This document discusses various job sequencing rules and models that can be used to determine the optimal sequence of jobs on machines to minimize idle time. It describes 11 rules for sequencing single jobs on one machine, including first-in first-out, last-in first-out, earliest due date, shortest process time, and longest process time. For sequencing multiple jobs on multiple machines, it outlines Johnson's rule for sequencing jobs on two machines to minimize completion time. It also lists assumptions and modeling approaches for job sequencing problems.
The document provides a syllabus for an operations research course covering topics like linear programming, transportation problems, assignment problems, and sequencing. Linear programming topics include formulation, graphical solution, and the simplex method. Transportation problems cover modeling the transportation of resources from origins to destinations to meet demands. Assignment problems involve allocating jobs to workers efficiently.
The document describes an Operations Research course. It includes 8 units covering topics like linear programming, transportation problems, queuing theory, PERT-CPM techniques, game theory, and integer programming. It provides details of each unit including the number of lecture hours and the topics to be covered. It also lists the textbooks and reference books for the course. The course aims to introduce students to various operations research techniques and their applications in decision making.
Simulation involves imitating the operation of a real-world process over time, usually on a computer. It is widely used for decision making and analyzing complex systems that cannot be solved mathematically. A simulation study involves problem formulation, model conceptualization, validation, experimentation, and implementation. Key aspects of a model include entities, attributes, resources, variables, events, and activities.
The document discusses using simulation to model queuing problems with random numbers. It describes queuing systems as having arrivals, a waiting line, service, and departure components. A single queue-single service point queuing structure is examined, with first-come, first-served queue discipline and random inter-arrival and service times. An example problem simulates 10 customer arrivals at a retail store using random numbers to estimate average waiting time and server idle time percentage. The solution shows calculating arrival and service time probabilities, simulating customer service, and finding total 4 minutes of waiting time and 12 minutes of idle time over 53 minutes.
The document discusses simulation as a technique for modeling real-world systems with uncertain inputs. It defines simulation as using models to represent systems over time to understand their behavior. The key aspects covered include:
- Components of a simulation model including inputs, calculations, and outputs
- Types of simulation like time-dependent vs time-independent and corporate/financial simulations
- Major applications in queuing systems and analyzing waiting times
- Steps of the simulation process from identifying the problem to evaluating results
- Components and structures of queuing systems like arrivals, queues, service, and departure.
The document discusses simulation theory and the Monte Carlo method of simulation. It defines simulation as imitating reality and explains that simulation is used to understand complex systems when real experimentation is not possible or analytical solutions are unknown. It describes the Monte Carlo method as using probability distributions and random numbers to simulate random systems. The key steps are: (1) obtaining variable probabilities from data, (2) converting to cumulative probabilities, (3) generating random numbers, (4) mapping random numbers to probability intervals to determine outcomes, and (5) repeating simulations. An example demonstrates using cumulative probabilities and random numbers to simulate daily cake demand for a bakery.
Six Sigma Methods and Formulas for Successful Quality ManagementIJERA Editor
This document discusses Six Sigma methods and formulas for quality management. It begins by introducing Six Sigma and defining key terms like defects per million opportunities and standard deviation. It then presents the Six Sigma implementation process (DMAIC), outlining the Define, Measure, Analyze, Improve, and Control phases. In the Define phase, projects are chartered and teams assembled. The Measure phase involves collecting data and doing capability analyses. The Analyze phase uses statistical tests like t-tests and ANOVA to find sources of variation. Formulas for measures of central tendency, dispersion, hypothesis testing, and process capability are also provided.
The document provides a syllabus for an operations research course covering topics like linear programming, transportation problems, assignment problems, and sequencing. Linear programming topics include formulation, graphical solution, and the simplex method. Transportation problems cover modeling the transportation of resources from origins to destinations to meet demands. Assignment problems involve allocating jobs to workers efficiently.
The document describes an Operations Research course. It includes 8 units covering topics like linear programming, transportation problems, queuing theory, PERT-CPM techniques, game theory, and integer programming. It provides details of each unit including the number of lecture hours and the topics to be covered. It also lists the textbooks and reference books for the course. The course aims to introduce students to various operations research techniques and their applications in decision making.
Simulation involves imitating the operation of a real-world process over time, usually on a computer. It is widely used for decision making and analyzing complex systems that cannot be solved mathematically. A simulation study involves problem formulation, model conceptualization, validation, experimentation, and implementation. Key aspects of a model include entities, attributes, resources, variables, events, and activities.
The document discusses using simulation to model queuing problems with random numbers. It describes queuing systems as having arrivals, a waiting line, service, and departure components. A single queue-single service point queuing structure is examined, with first-come, first-served queue discipline and random inter-arrival and service times. An example problem simulates 10 customer arrivals at a retail store using random numbers to estimate average waiting time and server idle time percentage. The solution shows calculating arrival and service time probabilities, simulating customer service, and finding total 4 minutes of waiting time and 12 minutes of idle time over 53 minutes.
The document discusses simulation as a technique for modeling real-world systems with uncertain inputs. It defines simulation as using models to represent systems over time to understand their behavior. The key aspects covered include:
- Components of a simulation model including inputs, calculations, and outputs
- Types of simulation like time-dependent vs time-independent and corporate/financial simulations
- Major applications in queuing systems and analyzing waiting times
- Steps of the simulation process from identifying the problem to evaluating results
- Components and structures of queuing systems like arrivals, queues, service, and departure.
The document discusses simulation theory and the Monte Carlo method of simulation. It defines simulation as imitating reality and explains that simulation is used to understand complex systems when real experimentation is not possible or analytical solutions are unknown. It describes the Monte Carlo method as using probability distributions and random numbers to simulate random systems. The key steps are: (1) obtaining variable probabilities from data, (2) converting to cumulative probabilities, (3) generating random numbers, (4) mapping random numbers to probability intervals to determine outcomes, and (5) repeating simulations. An example demonstrates using cumulative probabilities and random numbers to simulate daily cake demand for a bakery.
Six Sigma Methods and Formulas for Successful Quality ManagementIJERA Editor
This document discusses Six Sigma methods and formulas for quality management. It begins by introducing Six Sigma and defining key terms like defects per million opportunities and standard deviation. It then presents the Six Sigma implementation process (DMAIC), outlining the Define, Measure, Analyze, Improve, and Control phases. In the Define phase, projects are chartered and teams assembled. The Measure phase involves collecting data and doing capability analyses. The Analyze phase uses statistical tests like t-tests and ANOVA to find sources of variation. Formulas for measures of central tendency, dispersion, hypothesis testing, and process capability are also provided.
The information in this slide is very useful for me to do the assignment regarding the simulation in which we have to report together with the presentation...
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine the optimal solution to decision problems with limited resources. Linear programming is then introduced as a type of mathematical modeling where the objective function and constraints are linear. The key aspects of a linear programming problem are defined as the decision variables, objective function to maximize or minimize, and constraints. Graphical solutions and examples of linear programming problems are also provided.
Simulation is used to create models that represent real world systems and allow experimenting with different strategies without impacting the actual system. Models simplify real systems for analysis while maintaining key behaviors and results. Successful simulation models are easy to understand, represent the system accurately, produce fast results, and allow control and updating. Simulators are used when real experimentation is unsafe, too expensive, or when systems are still in development. Common uses of simulation include modeling systems in fields like military, education, healthcare, and engineering.
This document discusses desirable features for simulation software. It identifies general capabilities like modeling flexibility, ease of use, and debugging aids as important. Hardware/software considerations and statistical capabilities are also important factors. Good animation, documentation, customer support, and output reports are desirable as well. Flexibility, ease of use, statistical tools, and visualization are key aspects to consider in choosing simulation software.
A computer simulation is a computer model that recreates a real-world system over time. Simulations are often used for training purposes when recreating situations in real life would be too difficult, expensive, or dangerous. A key example is a flight simulator, which uses software to simulate the aircraft, environment, and instrument readings and allows pilots to practice responding to dangerous situations safely.
This document discusses simulation modeling and its applications. It begins with definitions of simulation as operating a model of a system over time to study its behavior. Simulation is used to evaluate system performance under different configurations before implementation. The key advantages are exploring "what if" scenarios without disrupting real systems and testing new designs. Common applications include manufacturing, construction, military, logistics and transportation. The document outlines the steps in a simulation study and discusses when simulation is appropriate versus not. It concludes with references on modeling and simulation.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document provides notes for an introduction to simulation course. It defines key terms like system, entities, events, and different types of models. It explains that simulation is useful for evaluating systems that would be too complex, expensive or dangerous to experiment on directly. The document outlines the goals of the course as understanding simulation concepts, mathematics, programming and implementing simulation projects. It also discusses different approaches to representing time in a simulation, like next-event time advance and fixed-increment time advance.
Machine Learning & Artificial Intelligence - Machine Controlled Data Dispensa...STePINForum
This document describes a machine learning approach to identifying sensitive data in databases. It is a 4-step process:
1. Creating a training set by extracting non-null values from database tables.
2. Creating a test set similarly from the same tables.
3. Fine-tuning the machine learning algorithm by running it iteratively on the training set until it achieves 100% accuracy.
4. Validating the algorithm by running it on the test set and comparing its predictions to the training set, storing the results.
The approach aims to reduce time and effort for identifying sensitive fields compared to manual or rule-based methods, and provides accurate and scalable sensitive data discovery.
A brief introduction to network simulation and the difference between simulator and emulator along with the most important types of simulations techniques.
Which of the following is an input to the master production schedule (mps)ramuaa130
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
Which of the following is not a problem definition tool from the operations c...ramuaa130
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
The centroid method for plant location uses which of the following dataramuaa128
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
Scheduling By Using Fuzzy Logic in ManufacturingIJERA Editor
This paper represents the scheduling process in furniture manufacturing unit. It gives the fuzzy logic application in flexible manufacturing system. Flexible manufacturing systems are production system in furniture manufacturing unit. FMS consist of same multipurpose numerically controlled machines. Here in this project the scheduling has been done in FMS by using fuzzy logic tool in Matlab software. The fuzzy logic based scheduling model in this paper will deals with the job and best alternative route selection with multi-criteria of machine. Here two criteria for job and sequencing and routing with rules. This model is applicable to the scheduling of any manufacturing industry.
The document discusses various concepts and techniques related to short-term scheduling operations management. It covers topics like capacity planning, aggregate scheduling, master scheduling, forward and backward scheduling, scheduling criteria, sequencing rules, priority dispatching, bottleneck identification and management, and finite capacity scheduling. The goal of scheduling is to optimize resource use so that production objectives are met.
The document discusses various concepts and techniques related to short-term scheduling operations management. It covers topics like capacity planning, aggregate scheduling, master scheduling, forward and backward scheduling, scheduling criteria, sequencing rules, priority dispatching, bottleneck identification and management, and finite capacity scheduling. The goal of scheduling is to optimize resource use so that production objectives are met.
Operational research (OR) is the scientific approach to problem solving and decision making. It involves modeling complex real-world situations and using analytical methods to evaluate solutions and help decision makers choose optimal alternatives. Some key OR techniques include linear programming, simulation, and data analysis. OR has been successfully applied in many fields like transportation, manufacturing, healthcare, and the airline industry to improve efficiency, maximize profits, and aid strategic planning. The document provides an overview of OR methodology, history, applications, and examples of its use.
This document discusses workcenter scheduling and related topics. It introduces workcenter scheduling and its objectives like meeting due dates, minimizing lead times and setup times. It also discusses job sequencing rules like first come first serve, shortest processing time and earliest due date. Methods for scheduling jobs on 1, 2 and multiple machines are presented, including Johnson's rule and assignment method. Finally, it briefly covers personnel scheduling in services, like deriving staffing plans and applying the first hour principle.
Which of the following is considered a primary report in an mrp systemramuaa130
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
From an operational perspective, yield management is most effective under whi...ramuaa127
For more course tutorials visit
Uophelp is now newtonhelp.com
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
In hau lee's uncertainty framework to classify supply chainsramuaa127
This document provides a guide to the OPS 571 Final Exam, including 29 multiple choice practice questions covering topics like operations and supply chain management, production processes, inventory management, project management, forecasting, and more. The questions assess understanding of key concepts and tools used in operations, supply chain, and project management.
The information in this slide is very useful for me to do the assignment regarding the simulation in which we have to report together with the presentation...
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine the optimal solution to decision problems with limited resources. Linear programming is then introduced as a type of mathematical modeling where the objective function and constraints are linear. The key aspects of a linear programming problem are defined as the decision variables, objective function to maximize or minimize, and constraints. Graphical solutions and examples of linear programming problems are also provided.
Simulation is used to create models that represent real world systems and allow experimenting with different strategies without impacting the actual system. Models simplify real systems for analysis while maintaining key behaviors and results. Successful simulation models are easy to understand, represent the system accurately, produce fast results, and allow control and updating. Simulators are used when real experimentation is unsafe, too expensive, or when systems are still in development. Common uses of simulation include modeling systems in fields like military, education, healthcare, and engineering.
This document discusses desirable features for simulation software. It identifies general capabilities like modeling flexibility, ease of use, and debugging aids as important. Hardware/software considerations and statistical capabilities are also important factors. Good animation, documentation, customer support, and output reports are desirable as well. Flexibility, ease of use, statistical tools, and visualization are key aspects to consider in choosing simulation software.
A computer simulation is a computer model that recreates a real-world system over time. Simulations are often used for training purposes when recreating situations in real life would be too difficult, expensive, or dangerous. A key example is a flight simulator, which uses software to simulate the aircraft, environment, and instrument readings and allows pilots to practice responding to dangerous situations safely.
This document discusses simulation modeling and its applications. It begins with definitions of simulation as operating a model of a system over time to study its behavior. Simulation is used to evaluate system performance under different configurations before implementation. The key advantages are exploring "what if" scenarios without disrupting real systems and testing new designs. Common applications include manufacturing, construction, military, logistics and transportation. The document outlines the steps in a simulation study and discusses when simulation is appropriate versus not. It concludes with references on modeling and simulation.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document provides notes for an introduction to simulation course. It defines key terms like system, entities, events, and different types of models. It explains that simulation is useful for evaluating systems that would be too complex, expensive or dangerous to experiment on directly. The document outlines the goals of the course as understanding simulation concepts, mathematics, programming and implementing simulation projects. It also discusses different approaches to representing time in a simulation, like next-event time advance and fixed-increment time advance.
Machine Learning & Artificial Intelligence - Machine Controlled Data Dispensa...STePINForum
This document describes a machine learning approach to identifying sensitive data in databases. It is a 4-step process:
1. Creating a training set by extracting non-null values from database tables.
2. Creating a test set similarly from the same tables.
3. Fine-tuning the machine learning algorithm by running it iteratively on the training set until it achieves 100% accuracy.
4. Validating the algorithm by running it on the test set and comparing its predictions to the training set, storing the results.
The approach aims to reduce time and effort for identifying sensitive fields compared to manual or rule-based methods, and provides accurate and scalable sensitive data discovery.
A brief introduction to network simulation and the difference between simulator and emulator along with the most important types of simulations techniques.
Which of the following is an input to the master production schedule (mps)ramuaa130
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
Which of the following is not a problem definition tool from the operations c...ramuaa130
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
The centroid method for plant location uses which of the following dataramuaa128
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
Scheduling By Using Fuzzy Logic in ManufacturingIJERA Editor
This paper represents the scheduling process in furniture manufacturing unit. It gives the fuzzy logic application in flexible manufacturing system. Flexible manufacturing systems are production system in furniture manufacturing unit. FMS consist of same multipurpose numerically controlled machines. Here in this project the scheduling has been done in FMS by using fuzzy logic tool in Matlab software. The fuzzy logic based scheduling model in this paper will deals with the job and best alternative route selection with multi-criteria of machine. Here two criteria for job and sequencing and routing with rules. This model is applicable to the scheduling of any manufacturing industry.
The document discusses various concepts and techniques related to short-term scheduling operations management. It covers topics like capacity planning, aggregate scheduling, master scheduling, forward and backward scheduling, scheduling criteria, sequencing rules, priority dispatching, bottleneck identification and management, and finite capacity scheduling. The goal of scheduling is to optimize resource use so that production objectives are met.
The document discusses various concepts and techniques related to short-term scheduling operations management. It covers topics like capacity planning, aggregate scheduling, master scheduling, forward and backward scheduling, scheduling criteria, sequencing rules, priority dispatching, bottleneck identification and management, and finite capacity scheduling. The goal of scheduling is to optimize resource use so that production objectives are met.
Operational research (OR) is the scientific approach to problem solving and decision making. It involves modeling complex real-world situations and using analytical methods to evaluate solutions and help decision makers choose optimal alternatives. Some key OR techniques include linear programming, simulation, and data analysis. OR has been successfully applied in many fields like transportation, manufacturing, healthcare, and the airline industry to improve efficiency, maximize profits, and aid strategic planning. The document provides an overview of OR methodology, history, applications, and examples of its use.
This document discusses workcenter scheduling and related topics. It introduces workcenter scheduling and its objectives like meeting due dates, minimizing lead times and setup times. It also discusses job sequencing rules like first come first serve, shortest processing time and earliest due date. Methods for scheduling jobs on 1, 2 and multiple machines are presented, including Johnson's rule and assignment method. Finally, it briefly covers personnel scheduling in services, like deriving staffing plans and applying the first hour principle.
Which of the following is considered a primary report in an mrp systemramuaa130
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
From an operational perspective, yield management is most effective under whi...ramuaa127
For more course tutorials visit
Uophelp is now newtonhelp.com
www.newtonhelp.com
1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
In hau lee's uncertainty framework to classify supply chainsramuaa127
This document provides a guide to the OPS 571 Final Exam, including 29 multiple choice practice questions covering topics like operations and supply chain management, production processes, inventory management, project management, forecasting, and more. The questions assess understanding of key concepts and tools used in operations, supply chain, and project management.
The document provides a guide for an OPS 571 final exam with 30 multiple choice questions covering operations and supply chain management topics such as capacity utilization, activity system maps, production layouts, queuing theory, inventory models, lean principles, critical path method, forecasting techniques, yield management, transaction processing, master production scheduling, and plant location models.
For more course tutorials visit
Uophelp is now newtonhelp.com
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
The document discusses process management in operating systems. It defines a process as a program in execution that requires resources like processing, memory and I/O. It describes how an operating system handles multiple processes through concepts like multi-programming and time-sharing to improve processor utilization. Process states like ready, running and waiting are discussed along with process scheduling and different scheduling policies.
A simple project listing of five activities and their respective time estimat...yearstart1
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1.
Which of the following is a measure of operations and supply management efficiency used by Wall Street?
Dividend payout ratio
Receivable turnover
Current ratio
Financial leverage
Earnings per share growth
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
2. It is common problem in the production
process to find the sequence of the jobs that
will result in least idle time for the better
utilization of equipment.
For example one has to wear socks before
wearing shoe.
3. It will be most convenient to study job
sequence in the following models
1.n jobs X 1 machine
2.n jobs X 2 machines
3.n jobs X 3 machines
4.n jobs X m machines
5. 2 jobs X n machines
4. 1. n jobs X 1 machine
When deferent letters are to be typed by a
typist he has to decide the order in which he
as to type.
When a student has some questions to
answer
in exam he would first choose the order in
which he has present to make it effective.
In the same way when certain jobs are to be
done in a production on one machine only,
then it is the turn of the engineer to decide
the order of these jobs to process.
5. The following are the rules from which a
person can select for his job shop sequencing
when only one machine is available
1. First In First Out (FIFO):
When the jobs do not require any
preferential
treatment, this rule is considered.
Complaints at telecom departments,
electricity departments, trains on single track
or platform, customers a retailer or a
telephone booth etc., will following rule.
6. 2.Last In First Out(LIFO):
When a machine is dismantled for repair or
overhaul, the parts are put back in LIFO
system. Pipeline laying in water works,
electrical wiring maintenance system will
follow this model. The office filling system,
loading and unloading of trucks,
dressing/undressing shirt and a coat,
wearing/removing socks and shoe are some
more common examples that occur in daily
life to follow LIFO
7. 3. Earliest Due Date (EDD):
Most of the times the production
departments are asked for the probable
time of completion of the job and based on
these promises the production engineer
plants his shop production.
4.Shortest Process Time (SPT):
This is another policy to apply on the
engineering jobs with the concept that the
jobs those take less time to perform are
taken first. A student while writing his exam
would prefer to write the question that
takes lesser time to that takes longer
duration.
8. 5.Longest process Time (LPT):
in contrast to the above , the job takes
longer time will be taken up first and the jobs
which take smaller time will be taken up last.
6.Pre-emptive Priority Rule:
When a job is very urgent , it will be taken
up on priority basis by attending
immediately stopping all other jobs. Under
this rule the highest priority job is allowed to
enter into the service immediately even if
another job with lower priority is already in
service. Doctors use this discipline when
emergency cases arrive to their clinics.
9. 7.Non Pre-emptive priority Rule:
In this case, highest priority goes ahead in
sequence but service is started immediately
after completion of the current service. For
example , a doctor gives priority to a medical
representative , but the gives the
appointment after the finishing the current
job.
10. 8.Priority Service:
Priority is given to certain jobs by the virtue
of their importance or recommendation of
top officials or expediting jobs etc.
9.Select In Random Order(SIRO):
Under this rule the jobs are selected at
random for operation irrespective their
arrival,
urgency , due date etc.
11. 10.Minimum cost Rule:
The jobs are selected in the ascending order
of their costs.
11.Maximum Profit Rule : The jobs are
selected in the descending order of the
profits
In the above the first five are static in nature
and the rest are dynamic
12. Sequencing of the n jobs X2 Machines:
Johnson’s rules:
S.M Johnson’s suggested a sequencing rule
for a situation where is a group of n jobs to
be processed through two successive work
centers .The rule ensures minimum
completion time for the group of n jobs by
minimsing the total idle times of the work
centers.
13. Assumptions for n jobs X2 sequencing:
The following assumptions are usually made
while dealing with sequencing problems.
1. No passing rule is allowed strictly i.e., the
same order of jobs is maintained over each
machine . In other words , a job can not be
processed 2ndmachine unless it is processed
on 1st machine.
2.Only one operation is carryout on a
machine at a time
14. 3.Processing times are know and do not
change
4.Processing time of a job on machine is
independent of other jobs.
5. Then time involved in moving jobs from
one
Machine to another is negligible
6.Each operation , once started ,must be
completed.
7.An operation must be completed before its
succeeding operation Can start.
15. 8.One machine of each type is available.
9.A job is processed as soon as possible , but
only in the order specified.
10.All the men/machines work with
consistent, efficiency.
11 . Setting time is either include process
time or neglected
12.No reworking is allowed.
16. The Rule:
Identify the job with lowest processing time
among all the jobs on both the machines
If this shortest processing time happens to
belong to the first machine then this job is placed
first in sequence, if the shortest processing time
happens to belong to the second work centre,
this job is put last in the sequence.
20. Simulation is a quantitative technique that
conducting a series of repeated trail and error
experiment on a prototype to predict the behavior
of the original system over a period of time.
A Simulation of a system is the operation of a
model, which is a representation of that system.
The model is amenable to manipulation which
would be impossible, too expensive, or too
impractical to perform on the system which it
portrays.
The operation of the model can be studied, and
from this, properties concerning the behavior of
the actual system can be inferred.
21. Designing and analyzing manufacturing
systems
Evaluating H/W and S/W requirements for a
computer system
Evaluating a new military weapons system or
tactics
Determining ordering policies for an
inventory system
Designing communications systems and
message protocols for them
22. Designing and operating transportation
facilities such as freeways, airports, subways,
or ports
Evaluating designs for service organizations
such as hospitals, post offices, or fast-food
restaurants
Analyzing financial or economic systems
23. 1. Define an achievable goal
2. Put together a complete mix of skills on
the team
3. Involve the end-user
4. Choose the appropriate simulation tools
5. Model the appropriate level(s) of detail
6. Start early to collect the necessary input
data
24. 7. Provide adequate and on-going documentation
8. Develop a plan for adequate model
verification
(Did we get the “right answers ?”)
9. Develop a plan for model validation
(Did we ask the “right questions ?”)
10. Develop a plan for statistical output analysis
25. “To model the…” is NOT a goal!
“To model the…in order to
select/determine feasibility/…is a
goal.
Goal selection is not cast in concrete
Goals change with increasing insight
26. We Need:
-Knowledge of the system under
investigation
-System analyst skills (model formulation)
-Model building skills (model Programming)
-Data collection skills
-Statistical skills (input data representation)
27. We Need:
-More statistical skills (output data analysis)
-Even more statistical skills (design of
experiments)
-Management skills (to get everyone pulling
in the same direction)
28. -Modeling is a selling job!
-Does anyone believe the results?
-Will anyone put the results into action?
-The End-user (your customer) can (and must) do
all of the above BUT, first he must be convinced!
-He must believe it is HIS Model!
29. Assuming Simulation is the appropriate
means, three alternatives exist:
1. Build Model in a General Purpose
Language
2. Build Model in a General Simulation
Language
3. Use a Special Purpose Simulation
Package
30. Advantages:
◦ Little or no additional software cost
◦ Universally available (portable)
◦ No additional training (Everybody knows…(language X) ! )
Disadvantages:
◦ Every model starts from scratch
◦ Very little reusable code
◦ Long development cycle for each model
◦ Difficult verification phase
31. FORTRAN
◦ Probably more models than any other language.
PASCAL
◦ Not as universal as FORTRAN
MODULA
◦ Many improvements over PASCAL
ADA
◦ Department of Defense attempt at standardization
C, C++
◦ Object-oriented programming language
32. Advantages:
◦ Standardized features often needed in modeling
◦ Shorter development cycle for each model
◦ Much assistance in model verification
◦ Very readable code
Disadvantages:
◦ Higher software cost (up-front)
◦ Additional training required
◦ Limited portability
33. GPSS
◦ Block-structured Language
◦ Interpretive Execution
◦ FORTRAN-based (Help blocks)
◦ World-view: Transactions/Facilities
SIMSCRIPT II.5
◦ English-like Problem Description Language
◦ Compiled Programs
◦ Complete language (no other underlying language)
◦ World-view: Processes/ Resources/ Continuous
34. MODSIM III
◦ Modern Object-Oriented Language
◦ Modularity Compiled Programs
◦ Based on Modula2 (but compiles into C)
◦ World-view: Processes
SIMULA
◦ ALGOL-based Problem Description Language
◦ Compiled Programs
◦ World-view: Processes
36. Advantages
◦ Very quick development of complex models
◦ Short learning cycle
◦ No programming--minimal errors in usage
Disadvantages
◦ High cost of software
◦ Limited scope of applicability
◦ Limited flexibility (may not fit your specific
application)
37. NETWORK II.5
◦ Simulator for computer systems
OPNET
◦ Simulator for communication networks, including
wireless networks
COMNET III
◦ Simulator for communications networks
SIMFACTORY
◦ Simulator for manufacturing operations
38. Many people think of the cost of a
simulation only in terms of the software
package price.
There are actually at least three
components to the cost of simulation:
1.Purchase price of the software
2.Programmer / Analyst time
3.“Timeliness of Results”
39. System
◦ A group of objects that are joined together in
some regular interaction or interdependence
toward the accomplishment of some
purpose.
◦ Entity
◦ An object of interest in the system.
◦ E.g., customers at a bank
40. Attribute
◦ a property of an entity
◦ E.g., checking account balance
Activity
◦ Represents a time period of specified length.
◦ Collection of operations that transform the
state of an entity
◦ E.g., making bank deposits
41. Event:
◦ change in the system state.
◦ E.g., arrival; beginning of a new execution;
departure
State Variables
◦ Define the state of the system
◦ Can restart simulation from state variables
◦ E.g., length of the job queue.
42. Process
◦ Sequence of events ordered on time
Note:
◦ the three concepts(event, process,and activity) give
rise to three alternative ways of building discrete
simulation models
43. System Entities Attributes Activities Events State
Variables
Banking Customers Checking
account
balance
Making
deposits
Arrival;
Departure
# of busy
tellers; # of
customers
waiting
Note: State Variables may change continuously (continuous sys.)
over time or they may change only at a discrete set of points
(discrete sys.) in time.
45. Less time consuming
Sharpen the managerial Skills
Doesn’t Disrupt the Real situation
Gives better understanding to Managers
Easy to use for non-technical managers also
Less expensive
Nearest relation between the Real and
Simulated model
Scopes to study Environmental and Related
changes
46. Monte Carlo simulation is a computerized
mathematical technique that allows people to
account for risk in quantitative analysis and
decision making. The technique is used by
professionals in such widely disparate fields
as finance, project management, energy,
manufacturing, engineering, research and
development, insurance, oil & gas,
transportation, and the environment
47. In this competitive world,it is essential for an
executive to study or at least to guess the
activities of the competitor. Moreover he has
to plan his counter actions when his
competitor uses certain technique. Such war
or game is a regular feature in the market
which aims maximize the profits and
minimize the losses. So, the competitive
situation is called game.
48. There are finite number of competitors called “players”
Each players has a finite number of actions which are
called as “ strategies”
No player knows his opponent’s strategy until he decides
his own strategy
The game is a combination of strategies in certain units
(generally money) which determines the gain or loss
The figure shows the outcome of strategies n a matrix
form is called “pay off matrix”
49. The player playing the game always tries to
choose best course of action which results in
optimal pay off called as “optimal strategy”
The expected pay off when all the players of the
follow their optimal strategies is called “Value of
the game”. The main objective of a problem of
games is to find the value of the game.
The game is said to be “fair game” if the value of
the game is zero, otherwise it is known as
“unfair”
50. Terminology:
Strategy: It is defined as set of rules while playing
the game
(a) Pure strategy : If the player select the same
strategy each time then that is called as
Pure strategy
(b) Mixed strategy : When the players uses the
combination of strategies that is called as
Mixed strategy
Optimum strategy : A course of action or play
which puts the player in the most preferred
position is called Optimum strategy
51. Value of the game:
It is the expected pay-off of play (final result)
when all the players are following their optimum
strategies.
If value of game V=0, then fair game, and
If V≠ 0, then unfair game.
Two persons – Zero sum game:
When two players are playing the game and, if
loss of a player is gain of other and vice versa,
then it is called as two persons – zero sum game.
52. Payoff Matrix:
A two persons-zero sum game is represented by
a matrix as shown below
A’s pay-off matrix:
Column Player B
B1 B2 B3
A1 a11 a12 a13
Row
Player A : A2 a21 a22 a23
A3 a31 a32 a33
53. B’s pay-off matrix:
Column Player B
B1 B2 B3
A1 -a11 -a12 -a13
Row
Player A : A2 -a21 -a22 -a23
A3 -a31 -a32 -a33
54. 1. two persons games:
If only two players are playing the game
that type of game is called two persons game
ex: playing chess
2. Multi person game:
If more than two players are playing the
game that type of game is called multi
persons game
ex: playing football,cricket.
55. Zero sum game:
If loss of one player is gain of other player and
Vice versa then that game is zero sum game
Non-zero sum game:
If loss of one player is not gain of other player
and Vice versa then that game is non-zero
sum game
Deterministic game:
If the game yields a solution with single
strategy then that is called as deterministic
game
56. Probabilistic game:
If a player adopts more than one strategy
with some probabilities that is called as
Probabilistic game
Fair game: If value of game is zero i.e.,
neither player wins nor loss(drawn) that is fair
game
Unfair game: If one player wins and other
losses ( value of the game is non zero i.e.,
may be positive or negative) that type of
game is called unfair game
57. These type of games are two types
Deterministic with pure strategy
Probabilistic with mixed strategy
To solve these problems two types of methods
are there
1.Minimax-Maximin principle
2.Dominance principle
58. Step 1: Write pay-off matrix
Step 2: Select the minimum value of each row
of the pay –off matrix and encircle the
element
Step 3:Select the largest among the row
minima found and write beneath the row
minima
Step 4: Select the maximum value of each
column of the pay –off matrix and
enrectangle the element
59. Step 5:Select the minimum value among the
column maxima found and write this at the
right end
Step 6:If Max (Rmin)=Min (Cmax) then saddle
point exist. Thus saddle point is found at the
element on which both circle and box are
enclosed. This saddle point is also called as
value of the game
60. In certain cases no pure strategy solutions
exist for the game. In other words saddle
point/ value of the game does not exist.
Here we will adopt the Algebraic method we
will use to solve the problem
Player B
B1 B2
A1 a b
Player A
A2 c d
61. No machine is immortal and immune
completely to any failures. No matter how
safety you run . How closely you follow the
instruction of the manufacturer or supplier,
how best you maintain to its standards and
specification perhaps we can only try to
prevent or prolong the occurrence of failure
if we know the probable reason for its
occurrence.
62. The awareness of the equipment often makes
the engineer so confident that after the
rectification of the failure he will be able to
assure the production manager about its
running condition.
Types of failures:
1.Early failures (infant failures)
2.Random or rare event failures (youth failures)
3.Old age failures
63. Failure costs:
While analyzing the machine failure we are
concerned with the following costs.
1. Purchase cost
2. Salvage/ Scrap /Resale/Depreciation cost
3. Running cost/Maintenance, Repair and
Operating costs (MRO)
4. Failure/damage cost