Matching Items (19)
Description
Nonregular screening designs can be an economical alternative to traditional resolution IV 2^(k-p) fractional factorials. Recently 16-run nonregular designs, referred to as no-confounding designs, were introduced in the literature. These designs have the property that no pair of main effect (ME) and two-factor interaction (2FI) estimates are completely confounded. In this dissertation, orthogonal arrays were evaluated with many popular design-ranking criteria in order to identify optimal 20-run and 24-run no-confounding designs. Monte Carlo simulation was used to empirically assess the model fitting effectiveness of the recommended no-confounding designs. The results of the simulation demonstrated that these new designs, particularly the 24-run designs, are successful at detecting active effects over 95% of the time given sufficient model effect sparsity. The final chapter presents a screening design selection methodology, based on decision trees, to aid in the selection of a screening design from a list of published options. The methodology determines which of a candidate set of screening designs has the lowest expected experimental cost.
ContributorsStone, Brian (Author) / Montgomery, Douglas C. (Thesis advisor) / Silvestrini, Rachel T. (Committee member) / Fowler, John W (Committee member) / Borror, Connie M. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation explores different methodologies for combining two popular design paradigms in the field of computer experiments. Space-filling designs are commonly used in order to ensure that there is good coverage of the design space, but they may not result in good properties when it comes to model fitting. Optimal designs traditionally perform very well in terms of model fitting, particularly when a polynomial is intended, but can result in problematic replication in the case of insignificant factors. By bringing these two design types together, positive properties of each can be retained while mitigating potential weaknesses. Hybrid space-filling designs, generated as Latin hypercubes augmented with I-optimal points, are compared to designs of each contributing component. A second design type called a bridge design is also evaluated, which further integrates the disparate design types. Bridge designs are the result of a Latin hypercube undergoing coordinate exchange to reach constrained D-optimality, ensuring that there is zero replication of factors in any one-dimensional projection. Lastly, bridge designs were augmented with I-optimal points with two goals in mind. Augmentation with candidate points generated assuming the same underlying analysis model serves to reduce the prediction variance without greatly compromising the space-filling property of the design, while augmentation with candidate points generated assuming a different underlying analysis model can greatly reduce the impact of model misspecification during the design phase. Each of these composite designs are compared to pure space-filling and optimal designs. They typically out-perform pure space-filling designs in terms of prediction variance and alphabetic efficiency, while maintaining comparability with pure optimal designs at small sample size. This justifies them as excellent candidates for initial experimentation.
ContributorsKennedy, Kathryn (Author) / Montgomery, Douglas C. (Thesis advisor) / Johnson, Rachel T. (Thesis advisor) / Fowler, John W (Committee member) / Borror, Connie M. (Committee member) / Arizona State University (Publisher)
Created2013
Description
A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.
ContributorsFu, Mengying (Author) / Askin, Ronald G. (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Fowler, John W (Committee member) / Pan, Rong (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
The ever-changing economic landscape has forced many companies to re-examine their supply chains. Global resourcing and outsourcing of processes has been a strategy many organizations have adopted to reduce cost and to increase their global footprint. This has, however, resulted in increased process complexity and reduced customer satisfaction. In order to meet and exceed customer expectations, many companies are forced to improve quality and on-time delivery, and have looked towards Lean Six Sigma as an approach to enable process improvement. The Lean Six Sigma literature is rich in deployment strategies; however, there is a general lack of a mathematical approach to deploy Lean Six Sigma in a global enterprise. This includes both project identification and prioritization. The research presented here is two-fold. Firstly, a process characterization framework is presented to evaluate processes based on eight characteristics. An unsupervised learning technique, using clustering algorithms, is then utilized to group processes that are Lean Six Sigma conducive. The approach helps Lean Six Sigma deployment champions to identify key areas within the business to focus a Lean Six Sigma deployment. A case study is presented and 33% of the processes were found to be Lean Six Sigma conducive. Secondly, having identified parts of the business that are lean Six Sigma conducive, the next steps are to formulate and prioritize a portfolio of projects. Very often the deployment champion is faced with the decision of selecting a portfolio of Lean Six Sigma projects that meet multiple objectives which could include: maximizing productivity, customer satisfaction or return on investment, while meeting certain budgetary constraints. A multi-period 0-1 knapsack problem is presented that maximizes the expected net savings of the Lean Six Sigma portfolio over the life cycle of the deployment. Finally, a case study is presented that demonstrates the application of the model in a large multinational company. Traditionally, Lean Six Sigma found its roots in manufacturing. The research presented in this dissertation also emphasizes the applicability of the methodology to the non-manufacturing space. Additionally, a comparison is conducted between manufacturing and non-manufacturing processes to highlight the challenges in deploying the methodology in both spaces.
ContributorsDuarte, Brett Marc (Author) / Fowler, John W (Thesis advisor) / Montgomery, Douglas C. (Thesis advisor) / Shunk, Dan (Committee member) / Borror, Connie (Committee member) / Konopka, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
This research is motivated by a deterministic scheduling problem that is fairly common in manufacturing environments, where there are certain processes that call for a machine working on multiple jobs at the same time. An example of such an environment is wafer fabrication in the semiconductor industry where some stages can be modeled as batch processes. There has been significant work done in the past in the field of a single stage of parallel machines which process jobs in batches. The primary motivation behind this research is to extend the research done in this area to a two-stage flow-shop where jobs arrive with unequal ready times and belong to incompatible job families with the goal of minimizing total weighted tardiness. As a first step to propose solutions, a mixed integer mathematical model is developed which tackles the problem at hand. The problem is NP-hard and thus the developed mathematical program can only solve problem instances of smaller sizes in a reasonable amount of time. The next step is to build heuristics which can provide feasible solutions in polynomial time for larger problem instances. The basic nature of the heuristics proposed is time window decomposition, where jobs within a moving time frame are considered for batching each time a machine becomes available on either stage. The Apparent Tardiness Cost (ATC) rule is used to build batches, and is modified to calculate ATC indices on a batch as well as a job level. An improvisation to the above heuristic is proposed, where the heuristic is run iteratively, each time assigning start times of jobs on the second stage as due dates for the jobs on the first stage. The underlying logic behind the iterative approach is to improve the way due dates are estimated for the first stage based on assigned due dates for jobs in the second stage. An important study carried out as part of this research is to analyze the bottleneck stage in terms of its location and how it affects the performance measure. Extensive experimentation is carried out to test how the quality of the solution varies when input parameters are varied between high and low values.
ContributorsTewari, Anubha Alokkumar (Author) / Fowler, John W (Thesis advisor) / Monch, Lars (Thesis advisor) / Gel, Esma S (Committee member) / Arizona State University (Publisher)
Created2012
Description
The client detailed in this report is a premier continuing healthcare education organization providing a variety of homeopathic therapy classes for its students. The purpose of this paper is to showcase a business plan that will help dictate the basic structure of the client's business once they are independent of their current managing company. Extensive analysis, primarily based upon online market research and personal correspondence with the client, was conducted for this report. Detailed within this paper are several areas where the client can significantly lower costs and increase future revenues by modifying practices employed by the managing company. From the analysis provided, the client has the opportunity to create and grow a well-organized, profitable business with a sustainable future.
ContributorsMionis, Erika (Co-author) / Lee, Betty (Co-author) / Coult, Natasha (Co-author) / Brooks, Dan (Thesis director) / Chikly, Bruno (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / School of International Letters and Cultures (Contributor) / School of Life Sciences (Contributor) / W. P. Carey School of Business (Contributor)
Created2015-05
Description
This article summarizes exploratory research conducted on private and public hospital systems in Australia and Costa Rica analyzing the trends observed within supply chain procurement. Physician preferences and a general lack of available comparative effectiveness research—both of which are challenges unique to the health care industry—were found to be barriers to effective supply chain performance in both systems. Among other insights, the ability of policy to catalyze improved procurement performance in public hospital systems was also was observed. The role of centralization was also found to be fundamental to the success of the systems examined, allowing hospitals to focus on strategic rather than operational decisions and conduct value-streaming activities to generate increased cost savings.
ContributorsBudgett, Alexander Jay (Author) / Schneller, Eugene (Thesis director) / Gopalakrishnan, Mohan (Committee member) / Barrett, The Honors College (Contributor) / Department of Supply Chain Management (Contributor) / Department of English (Contributor)
Created2015-05
Description
Customized online education is a means of educating a large amount of users in a way that will change their behavior at a low incremental cost to the one providing the information. This thesis will examine several aspects of online education, but primarily focus on the presentation of the materials. It will examine how this is done through a consulting project I worked on in conjunction with the New Venture Group for Parenting Arizona. Parenting Arizona is a non-profit organization based in Arizona that offers classes for parents who are seeking better ways to manage their family responsibilities. The purpose of the consulting project was to take the instructional materials used in in-person group classes and modify it to be effectively used for instruction in an online environment. Parenting Arizona foresaw a number of benefits from this modification and migration of instructional materials for the web; first among these was the ability of people in remote areas or in situations that did not allow them to attend an on-ground class to gain access to instructional material. In addition, the broader availability of the material that would come from its presence on the web would expand the influence of good parenting instructions to a greater audience both inside and outside the State of Arizona, aiding even more families.
ContributorsAnderson, Kyle (Author) / Brooks, Dan (Thesis director) / Forss, Brennan (Committee member) / Rosen, Julie (Committee member) / Barrett, The Honors College (Contributor)
Created2012-05
Description
This paper goes through a two-pronged approach in the attempt to understand E-Sports, entertainment gaming, and the creation of the E-Sports bar/Barcade. The first portion aims to explain and quantify the growth of electronic sports (or E-sports). This new craze has been growing immensely in the past 5 years, by viewership and by monetary endorsements. With these changes and growth patterns, we then move on to explain one of the many niche markets that has been created from the growth of E-sports and entertainment gaming. Through our experience in the field, we have evaluated 8 E-sports bars and Barcades in order to confirm their viability in the marketplace. Through our worldwide research we have found that E-sports will continue to grow and that Barcades will not only be viable, but will be a competitive market in the next 10-20 years.
ContributorsNist, Nicholas (Co-author) / Hester, James (Co-author) / Brooks, Dan (Thesis director) / Forss, Brennan (Committee member) / Barrett, The Honors College (Contributor) / Department of Economics (Contributor) / Department of Supply Chain Management (Contributor) / W. P. Carey School of Business (Contributor) / Department of Psychology (Contributor)
Created2015-05
Description
This report details a prioritization value model that was created for the use of Arizona State University and ASU LightWorks in determining and implementing appropriate sustainability projects for removing greenhouse gas emissions. A thorough review regarding the current project selection process, and an extensive analysis into the desired state of the process was conducted for this paper. The newly developed prioritization model includes multiple attributes that rank and prioritize projects based upon the highest value as determined by criteria set forth by the university. Encompassed within this report are the steps in creating the decision model, as well as the benefits and additional uses of the model for the end user. From the analysis and model created, the end user has the ability to choose carbon neutral projects that better align with the vision of the New American University.
ContributorsAmoroso, Nicholas (Co-author) / Lee, Betty (Co-author) / Brooks, Dan (Thesis director) / Johnson, Travis (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor)
Created2015-05