fatule

Open Access Theses & Dissertations

The Centralized Carrier Collaboration and Multi-hub Location Problem (CCCMLP) represents a strategy that small-to-medium sized less-than-truckload (LTL) carrier companies can use in order to improve their profit margins. It is a strategy that is being explored in order to make these companies more sustainable as they are forced to reinvent their processes and supply chains. In this work, I will present a metaheuristic approach to optimizing their hub establishment and routing policies in order to better their expected profit margins and reduce their environmental impacts. The study considers the costs of transportation, loading and unloading, maintenance, operations, and inventory holding …

Solving Combinatorial Optimization Problems Using Genetic Algorithms And Ant Colony Optimization, Gautham Puttur Rajappa

Doctoral Dissertations

This dissertation presents metaheuristic approaches in the areas of genetic algorithms and ant colony optimization to combinatorial optimization problems.

Ant colony optimization for the split delivery vehicle routing problem

An Ant Colony Optimization (ACO) based approach is presented to solve the Split Delivery Vehicle Routing Problem (SDVRP). SDVRP is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) wherein a customer can be visited by more than one vehicle. The proposed ACO based algorithm is tested on benchmark problems previously published in the literature. The results indicate that the ACO based approach is competitive in both solution quality and solution …

New Mathematical And Evolutionary Optimization Methods To Achieve Fair Division In Multi-Agent Resource Allocation, Emmanuel Gurrola Molina

Open Access Theses & Dissertations

The problem of resource allocation among a group of agents naturally arises in a wide range of real-life events. The subject has earned popularity across the disciplines of Economics, Computer Science, Artificial Intelligence Operations Research and Social Welfare. This resource allocation problem can be commonly referred to as Multi-Agent Resource Allocation (MARA). This work considers a MARA problem where a central agent decides to allocate a set of divisible and non-divisible goods. MARA is considered to be part of an interdisciplinary research area in which the literature is vast and rapidly developing. However, most of the available literature mainly focuses …

Finding Robust-Under-Risk Solutions For Flowshop Scheduling, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We propose and explore, in the context of benchmark problems for flowshop scheduling, a risk-based concept of robustness for optimization problems. This risk-based concept is in distinction to, and complements, the uncertainty-based concept employed in the field known as robust optimization. Implementation of our concept requires problem solution methods that sample the solution space intelligently and that produce large numbers of distinct sample points. With these solutions to hand, their robustness scores are easily obtained and heuristically robust solutions found. We find evolutionary computation to be effective for this purpose on these problems.

Optimization Models For The Economic And Non-Economic Level Of Repair Analysis, Carlos Marco Ituarte-Villarreal

Open Access Theses & Dissertations

Every component, equipment or system will eventually fail. All failures produce a maintenance cost and, repair costs are especially hard to estimate ahead of time. Research shows that maintenance costs are the most substantial costs of development and use of equipment, reason why the cost of the product or equipment through its life is a major concern. Therefore, the main objective of this thesis is to provide a new optimization model to determine the minimum cost maintenance policy for complex systems.

Component Replacement Analysis For Electricity Distribution Systems Using Evolutionary Algorithms, Vasukumar Chenna

Open Access Theses & Dissertations

The main objective of the electric power grid is to supply economical and reliable electricity to industrial, commercial, household, transportation, and other end-users, including agricultural, educational institutions and hospitals. The power system is a very large and complex network consisting of generation, transmission, and distribution systems. The main focus of the present research is in the area of power distribution systems. Almost all the areas of the power grid uses simpler radial distribution systems to distribute electricity to the end consumer, it is the final and therefore vital link between the consumer and the rest of the power grid. Therefore …

An Improved Genetic Algorithm For Knapsack Problems, Taskiran, Gamze Kilincli

Browse all Theses and Dissertations

In this study, an improved genetic algorithm (GA) is presented to solve the multidimensional 0-1 knapsack problem (MKP). The MKP is a well-known combinatorial optimization problem and has received wide attention from the operations research community for decades. Although recent advances in computing and optimization technologies have made the solution of small and medium size instances possible, this NP-hard problem, in general, still remains one of the challenging problems yet to be solved.

Of the various algorithms developed to solve the MKP, GA seems to be one of the best methods pointed out in the literature. A GA is an …

Multiple Objective Optimization Of Performance Based Logistics, Delia Villanueva

Open Access Theses & Dissertations

This thesis presents a new Performance Based Logistics optimization model. Performance Based Logistics (PBL) is becoming increasingly important for manufacturers in mission critical environments that need to provide ultimate product availability at the lowest cost and with the highest level of customer satisfaction. The U.S. Department of Defense has mandated that Performance Based Logistics programs be adopted by its major weapon systems and equipment suppliers, is one of the newest support strategies to improve the weapon system readiness. This work presents a new multiple objective evolutionary approach that simultaneously optimizes objectives such as Reliability, Maintainability and Total Cost for Ownership. …

Electric Power Distribution Optimization Using Evolutionary Algorithms, Sowmya Parimi

Open Access Theses & Dissertations

In the present research, a new evolutionary algorithm is developed to solve the component allocation problem in electricity distribution systems. The problem addresses the upgrade/design of an electricity distribution system with the objective of minimizing expected system downtime subject to cost and repair time constraints. The algorithm is tested on the Dual Element Spot Network (DESN) configuration which is one of the most commonly used configurations by the power industry. This algorithm is demonstrated with two examples.

Optimization Of The Fuzzy Logic Controller For An Autonomous Uav, Jon C. Ervin, Sema E. Alptekin, Dianne J. Deturris

Industrial and Manufacturing Engineering

In this paper, we describe the optimization of membership functions in an application employing a hierarchical Fuzzy Logic Controller. The size of the rule base is made manageable by using a unique formulation, known as Combs method, to help control the problem of ‘exponential rule expansion’. The optimization is performed using a steady state genetic algorithm with a dynamic fitness function. The controller being developed is designed to fly a small, autonomous parafoil, suitable for short-range reconnaissance and land survey applications. The optimization process is performed in the Matlab/Simulink software environment and incorporates fuzzy logic modules developed in the Matlab …

Combining Evolving Neural Network Classifiers Using Bagging, Sunghwan Sohn, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The performance of the neural network classifier significantly depends on its architecture and generalization. It is usual to find the proper architecture by trial and error. This is time consuming and may not always find the optimal network. For this reason, we apply genetic algorithms to the automatic generation of neural networks. Many researchers have provided that combining multiple classifiers improves generalization. One of the most effective combining methods is bagging. In bagging, training sets are selected by resampling from the original training set and classifiers trained with these sets are combined by voting. We implement the bagging technique into …

Using A Neuro-Fuzzy-Genetic Data Mining Architecture To Determine A Marketing Strategy In A Charitable Organization's Donor Database, Korakot Hemsathapat, Cihan H. Dagli, David Lee Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

This paper describes the use of a neuro-fuzzy-genetic data mining architecture for finding hidden knowledge and modeling the data of the 1997 donation campaign of an American charitable organization. This data was used during the 1998 KDD Cup competition. In the architecture, all input variables are first preprocessed and all continuous variables are fuzzified. Principal component analysis (PCA) is then applied to reduce the dimensions of the input variables in finding combinations of variables, or factors, that describe major trends in the data. The reduced dimensions of the input variables are then used to train probabilistic neural networks (PNN) to …

An Object-Based Evolutionary Algorithm: The Nesting Solution, Kanchitpol Ratanapan, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The nesting problems have received considerable attention and have been addressed by a variety of algorithms. Recently, evolutionary algorithms have been adopted for solutions. Most of these algorithms, however, require a search in one-dimensional space; thus a transformation of the problem to a single dimension, as in the sequencing problems, is needed. Unfortunately, this restricts the search space. In this study an object-based evolutionary algorithm for the nesting problems is proposed. The methodology is created in a true two-dimensional space, allowing object-based mechanisms and object-based evolutionary operators to perform effectively on the space without restricting search alternatives. Implementation of the …

An Object-Based Evolutionary Algorithm For Solving Rectangular Piece Nesting Problems, Kanchitpol Ratanapan, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

Nesting problems have been tackled by researchers using a vast number of algorithms in the past. Most of the algorithms, however, need to perform on a one-dimensional space. Therefore, the problem must be transformed into a one-dimensional space problem similar to the travelling salesman problem. Consequently, loss of solutions due to the dimensional reduction may occur. In this study, an object-based evolutionary algorithm for rectangular piece nesting problems is proposed. This methodology is created on truly two-dimensional space, allowing new mechanisms (i.e., individual representation, initialization, etc.) and new object-based genetic operators (i.e., hill-climbing, mutation, and recombination operators) to perform effectively …