Operational Research Commons^™

Connection-Saving Gate Assignment: A Computational Approach, Rob Mailley

Computer Science Senior Theses

The growth of the commercial aviation industry has yielded many interesting problems in the field of Operations Research, many of which are now able to be solved as both technology and mathematical optimization improve. A particularly interesting problem in airport operations re- search is the Aircraft Gate Assignment Problem (AGAP), which seeks to create a feasible match- ing between planes and flights at an airport. This problem is well-suited to modeling with Integer Programming, and has attracted research since the 1970s. Researchers of the AGAP have considered many different objectives, ranging from airline-focused objectives to more passenger-focused objective functions. In …

Paints-R-Us Term Project, Tyler Campbell, Skye Gilbreth, Michael Oluwole, Elijah Raffo, Brad Unruh

Engineering and Technology Management Student Projects

This project will consider a linear product mix optimization problem for a fictional paint company, Paints-R-Us. Paints-R-Us is a wholesale paint manufacturer located in the Pacific Northwest. The Global Production Manager, Steve Brush, has been tasked with maximizing Paints-R-Us’s profit in the upcoming quarter. Steve Brush oversees the global production plan, and in collaboration with the production planners will develop a production plan which optimizes the profits that Paints-R-Us can create in the quarter accounting for the following criteria:

• Demand in the given quarter for each of the 5 paint types that Paints-R-Us produces • The warehousing storage capacity …

Adaptive Pareto Set Estimation For Stochastic Mixed Variable Design Problems, Christopher D. Arendt

Theses and Dissertations

Many design problems require the optimization of competing objective functions that may be too complicated to solve analytically. These problems are often modeled in a simulation environment where static input may result in dynamic (stochastic) responses to the various objective functions. System reliability, alloy composition, algorithm parameter selection, and structural design optimization are classes of problems that often exhibit such complex and stochastic properties. Since the physical testing and experimentation of new designs can be prohibitively expensive, engineers need adequate predictions concerning the viability of various designs in order to minimize wasteful testing. Presumably, an appropriate stochastic multi-objective optimizer can …

Improving Mixed Variable Optimization Of Computational And Model Parameters Using Multiple Surrogate Functions, David Bethea

Theses and Dissertations

This research focuses on reducing computational time in parameter optimization by using multiple surrogates and subprocess CPU times without compromising the quality of the results. This is motivated by applications that have objective functions with expensive computational times at high fidelity solutions. Applying, matching, and tuning optimization techniques at an algorithm level can reduce the time spent on unprofitable computations for parameter optimization. The objective is to recover known parameters of a flow property reference image by comparing to a template image that comes from a computational fluid dynamics simulation, followed by a numerical image registration and comparison process. Mixed …

Search Techniques For Multi-Objective Optimization Of Mixed Variable Systems Having Stochastic Responses, Jennifer G. Walston

Theses and Dissertations

A research approach is presented for solving stochastic, multi-objective optimization problems. First, the class of mesh adaptive direct search (MADS) algorithms for nonlinearly constrained optimization is extended to mixed variable problems. The resulting algorithm, MV-MADS, is then extended to stochastic problems (MVMADS-RS), via a ranking and selection procedure. Finally, a two-stage method is developed that combines the generalized pattern search/ranking and selection (MGPS-RS) algorithms for single-objective, mixed variable, stochastic problems with a multi-objective approach that makes use of interactive techniques for the specification of aspiration and reservation levels, scalarization functions, and multi-objective ranking and selection. A convergence analysis for the …

Critical Infrastructure Rebuild Prioritization Using Simulation Optimization, Namsuk Cho

Theses and Dissertations

This thesis examines the importance of a critical infrastructure rebuild strategy following a terrorist attack or natural disaster such as Hurricane Katrina. Critical infrastructures are very complex and dependent systems in which their re-establishment is an essential part of the rebuilding process. A rebuild simulation model consisting of three layers (physical, information, and spatial) captures the dependency between the six critical infrastructures modeled. We employ a simulation optimization approach to evaluate rebuild prioritization combinations with a goal of minimizing the time needed to achieve an acceptable rebuild level. We use a simulated annealing heuristic as an optimization technique that works …

An Adaptive Tabu Search Heuristic For The Location Routing Pickup And Delivery Problem With Time Windows With A Theater Distribution Application, Robert E. Burks Jr.

Theses and Dissertations

The time constrained pickup and delivery problem (PDPTW) is a problem of finding a set of routes for a fleet of vehicles in order to satisfy a set of transportation requests. Each request represents a user-specified pickup and delivery location. The PDPTW may be used to model many problems in logistics and public transportation. The location routing problem (LRP) is an extension of the vehicle routing problem where the solution identifies the optimal location of the depots and provides the vehicle schedules and distribution routes. This dissertation seeks to blend the PDPTW and LRP areas of research and formulate a …

Optimization Of A Multi-Echelon Repair System Via Generalized Pattern Search With Ranking And Selection: A Computational Study, Derek D. Tharaldson

Theses and Dissertations

With increasing developments in computer technology and available software, simulation is becoming a widely used tool to model, analyze, and improve a real world system or process. However, simulation in itself is not an optimization approach. Common optimization procedures require either an explicit mathematical formulation or numerous function evaluations at improving iterative points. Mathematical formulation is generally impossible for problems where simulation is relevant, which are characteristically the types of problems that arise in practical applications. Further complicating matters is the variability in the simulation response which can cause problems in iterative techniques using the simulation model as a function …

Routing Unmanned Aerial Vehicles While Considering General Restricted Operating Zones, Darin T. Brown

Theses and Dissertations

U.S. military forces employ unmanned aerial vehicles (UAVs) to conduct intelligence-gathering missions worldwide. For a typical mission, commanders may task UAV operators to gather imagery on 100 or more sites or targets. UAV operators must quickly prepare mission plans that meet the needs of their commanders while dealing with real-world constraints such as time windows, site priorities, imagery requirements, UAVs with different capabilities (i.e. imagery equipment, speed, and range), and UAVs departing from different bases. Previous AFIT research provided the UAV Battlelab with a tool, AFIT Router, for generating high-quality routes to aid mission planning. This research enhances the AFIT …

Routing Optimization Through Computer-Aided Simulation, Madhusudhan Akundi

Electrical & Computer Engineering Theses & Dissertations

Routing has always been a challenging field, and the various techniques of mathematics and operations research have been developed. For commercial organizations, routing has begun to receive extensive attention because of the savings gained in terms of time, money, personnel, and the transportation fleet by developing optimized routing networks. Until recently, it was difficult to develop optimized networks due to the computational difficulties of the problem. However, with the advancement in computer technology and processing speeds, it has become possible to simulate a large number of routing scenarios and determine optimal solutions for a given set of constraints. Not only …

Analysis Of A Methodology For Linear Programming Optimality Analysis, Chanseok Jeong

Theses and Dissertations

The methodology of Johnson, Baner, Moore, and Grant can be applied to large scale linear programming models. A methodology for optimality analysis of linear programs was developed to create metamodels using response surface methodology techniques such as experimental design and least squares regression. A metamodel consists of a simple equation which is able to predict the optimal objective function value of a linear program. What is needed is some large scale application of the techniques to verify how accurate they are. In the research, I plan to use the large scale LP model, STORM. I use the "Hot Start" idea …

Right-Hand-Side Multidimensional Optimality Analysis Of A Large Scale Linear Program Using Metamodelling Techniques, Osman Iyde

Theses and Dissertations

A methodology for optimality analysis of linear programs was developed by Johnson, Bauer, Moore, and Grant to create metamodels using response surface methodology techniques such as experimental design and least squares regression, and a geostatistical estimation technique, namely kriging. Metamodels have the form of a simple polynomial, and they predict the optimal objective function value of an LP for various levels of the constraints. They eliminate the necessity of determining which critical region contains the right-hand-side (RHS) vector of interest since they are valid over multiple critical regions. The methodology of Johnson, et al. can be applied to large scale …

Optimization Of Stochastic Response Surfaces Subject To Constraints With Linear Programming, Robert G. Harvey

Theses and Dissertations

This research investigated an alternative to the traditional approaches of optimizing a stochastic response surface subject to constraints. This research investigated the bias in the expected value of the solution, possible alternative decision variable settings, and a method to improve the solution. A three step process is presented to evaluate stochastic response surfaces subject to constraints. Step 1 is to use a traditional approach to estimate the response surface and a covariance matrix through regression. Step 2 samples the objective function of the linear program (i.e., response surface) and identifies the extreme points visited. Step 3 presents a method to …