Lynn Coupal has an educational background in mathematics, including undergraduate degrees in mathematics and secondary mathematics education and a master's degree in systems engineering. Her coursework focused on probability, statistics, reliability modeling, systems analysis and optimization. She took courses covering reliability assessment, statistical process control, simulation, testing and more. Her education provided a strong foundation in the mathematical modeling and analysis skills required for a career in systems engineering.
The document summarizes the Multiview methodology for information system development. The methodology has 5 stages: 1) analysis of human activity, 2) analysis of information, 3) analysis and design of socio-technical aspects, 4) design of the human-computer interface, and 5) design of technical aspects. The final outputs are specifications for the application, information retrieval, database, database maintenance, control, recovery, and monitoring systems.
Design Knowledge Gain by Structural Health MonitoringStroNGER2012
The design of complex structures should be based on advanced approaches able to take into account the behavior of the constructions during their entire life-cycle. Moreover, an effective design method should consider that the modern constructions are usually complex systems, characterized by strong interactions among the single components and with the design environment.
A modern approach, capable of adequately considering these issues, is the so-called performance-based design (PBD). In order to profitably apply this design philosophy, an effective framework for the evaluation of the overall quality of the structure is needed; for this purpose, the concept of dependability can be effectively applied.
In this context, structural health monitoring (SHM)
assumes the essential role to improve the knowledge on the structural system and to allow reliable evaluations of the structural safety in operational conditions. SHM should be planned at the design phase and should be performed during the entire life-cycle of the structure.
In order to deal with the large quantity of data coming from the continuous monitoring various processing techniques exist. In this work different approaches are discussed and in the last part two of them are applied on the same dataset.
It is interesting to notice that, in addition to this first level of knowledge, structural health monitoring allows obtaining a further more general contribution to the design knowledge of the whole sector of structural engineering.
Consequently, SHM leads to two levels of design knowledge gain: locally, on the specific structure, and globally, on the general class of similar structures.
1) The document proposes using an assignment problem linear programming technique to quantify the technical performance of processes in system engineering. The assignment problem can optimize processes by finding minimum compilation time, execution time, and memory allocation.
2) An example assignment problem is described where jobs are assigned to programmers to minimize time. The technique is applied to quantify a software development process by measuring compilation time, execution time, memory usage, and output of sample programs.
3) The results show that programs developed by two of three programmers optimized the process, with minimum memory usage, execution speed and output values, as identified by the assignment problem modeling.
This document discusses risks associated with enterprise resource planning (ERP) projects. It begins by explaining that ERP projects represent large investments for organizations and present new challenges compared to traditional IT projects. The document then reviews literature on risk factors for IT projects, including issues related to organizational fit, skills, management, software design, user involvement, and technology. It identifies some unique risks of ERP projects as re-engineering business processes, investing in new skills, using external consultants, and technological bottlenecks. The summary concludes that case studies highlight challenges of ERP projects include recruiting staff with both business and technical skills.
The document outlines the key stages of a system development cycle project, including planning, design, implementation, testing and maintenance. It discusses project management principles and describes tools used at each stage such as data flow diagrams, decision trees and documentation. Testing involves verifying hardware, software and backups are functioning as intended. Implementation may use direct, parallel, phased or pilot conversion methods. Overall the document provides guidance on managing an information systems project from start to finish.
Analysis of building performance evaluation and valueAlexander Decker
This document discusses building performance evaluation and value management as tools that can be used in building facilities management. It defines building performance evaluation as a process that systematically evaluates the performance and effectiveness of buildings. Value management is defined as a structured process that seeks to achieve value for money by providing necessary functions at the lowest cost. The document suggests that building performance evaluation data should be integrated into value management studies to realize maximum effectiveness of facilities management decisions. It also provides an overview of how performance evaluation and value management can improve facilities management functions and help organizations make better informed facilities decisions.
Systems thinking in innovation project managementMaria Kapsali
1. Conventional project management methods are not effective for managing innovation projects because they are too rigid and focused on control, which does not suit the non-linear nature of innovation.
2. Case studies showed that applying systems thinking concepts like managing external relationships and allowing flexibility to adapt to changes led to more successful projects.
3. Implementing systems thinking is challenging because its concepts are abstract and hard to measure concretely. Future research should study how concepts like holism and flexibility are applied in real project activities and relationships.
Systems Thinking in Innovation Project Management @ EURAM 2010: Systems Think...Maria Kapsali
1. Conventional project management methods are not effective for managing innovation projects because they are too rigid and focused on control, which does not suit the non-linear nature of innovation.
2. Case studies showed that applying systems thinking concepts like managing external relationships and allowing flexibility to adapt to changes led to more successful projects.
3. Implementing systems thinking is challenging because its concepts are abstract and hard to measure concretely. Future research should study how concepts like holism and flexibility are applied in real project activities and relationships.
The document summarizes the Multiview methodology for information system development. The methodology has 5 stages: 1) analysis of human activity, 2) analysis of information, 3) analysis and design of socio-technical aspects, 4) design of the human-computer interface, and 5) design of technical aspects. The final outputs are specifications for the application, information retrieval, database, database maintenance, control, recovery, and monitoring systems.
Design Knowledge Gain by Structural Health MonitoringStroNGER2012
The design of complex structures should be based on advanced approaches able to take into account the behavior of the constructions during their entire life-cycle. Moreover, an effective design method should consider that the modern constructions are usually complex systems, characterized by strong interactions among the single components and with the design environment.
A modern approach, capable of adequately considering these issues, is the so-called performance-based design (PBD). In order to profitably apply this design philosophy, an effective framework for the evaluation of the overall quality of the structure is needed; for this purpose, the concept of dependability can be effectively applied.
In this context, structural health monitoring (SHM)
assumes the essential role to improve the knowledge on the structural system and to allow reliable evaluations of the structural safety in operational conditions. SHM should be planned at the design phase and should be performed during the entire life-cycle of the structure.
In order to deal with the large quantity of data coming from the continuous monitoring various processing techniques exist. In this work different approaches are discussed and in the last part two of them are applied on the same dataset.
It is interesting to notice that, in addition to this first level of knowledge, structural health monitoring allows obtaining a further more general contribution to the design knowledge of the whole sector of structural engineering.
Consequently, SHM leads to two levels of design knowledge gain: locally, on the specific structure, and globally, on the general class of similar structures.
1) The document proposes using an assignment problem linear programming technique to quantify the technical performance of processes in system engineering. The assignment problem can optimize processes by finding minimum compilation time, execution time, and memory allocation.
2) An example assignment problem is described where jobs are assigned to programmers to minimize time. The technique is applied to quantify a software development process by measuring compilation time, execution time, memory usage, and output of sample programs.
3) The results show that programs developed by two of three programmers optimized the process, with minimum memory usage, execution speed and output values, as identified by the assignment problem modeling.
This document discusses risks associated with enterprise resource planning (ERP) projects. It begins by explaining that ERP projects represent large investments for organizations and present new challenges compared to traditional IT projects. The document then reviews literature on risk factors for IT projects, including issues related to organizational fit, skills, management, software design, user involvement, and technology. It identifies some unique risks of ERP projects as re-engineering business processes, investing in new skills, using external consultants, and technological bottlenecks. The summary concludes that case studies highlight challenges of ERP projects include recruiting staff with both business and technical skills.
The document outlines the key stages of a system development cycle project, including planning, design, implementation, testing and maintenance. It discusses project management principles and describes tools used at each stage such as data flow diagrams, decision trees and documentation. Testing involves verifying hardware, software and backups are functioning as intended. Implementation may use direct, parallel, phased or pilot conversion methods. Overall the document provides guidance on managing an information systems project from start to finish.
Analysis of building performance evaluation and valueAlexander Decker
This document discusses building performance evaluation and value management as tools that can be used in building facilities management. It defines building performance evaluation as a process that systematically evaluates the performance and effectiveness of buildings. Value management is defined as a structured process that seeks to achieve value for money by providing necessary functions at the lowest cost. The document suggests that building performance evaluation data should be integrated into value management studies to realize maximum effectiveness of facilities management decisions. It also provides an overview of how performance evaluation and value management can improve facilities management functions and help organizations make better informed facilities decisions.
Systems thinking in innovation project managementMaria Kapsali
1. Conventional project management methods are not effective for managing innovation projects because they are too rigid and focused on control, which does not suit the non-linear nature of innovation.
2. Case studies showed that applying systems thinking concepts like managing external relationships and allowing flexibility to adapt to changes led to more successful projects.
3. Implementing systems thinking is challenging because its concepts are abstract and hard to measure concretely. Future research should study how concepts like holism and flexibility are applied in real project activities and relationships.
Systems Thinking in Innovation Project Management @ EURAM 2010: Systems Think...Maria Kapsali
1. Conventional project management methods are not effective for managing innovation projects because they are too rigid and focused on control, which does not suit the non-linear nature of innovation.
2. Case studies showed that applying systems thinking concepts like managing external relationships and allowing flexibility to adapt to changes led to more successful projects.
3. Implementing systems thinking is challenging because its concepts are abstract and hard to measure concretely. Future research should study how concepts like holism and flexibility are applied in real project activities and relationships.
Selecting A Development Approach For Competitive Advantagemtoddne
Companies that rely on their information systems to provide a competitive advantage must employ development methodologies that: facilitate innovation, improve customer and supplier relationships, and enable change at the speed of business. Potential development approaches include traditional, object-oriented, and vision and value oriented methodologies. The recommended approach is a hybrid methodology that incorporates agility, adaptability, reuse, collaborative thinking, and evolving innovation. At the foundation of this approach are agile development philosophies and practices, and the system designer. From an architectural perspective, the approach utilizes SOAs and SOMA methods. And, design thinking and innovation evolution cycle principles are incorporated to drive system innovations.
This document provides information about getting fully solved assignments from an assignment help service. It includes contact details like email and phone number to send requests to along with sample assignment details like semester, subject code, credits, and marks. The assignment asks students to answer questions related to a restaurant management information system that automates order processing and helps management with tasks like menu planning and cost control. It also contains questions about concepts like business process reengineering, quality parameters of information, neural networks, and a sample PERT network problem to solve.
This document describes a study that combined usability heuristics with Markov models of user behavior to assess interactive system effectiveness. Researchers developed a method to calculate an overall system effectiveness score by combining subjective user ratings based on a usability framework with an objective measure of average clicks predicted by a Markov model. They applied this method to compare an old and new version of an e-commerce website. Results showed the new site received significantly higher effectiveness scores, and its average clicks were accurately predicted by the Markov model, supporting the combined quantitative/qualitative approach.
This document provides a tutorial for using the SuperDecisions software to build decision models using the Analytic Hierarchy Process (AHP) or Analytic Network Process (ANP). It explains the basic concepts of clusters and elements, and how to create a hierarchical model by defining the goal, criteria and alternative clusters, adding elements to each cluster, and connecting the elements. The tutorial also provides an overview of performing pairwise comparisons to obtain priority weights in the decision models. The overall purpose is to demonstrate how to use the SuperDecisions software to structurally model decisions and obtain results using AHP or ANP.
This document provides answers to three questions related to project feasibility analysis and data flow diagrams. For the first question, it discusses the four main types of feasibility studies - technical, operational, economic, and schedule feasibility. It provides examples of questions to address for each type of feasibility study when evaluating a new inventory system project. For the second question, it outlines characteristics of a quality information system such as being better than the existing system, effective, user-friendly, and ensuring accurate data. For the third question, it describes the rules for creating different symbols used in data flow diagrams including processes, data stores, external entities, and data flows.
Applying systemic methodologies to bridge the gap between a process-oriented ...Panagiotis Papaioannou
This work is an application of the Soft Systems Methodology (SSM) to improve an information system to fully support the related process-based management system and help its internal improvement. Design and Control Systemic Methodology (DCSYM) is used as a modelling tool to facilitate conceptual models comparison within the SSM context.
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...QuantUniversity
Model risk and the importance of model risk management has gotten significant attention in the last few years. As financial companies increase their reliance on quants and quantitative models for decision making, they are increasingly exposed to model risk and are looking for ways to mitigate it. The financial crisis of 2008 and various high profile financial accidents due to model failures has brought model risk management to the forefront as an important topic to be addressed. Many regulatory efforts (Solvency II, Basel III, Dodd-Frank etc.) have been initiated obligating banks and financial institutions to incorporate formal model risk management programs to address model risk. Regulatory agencies have issued guidance letters and supervisory insights to assist companies in developing model risk management programs. In the United States, as the Dodd-Frank act is implemented, newer guidance letters have been issued that emphasize model risk management. Despite these efforts, in practice, financial companies continue to struggle in formulating and developing a model risk management program. A lot of companies acknowledge and understand the model risk management guidelines in spirit but have practical challenges in implementing these guidance letters. In our prior article on model risk , we discussed many drivers to address model risk and challenges in integrating model risk into the quant development process. In this talk, we will discuss ten best practices for the implementation of an effective model risk management program. These best practices have evolved from discussions with industry experts and consulting projects we have worked with in the recent years to create robust risk management programs. These best practices meant to provide practical tips for companies embarking on a formal model risk management program or enhancing their model risk methodologies to address the new realities
Qais Yahya Hatim has a PhD in industrial engineering and operations research from Penn State University. He currently works as a statistician and operations research analyst at the FDA, where he applies statistical methods like data mining, multivariate analysis, and Bayesian inference to evaluate pharmaceutical quality data. His research experience includes engineering statistics, supply chain modeling, and manufacturing optimization. He has worked on projects involving production simulation, finite element analysis, and statistical modeling at NIST and IAEC.
This document outlines the course curriculum for MBA students at Alliance University in September 2015. It includes topics like principles of operations management, introduction to logistics, inventory and supply chain management, introduction to project management, total quality management, and operational strategy. It also lists common steps in problem solving such as defining the problem, constructing a model, solving the model, validating the model, and implementing results. Finally, it provides potential topics for student projects related to operations management.
A results-driven Engineering and Information Science, Mathematics, Physics, and Science Teacher with a unique real-world background as an accomplished electrical / biomedical / software engineer, change agent, and trainer working across national and cultural boundaries.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
This summarizes my work during my first year of PhD at Institute for Manufacturing, University of Cambridge where I investigate the feasibility of deploying machine learning under uncertainty for cyber-physical manufacturing systems.
Goal Dynamics_From System Dynamics to ImplementationAmjad Adib
1) The document describes a PhD research proposal on developing dynamic modeling methods for goal dynamics and multi-agent systems.
2) The research aims to analyze and capture goal dynamics in social contexts and provide intelligent agents that can handle complex, distributed events in real-time.
3) The methodology involves defining artifacts and processes, modeling tools, and evaluating the results against objectives through case studies and simulations.
Eugenio Mauri: resumee of the article "From conceptual modelling to requireme...Eugenio Mauri
- Requirements engineering (RE) focuses on requirements elicitation, validation, and representation to better manage change compared to conceptual modeling (CM) which only focused on system functionality.
- RE divides the universe of discourse into three worlds - the subject world, usage world, and system world - related by four types of relationships, whereas CM only considered one relationship.
- Goal-driven and scenario-based approaches in RE help relate organizational objectives to system functions by considering user points of view through normal and exceptional use cases.
towards a model-based framework for development of engineering1 (1)Jinzhi Lu
This document proposes a model-based framework for developing engineering tool-chains that support cyber-physical systems modeling and simulation. It presents the SPIT framework, which takes a systems approach to support MBSE tool-chain development. The framework addresses functionalities of MBSE tool-chains from a systems engineering perspective. Demo tool-chains are developed to support co-simulation of CPS using MBSE. Future work includes extending tool integration languages to formalize co-simulation tool-chains and analyzing the functional dynamics of MBSE enterprise transitioning.
Software requirement analysis enhancements by
prioritizing requirement attributes using rank
based Agents.
Ashok Kumar Vinay Goyal
Professor Assistant Professor
Department of Computer Science and Applications Department of MCA
Kurukshetra University, Kurukshetra, India Panipat Institute of Engineering & Technology
Panipat, India
Abstract- This paper proposes a new technique in the
domain of Agent oriented software engineering. Agents
work in autonomous environments and can respond to
agent triggers. Agents can be very useful in requirement
analysis phase of software development process, where
they can react towards the requirement triggers and
result in aligned notations to identify the best possible
design solution from existing designs. Agent helps in
design generation process, which includes the use of
Artificial intelligence. The results produced clearly
shows the improvements over the conventional
reusability principles and ideas.
1. INTRODUCTION
Agent oriented software engineering is a new
emerging technique which is growing very
rapidly. Software development industries have
invested huge efforts in this domain and results
published by many of them are very exiting [1].
The autonomous and reactive nature of agents
makes it possible for the designers to visualize
in terms of real life problem solving scenarios
where socio-logical [2] characteristics of agents
automatically activate the timely checks for any
problem in domain and to solve the same using
agents.
Agents are very helpful in the software
development life cycle. Experiments carried out
in past have shown [2][9][10] the improvement
in the SDLC and conclusion is that agents can be
very helpful in cost and effort minimization; if
tuned properly. Fine-tuning of agents and SDLC
process-state-plug-in for two-way
communications results in agent based software
development process where intelligent agents
will take decisions for better time and resource
utilization.
Fine-tuning of agents and SDLC process-state-
plug-in for two-way communications results in
agent based software development process
where intelligent agents will take decisions for
better time and resource utilization. Agents are
capable of storing historic data, which helps in
decision-making using heuristic based approach.
This paper discusses the details of one such
experiment conducted to improve the
requirement analysis process with the help of
proactive agents. Agents automatically sense the
requirement environment and propose their own
set of important requirement checklist. This is
sort of intelligent assistance with domain
heuristic, which leads to cover all possible
requirement entities of the problem domain.
2. RELATED WORK
Michael Wooldridge, Nicholas R. Jennings &
David Kinny describe the analysis process using
agent-oriented approach [1]. They have
considered the GAIA notations. The analysis
stages of Gaia are:
1) Identify the agent’s roles in the system, which
typically correspond to identify ro ...
Cybernetics in supply chain managementLuis Cabrera
This document discusses the role of operations research and simulation modeling in developing a cybernetic dynamic simulation model of a manufacturing supply chain system. It notes that production planning is a key but complex component that benefits from mathematical algorithms and computer modeling. Simulation allows analyzing complex systems with many variables and obtaining solutions that aren't possible with closed-form equations. The document provides examples of why simulation is useful and discusses representing real-world processes and testing different configurations and policies.
Compareable between lean and six sigma.docxwrite22
This document provides an overview of Lean and Six Sigma methods for quality improvement. It discusses the key differences between the statistical and business perspectives of Six Sigma. The Six Sigma DMAIC process of Define, Measure, Analyze, Improve, Control is explained. Key Six Sigma strategies, tools, techniques and principles are outlined including the Design for Six Sigma methodology. The benefits and challenges of implementing Six Sigma projects are also addressed.
This document discusses the data mining process and machine learning framework. It describes several approaches to data mining, including CRISP-DM, SEMMA, and KDD. CRISP-DM is explained in depth, with its six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Each phase is described in terms of its goals and tasks. The modeling phase also defines terms like overfitting, underfitting, and fine-tuning. Overall, the document provides an overview of data mining methodologies with a focus on explaining the CRISP-DM process.
Selecting A Development Approach For Competitive Advantagemtoddne
Companies that rely on their information systems to provide a competitive advantage must employ development methodologies that: facilitate innovation, improve customer and supplier relationships, and enable change at the speed of business. Potential development approaches include traditional, object-oriented, and vision and value oriented methodologies. The recommended approach is a hybrid methodology that incorporates agility, adaptability, reuse, collaborative thinking, and evolving innovation. At the foundation of this approach are agile development philosophies and practices, and the system designer. From an architectural perspective, the approach utilizes SOAs and SOMA methods. And, design thinking and innovation evolution cycle principles are incorporated to drive system innovations.
This document provides information about getting fully solved assignments from an assignment help service. It includes contact details like email and phone number to send requests to along with sample assignment details like semester, subject code, credits, and marks. The assignment asks students to answer questions related to a restaurant management information system that automates order processing and helps management with tasks like menu planning and cost control. It also contains questions about concepts like business process reengineering, quality parameters of information, neural networks, and a sample PERT network problem to solve.
This document describes a study that combined usability heuristics with Markov models of user behavior to assess interactive system effectiveness. Researchers developed a method to calculate an overall system effectiveness score by combining subjective user ratings based on a usability framework with an objective measure of average clicks predicted by a Markov model. They applied this method to compare an old and new version of an e-commerce website. Results showed the new site received significantly higher effectiveness scores, and its average clicks were accurately predicted by the Markov model, supporting the combined quantitative/qualitative approach.
This document provides a tutorial for using the SuperDecisions software to build decision models using the Analytic Hierarchy Process (AHP) or Analytic Network Process (ANP). It explains the basic concepts of clusters and elements, and how to create a hierarchical model by defining the goal, criteria and alternative clusters, adding elements to each cluster, and connecting the elements. The tutorial also provides an overview of performing pairwise comparisons to obtain priority weights in the decision models. The overall purpose is to demonstrate how to use the SuperDecisions software to structurally model decisions and obtain results using AHP or ANP.
This document provides answers to three questions related to project feasibility analysis and data flow diagrams. For the first question, it discusses the four main types of feasibility studies - technical, operational, economic, and schedule feasibility. It provides examples of questions to address for each type of feasibility study when evaluating a new inventory system project. For the second question, it outlines characteristics of a quality information system such as being better than the existing system, effective, user-friendly, and ensuring accurate data. For the third question, it describes the rules for creating different symbols used in data flow diagrams including processes, data stores, external entities, and data flows.
Applying systemic methodologies to bridge the gap between a process-oriented ...Panagiotis Papaioannou
This work is an application of the Soft Systems Methodology (SSM) to improve an information system to fully support the related process-based management system and help its internal improvement. Design and Control Systemic Methodology (DCSYM) is used as a modelling tool to facilitate conceptual models comparison within the SSM context.
A Framework Driven Approach to Model Risk Management (www.dataanalyticsfinanc...QuantUniversity
Model risk and the importance of model risk management has gotten significant attention in the last few years. As financial companies increase their reliance on quants and quantitative models for decision making, they are increasingly exposed to model risk and are looking for ways to mitigate it. The financial crisis of 2008 and various high profile financial accidents due to model failures has brought model risk management to the forefront as an important topic to be addressed. Many regulatory efforts (Solvency II, Basel III, Dodd-Frank etc.) have been initiated obligating banks and financial institutions to incorporate formal model risk management programs to address model risk. Regulatory agencies have issued guidance letters and supervisory insights to assist companies in developing model risk management programs. In the United States, as the Dodd-Frank act is implemented, newer guidance letters have been issued that emphasize model risk management. Despite these efforts, in practice, financial companies continue to struggle in formulating and developing a model risk management program. A lot of companies acknowledge and understand the model risk management guidelines in spirit but have practical challenges in implementing these guidance letters. In our prior article on model risk , we discussed many drivers to address model risk and challenges in integrating model risk into the quant development process. In this talk, we will discuss ten best practices for the implementation of an effective model risk management program. These best practices have evolved from discussions with industry experts and consulting projects we have worked with in the recent years to create robust risk management programs. These best practices meant to provide practical tips for companies embarking on a formal model risk management program or enhancing their model risk methodologies to address the new realities
Qais Yahya Hatim has a PhD in industrial engineering and operations research from Penn State University. He currently works as a statistician and operations research analyst at the FDA, where he applies statistical methods like data mining, multivariate analysis, and Bayesian inference to evaluate pharmaceutical quality data. His research experience includes engineering statistics, supply chain modeling, and manufacturing optimization. He has worked on projects involving production simulation, finite element analysis, and statistical modeling at NIST and IAEC.
This document outlines the course curriculum for MBA students at Alliance University in September 2015. It includes topics like principles of operations management, introduction to logistics, inventory and supply chain management, introduction to project management, total quality management, and operational strategy. It also lists common steps in problem solving such as defining the problem, constructing a model, solving the model, validating the model, and implementing results. Finally, it provides potential topics for student projects related to operations management.
A results-driven Engineering and Information Science, Mathematics, Physics, and Science Teacher with a unique real-world background as an accomplished electrical / biomedical / software engineer, change agent, and trainer working across national and cultural boundaries.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
This summarizes my work during my first year of PhD at Institute for Manufacturing, University of Cambridge where I investigate the feasibility of deploying machine learning under uncertainty for cyber-physical manufacturing systems.
Goal Dynamics_From System Dynamics to ImplementationAmjad Adib
1) The document describes a PhD research proposal on developing dynamic modeling methods for goal dynamics and multi-agent systems.
2) The research aims to analyze and capture goal dynamics in social contexts and provide intelligent agents that can handle complex, distributed events in real-time.
3) The methodology involves defining artifacts and processes, modeling tools, and evaluating the results against objectives through case studies and simulations.
Eugenio Mauri: resumee of the article "From conceptual modelling to requireme...Eugenio Mauri
- Requirements engineering (RE) focuses on requirements elicitation, validation, and representation to better manage change compared to conceptual modeling (CM) which only focused on system functionality.
- RE divides the universe of discourse into three worlds - the subject world, usage world, and system world - related by four types of relationships, whereas CM only considered one relationship.
- Goal-driven and scenario-based approaches in RE help relate organizational objectives to system functions by considering user points of view through normal and exceptional use cases.
towards a model-based framework for development of engineering1 (1)Jinzhi Lu
This document proposes a model-based framework for developing engineering tool-chains that support cyber-physical systems modeling and simulation. It presents the SPIT framework, which takes a systems approach to support MBSE tool-chain development. The framework addresses functionalities of MBSE tool-chains from a systems engineering perspective. Demo tool-chains are developed to support co-simulation of CPS using MBSE. Future work includes extending tool integration languages to formalize co-simulation tool-chains and analyzing the functional dynamics of MBSE enterprise transitioning.
Software requirement analysis enhancements by
prioritizing requirement attributes using rank
based Agents.
Ashok Kumar Vinay Goyal
Professor Assistant Professor
Department of Computer Science and Applications Department of MCA
Kurukshetra University, Kurukshetra, India Panipat Institute of Engineering & Technology
Panipat, India
Abstract- This paper proposes a new technique in the
domain of Agent oriented software engineering. Agents
work in autonomous environments and can respond to
agent triggers. Agents can be very useful in requirement
analysis phase of software development process, where
they can react towards the requirement triggers and
result in aligned notations to identify the best possible
design solution from existing designs. Agent helps in
design generation process, which includes the use of
Artificial intelligence. The results produced clearly
shows the improvements over the conventional
reusability principles and ideas.
1. INTRODUCTION
Agent oriented software engineering is a new
emerging technique which is growing very
rapidly. Software development industries have
invested huge efforts in this domain and results
published by many of them are very exiting [1].
The autonomous and reactive nature of agents
makes it possible for the designers to visualize
in terms of real life problem solving scenarios
where socio-logical [2] characteristics of agents
automatically activate the timely checks for any
problem in domain and to solve the same using
agents.
Agents are very helpful in the software
development life cycle. Experiments carried out
in past have shown [2][9][10] the improvement
in the SDLC and conclusion is that agents can be
very helpful in cost and effort minimization; if
tuned properly. Fine-tuning of agents and SDLC
process-state-plug-in for two-way
communications results in agent based software
development process where intelligent agents
will take decisions for better time and resource
utilization.
Fine-tuning of agents and SDLC process-state-
plug-in for two-way communications results in
agent based software development process
where intelligent agents will take decisions for
better time and resource utilization. Agents are
capable of storing historic data, which helps in
decision-making using heuristic based approach.
This paper discusses the details of one such
experiment conducted to improve the
requirement analysis process with the help of
proactive agents. Agents automatically sense the
requirement environment and propose their own
set of important requirement checklist. This is
sort of intelligent assistance with domain
heuristic, which leads to cover all possible
requirement entities of the problem domain.
2. RELATED WORK
Michael Wooldridge, Nicholas R. Jennings &
David Kinny describe the analysis process using
agent-oriented approach [1]. They have
considered the GAIA notations. The analysis
stages of Gaia are:
1) Identify the agent’s roles in the system, which
typically correspond to identify ro ...
Cybernetics in supply chain managementLuis Cabrera
This document discusses the role of operations research and simulation modeling in developing a cybernetic dynamic simulation model of a manufacturing supply chain system. It notes that production planning is a key but complex component that benefits from mathematical algorithms and computer modeling. Simulation allows analyzing complex systems with many variables and obtaining solutions that aren't possible with closed-form equations. The document provides examples of why simulation is useful and discusses representing real-world processes and testing different configurations and policies.
Compareable between lean and six sigma.docxwrite22
This document provides an overview of Lean and Six Sigma methods for quality improvement. It discusses the key differences between the statistical and business perspectives of Six Sigma. The Six Sigma DMAIC process of Define, Measure, Analyze, Improve, Control is explained. Key Six Sigma strategies, tools, techniques and principles are outlined including the Design for Six Sigma methodology. The benefits and challenges of implementing Six Sigma projects are also addressed.
This document discusses the data mining process and machine learning framework. It describes several approaches to data mining, including CRISP-DM, SEMMA, and KDD. CRISP-DM is explained in depth, with its six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Each phase is described in terms of its goals and tasks. The modeling phase also defines terms like overfitting, underfitting, and fine-tuning. Overall, the document provides an overview of data mining methodologies with a focus on explaining the CRISP-DM process.
Machine Learning Approach for Quality Assessmentand Prediction in Large Soft...RAKESH RANA
This document proposes a machine learning approach for assessing and predicting software quality in large organizations. It suggests using ML techniques within the ISO/IEC 15939 measurement information model framework. Specifically, it recommends using historical metrics data to train ML models that can classify different quality characteristics and predict overall quality based on measurable attributes, without needing to explicitly define the relationships. The proposed approach has benefits like being self-improving as more data is collected over time, making it suitable for software quality analysis in large organizations.
An Assessment Model Study for Lean and Agile (Leagile) Index by Using Fuzzy AHPDr. Lutfi Apiliogullari
This document describes a study that develops an assessment model to evaluate companies on their level of implementing lean and agile principles and strategies. The study uses fuzzy analytic hierarchy process (fuzzy AHP) and decision making trial and evaluation laboratory (DEMATEL) methods to determine the important lean and agile criteria and their relationships. Lean and agile criteria are identified from literature and expert opinions. Fuzzy AHP is used to assign weights to the criteria. The model is applied to a company to calculate their initial lean/agile index. Improvements are then made and the index is recalculated to test the model. The goal is to help companies assess their situation regarding lean and agile implementation and identify areas for improvement.
The MS in Management Engineering program in the Philippines enhances the knowledge and skills of engineering graduates in technology and management perspectives. The 2-year program provides exposure to engineering management, operations engineering, optimization, and stochastic modelling. It requires recommendation letters, medical records, transcripts, certificates, and an entrance exam for admission. Graduates can work as managers in engineering firms, manufacturing plants, or colleges as professors, program heads, or department heads. Management engineering utilizes industrial engineering skills to develop efficient business processes and strategies through projects focused on quality improvement, management support, workflow, scheduling, organization, and decision making.
Applications Of Statistics In Software EngineeringKristen Carter
This document discusses applications of statistics in software engineering. It introduces a special issue that highlights papers applying statistical methods to solve software engineering problems and improve decision making. The issue includes papers on using statistical significance testing and Bayesian belief networks for risk management, using regression splines to understand factors affecting code inspection effectiveness, using Markov chains for reliability modeling, and applying clustering techniques for software partitioning and recovery. The document emphasizes that statistical analysis can help manage uncertainties in development, but challenges remain in collecting good data and integrating these methods into practice and education.
This document provides a summary of David O'Leary's qualifications, including his education, certification, skills, professional experience, research projects and activities. He received a Bachelor of Science in Industrial Engineering from the University of Pittsburgh and holds a Six Sigma Green Belt Certification. Through professional co-op rotations and consulting projects, he has experience with lean manufacturing concepts, process improvement, and financial and data analysis. His skills include Microsoft Office, project management software and CAD programs.
This presentations covers Definition of Operations Research , Models, Scope,Phases ,advantages,limitations, tools and techniques in OR and Characteristics of Operations research
1. Graduate Work – Systems Engineering
Lynn Coupal
Description of the education (courses etc.) and practical training or skills in my
background that are relevant to my success in Systems Engineering.
Most of my education and background has been primarily in Mathematics. My Undergraduate
degrees were Mathematics and Secondary Mathematics Education. I pursued my Masters degree
in Systems Engineering.
I studied the book entitled Case Studies in Reliability and Maintenance. There were a lot of
topics covered in Probability and Statistics, including but not limited to: modeling, reliability
assessment and prediction, simulation, testing, failure analysis, statistical process control,
regression analysis and reliability growth modeling and analysis. Most cases consisted of
mathematical modeling.
At the undergraduate level, I took a few Probability and Statistics courses. One was an advanced
Probability and Statistics class that was Calculus based. Additionally, one of the first classes
completed towards my master’s program was entitled Probability and Statistics for Scientists
and Engineers (NMTH 6701). My Calc. based Probability and Statistics class helped to prepare
me for the type of mathematics necessary for mastery of this course. In the Probability and
Statistics for Scientists and Engineers class, probability models and statistical methods were used
to analyze data. This course provided a comprehensive introduction to the use of models and
methods most likely that I will encounter in my future career as an engineer.
NSPP6325 – Integrated Design and Manufacturing - This course introduced me to a process
approach to engineering design, manufacturing, and service applications. Models, modeling
tools, solution approaches, and methodologies for analysis and improvement of processes,
including the product development and manufacturing processes were discussed. The science of
process modeling and analysis was illustrated with case studies – which appears to have some
similarities with the layout of System Testing and Reliability.
NSYS6120 – Systems Engineering and Analysis - This course introduced me to an organized
multidisciplinary approach to designing and developing systems. I was able to explore concepts,
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2. Graduate Work – Systems Engineering
Lynn Coupal
principles, and practices of systems engineering as applied to large integrated systems.
Discussion topics included requirements development, life-cycle costing, scheduling, risk
management, functional analysis, conceptual and preliminary design, testing and evaluation,
optimization, and modeling. This time, the approach is where the similarity lies with System
Testing and Reliability.
NSYS6140 – Systems Optimization and Analysis - This course introduced me to the theory and
practice of optimal system design as an element of the engineering design process. I learned how
to apply optimization as a tool in the various stages of product realization and management of
engineering and manufacturing activities. The course stressed the importance of application of
nonlinear programming methods. Topics included optimality criteria, gradient- and nongradient-
based unconstrained methods, and modern nonlinear programming methods such as penalty
functions, method of multipliers, generalized reduced gradient, and successive quadratic
programming. Special attention was given to large structured problems that naturally occur in
engineering practice. We were exposed to modern optimization software (e.g., OPTLIB, OPT,
BIAS) and extensive comparative results. Examples were cited from mechanical, electrical, civil,
and chemical engineering, as well as from engineering management. There was quite a lot of
mathematical formulation involved.
NSYS6160 – Systems Engineering Management - This course provided me the necessary
techniques for planning and controlling systems, including evaluating the schedule and
operational effectiveness of systems management strategies. Performance measurement, work
breakdown structures, cost estimating, and quality management were discussed. This course also
briefly covered configuration management, standards, and case studies of systems from different
applications areas. The employment of case studies is a commonality amongst many of the
courses.
NSYS6163 – Integrated Risk Management - This course provided an introduction to the theory
and methodology of risk management in the context of systems engineering. It addressed topics
including risk identification, risk ranking and filtering, performance metrics, event and fault
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3. Graduate Work – Systems Engineering
Lynn Coupal
trees, theory of extreme values, decisions on extreme events, combinatorial optimization,
systems configuration, network modeling, and system interdependencies. Knowledge of
probability and statistics was assumed. Once again, there is a commonality with the mathematics
involved.
NMGT8750 - Total Quality Management and Improvement - This course provided a historical
overview and a fundamental understanding of the subject including: statistical thinking, the 7
basic tools, quality systems, managing operations for quality, product quality, process quality,
customer satisfaction, the role of quality as a competitive tool, critical elements that differentiate
high performing organizations from their competitors, the quality improvement process and how
organizations deliver ever-improving value to customers, Daily Work Management, Quality
Function Deployment, Six Sigma, the psychology of quality, and managing people in a quality
environment.
NMBA6130 - Leadership and Teamwork - This course provided an overview of leadership and
teamwork with an emphasis on how leaders and teams manage change in a dynamic technology
and business environment. The course was structured into four broad modules: Level-Three
Leadership, Creating and Sustaining Collaboration, Leading in the New Workplace, and Leading
Change. In each module, I considered various frameworks and perspectives, and applied them to
case studies and other examples. By engaging with the class and its online learning community, I
gained critical expertise in navigating this new leadership landscape.
NMBA6313 - Supply Chain Management - This course provided a simulation to try to achieve a
strategic advantage that was required for effective design and integration of multiple players and
activities throughout the supply chain. I gained an understanding of the definition and scope of
supply chain management and an appreciation of the potential for businesses to improve bottom-
line performance through an integrated, strategic approach to the management of supply chains.
Managing the simulation gave a basic understanding of the roles of the various entities in
managing the supply chain, the interrelatedness of critical activities, and a strategic view of the
importance of supply chain management. The LINKS Supply Chain Management Simulation
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4. Graduate Work – Systems Engineering
Lynn Coupal
provided me with hands-on experience with the cross-functional impact of supply chain
decision-making: analyzing complex data; evaluating the costs and benefits of cross-functional
trade-offs; making critical supply chain decisions; evaluating the consequences of those
decisions; and working to continuously improve based on experience.
NSYS6152 - System Testing and Reliability - This course provided classical techniques and
concepts necessary for evaluating the long-term and short-term reliability of engineering
systems. Strategies were explored for integrating, testing, and validating products and systems.
This course provided an in-depth coverage of tasks, processes, methods, and techniques for
achieving, testing, and maintaining the required level of system reliability considering
operational performance, customer satisfaction, and affordability. Specific topics included the
integration of established system requirements, establishing system reliability requirements,
reliability program planning, system reliability modeling and analysis, system reliability design
guidelines and analysis, system reliability test and evaluation, verification and validation of a
system, and the maintenance of inherent system reliability during production and operation.
NEEC6501- Random Processes for Engineering Applications - This course provided a
background on communication systems and computer networks and how they are designed to
provide high performance consistently and reliably in the presence of noisy communication
channels; equipment faults; a wide range of media applications that combine voice, images and
video; and high variability in user demand. Probability models provided the mathematical
framework for characterizing random variability and formed the basis for tools to design systems
that perform predictably in the face of random inputs and environments. The concept of a
random variable and its characterization using a probability distribution function and associated
moments was reviewed. The focus was on characterizing the joint behavior of multiple random
variables to understand their interdependence and to enable prediction of likely outcomes. The
joint distribution function as well as the correlation and the covariance functions were essential
tools in achieving these objectives. Random processes described signals and dynamic behavior
encountered in engineering systems. The utility of probability models was demonstrated through
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5. Graduate Work – Systems Engineering
Lynn Coupal
applications in communication systems, reliability, digital signal processing, and
communications networks.
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