1,720,969 research outputs found
Developing procurement strategy by applying classification algorithms for effective supplier assessment
No full textPoster project completed at the Wichita State University Department of Industrial, Manufacturing and Systems Engineering. Presented at the 17th Annual Capitol Graduate Research Summit, Topeka, KS, February 26, 2020.The manufacturing sector ranks one of the top spots in the 2018 Kansas economy and this projection is predicted to continue with 0.5% growth in 2019. Within the manufacturing sector, aerospace is ranked fourth in Kansas with over 30,000 workers. Almost 44% of Kansans work for small businesses (less than 50 employees) and this percentage is expected to increase with the effective assessment of the suppliers in the supply chain network of the business in Kansas. This research aims to provide a comprehensive and robust assessment process for suppliers. Therefore, we propose the use of supervised machine learning algorithms to classify various suppliers into four categories: excellent, good, satisfactory, and unsatisfactory. In this research, supervised learning (classification) algorithms are applied to a supplier assessment problem where a model is trained based on the previous historical data and then tested on the new unseen data set. This method will provide an efficient way for supplier assessment that is more effective in terms of accuracy and time when compared to the multi-criteria decision-making approach. Classification algorithms such as support vector machines (with linear, polynomial and radial basis kernels), logistic regression, k-nearest neighbors, and naïve Bayes methods are used to train the model and their performance is assessed against a test data. Finally, the performance measures from all the classification methods are used to assess the best supplier in any business in Kansas
A practical approach to project scheduling: Considering multiple critical path scenarios in project network
No full textThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial, Systems and Manufacturing EngineeringA well-managed project results in efficient scheduling of the interrelated components of work tasks, resources, stakeholders and budgets plans. The goal is for project managers to be able to generate an initial schedule at an early stage of product development. Due to complex product development, uncertainties and variabilities exists during manufacturing process. Variable processing times may lead to multiple critical paths within the system. Based on the real world demands of project managers two objectives are considered: Identification of multiple critical paths within the system and minimize completion time of the activity by optimal resource allocation. To solve this problem, a mixed integer linear programming model (MILP) is proposed. The Multiple critical path scheduling approach (MCPSA) is developed to identify multiple critical paths dominant in a system. A criticality index is used as performance metric to measure the intensity of the critical path in comparison to the project completion time. An interface of MS- Project with a simulation software (@RISK) is used to obtain the results. An application of two auger screws used in extrusion molding machines from a leading manufacturer in Kansas is used to illustrate the case study models and the MCPSA algorithm
Quantum machine intelligence in smart transportation
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of of Industrial, Systems and Manufacturing EngineeringSmart Mobility is the key component of the Smart City initiative that is being explored throughout the world. In recent years, bike-sharing systems (BSS) are being widely established in urban cities to provide a sustainable mode of transport, by fulfilling the mobility requirements of public residents. The application of BSS in highly congested urban cities reduces the effect of overcrowding, pollution, and traffic congestion problems. The crucial role behind incorporating BSS depends on the prediction and rebalancing of bike demand across all the bike stations. The bike demand prediction involves real-time analysis for identifying the discrepancy between the bike pick-up and drop-off throughout all the bike stations in a given time period. The critical part of a BSS operation is the effective management of rebalancing vehicle carrier operations that ensure bikes are restored in each station to their target value during every pick-up and drop-off operation. To enhance the prediction and rebalancing analysis of bike demand we propose quantum computing algorithms to provide computational speedup in comparison with classical algorithms. In my thesis, we focused on developing algorithms for solving prediction and combinatorial optimization problems with applications in Shared Mobility. We extensively used three methods of approach (a) Quantum Bayesian network which is quantum equivalent to classical Bayesian network for bike sharing demand prediction problems, (b) Optimization models and Quadratic unconstrained binary optimization models for solving combinatorial optimization problems such as rebalancing bike sharing systems, (c) Ensemble of prediction models with Deep learning models to measure the accuracy and computational performance of both (Quantum & Classical) computing platforms.Embargoed till 2024-12-3
Learning ensemble classification method for supplier assessment
No full textPresented to the 15th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 26, 2019.Research completed in the Department of Industrial, Systems, and Manufacturing Engineering, College of EngineeringIn the current era of the global supply chain, integration of information technology and acceleration of competitiveness are necessary for an effective supply chain management (SCM). The SCM is considered as a competitive strategy that integrates suppliers and customers with the aim of improving the flexibility and responsiveness of the organization. Supplier assessment plays a significant role in accomplishing the strategic goals of the organization. Identification of the appropriate suppliers improves the corporate competitiveness and decision makers believe that the supplier assessment is the most crucial activity of the purchasing department. In today's world of uncertain customer preferences, fluctuating market demands, and new procurement policies, demand a quick and comprehensive supplier assessment process for all the organizations. Hence, supplier assessment is one of the most crucial steps during the procurement stage. In this paper, supplier assessment play a critical role in the supply chain management, which involves flow of goods and services from the initial stage (raw material procurement) to the final stage (delivery). Supplier assessment is a multi-criteria decision making approach that requires several criteria for the proper assessment of the suppliers. When there are several criteria involved, it makes the supplier assessment process more complicated. For a comprehensive and robust assessment process, we propose machine-learning algorithms to classify various suppliers into four categories: excellent, good, satisfactory, and unsatisfactory. In this paper, machine learning algorithms (especially classification algorithms) are applied for a supplier assessment problem where a model is trained based on the previous historical data and then tested on the new a previously unseen data set. However, the classification algorithm used in this paper helps to analyze the potential suppliers that are considered for the organization. This work concentrates on supplier assessment process by applying machine learning algorithms for model building, which is then used for decision-making regarding supplier selection. This method will provide an efficient way for supplier assessment that is more effective (in terms of accuracy and time). Machine learning techniques that include bagging and boosting methods are used to create various ensemble classifiers from training data, and their performance is measured using test data. Finally, we theorize a method to analyze the supplier performance by utilizing data analytics in SCM.Graduate School, Academic Affairs, University Librarie
Forecasting bike sharing demand using Quantum Bayesian Network
No full textClick on the DOI to access this article (may not be free).In recent years, bike-sharing systems (BSS) are being widely established in urban cities to provide a sustainable mode of transport, by fulfilling the mobility requirements of public residents. The application of BSS in highly congested urban cities reduces the effect of overcrowding, pollution, and traffic congestion problems. The crucial role behind incorporating BSS depends on the prediction of bike demand across all the bike stations. The bike demand prediction involves real-time analysis for identifying the discrepancy between the bike pick-up and drop-off throughout all the bike stations in a given time period. To enhance the prediction analysis of bike demand we propose quantum computing algorithms to provide computational speedup in comparison with classical algorithms. In this paper, we illustrate the construction of Quantum Bayesian Networks (QBN), for predicting bike demand. Furthermore, we provide a solution framework for implementing QBN for two case studies: (a) bike demand prediction during weekdays, (b) bike demand prediction during weekends. We have compared the quantum and classical solutions, by using IBM-Qiskit and Netica computing platforms
Smart rebalancing for bike sharing systems using quantum approximate optimization algorithm
No full textClick on the DOI link to access the conference paper (may not be free).Smart Mobility is the key component of Smart City initiative that are being explored throughout the world. The bike-sharing system (BSS) aims to provide an alternative mode of Smart Mobility transportation system, and it is being widely adopted in urban areas. The use of bikes for short-distance travel helps to reduce traffic congestion, reduce carbon emissions, and decrease the risk of overcrowding. Effective bike sharing system operations requires rebalancing analysis, which corresponds to transferal of bikes across various bike stations to ensure the supply meets expected demand. In this work, we present Quantum Approximate Optimization Algorithm(QAOA), a variational hybrid quantum-classical algorithm that has shown significant computational advantages in solving combinatorial optimization problems such as bike sharing system rebalancing problem (BSS-RBP). Here, we minimize the overall distance travelled by the transport vehicle across various bike station. In this preliminary work, we demonstrate the application of QAOA using the IBM-Qiskit quantum computing simulator for rebalancing analysis across three bike locations
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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