...Monte Carlo Statistical Analysis Name Course Instructor Date The Monte Carlo method is a mathematical method used for problem solving through the generation of random numbers and then observing a fraction of these numbers and the properties they obey. It is useful in obtaining numerical solutions to problems that are too complicated for analytical solutions. It is a form of probability used to understand the impact of risk and uncertainty in various areas such as financial and cost forecasting. It involves computation of the likelihood of given events occurring or not occurring, without taking into account the interaction of the elements involved in influencing the occurrence. The mathematical method was invented by Stanislaw Ulam in 1946 and named by Nicholas Metropolis after a classy gambling resort in Monaco, where a relative of Ulam frequently gambled [ (Fishman, 1996) ]. Concepts of the Monte Carlo method Uncertainty Being a forecasting model, there are assumptions that need to be made due to the uncertainty of various factors. One therefore needs to be able to make estimations of the expected results as they cannot predict with certainty what the end value will be. Important factors such as historical data and past experiences in the field can be helpful in making an accurate estimate. Estimation In some cases, estimation may be possible but in others it is not. In situations where estimation is possible, it is wise to use a wide range of possible values instead...
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... aluminum alloys, magnesium alloys and boron steels in automotive industry is increasing to reduce vehicle weight and increase crash performance. The use of these relatively new materials requires advanced and reliable techniques to a) obtain data on material properties and flow stress, b) predicting springback and fracture in bending and flanging, c) selecting lubricants and die materials/coatings for stamping and forging and d) designing tools for blanking and shearing. In addition, designing the process and tooling for a) hot stamping of boron steels, b) warm forming of Al and Mg alloys, and c) optimizing the use of servo-drive presses require advanced Finite Element based simulation methods. CPF is conducting R&D in most of these topics and also in many hot and cold forging related topics. This paper gives an overview of this research and discusses how the research results are applied in cooperation with industry. Keywords: Metal Forming, Sheet metal, Forging, FEM 1 INTRODUCTION The Center for Precision Forming (CPF) has been established with funding from the National Science Foundation (NSF) and a number of companies (www.cpforming.org). CPF is an outgrowth of the Engineering Research Center for Net Shape Manufacturing (ERC/NSM – www.ercnsm.org) and conducts research in sheet metal forming while ERC/NSM focuses on cold and hot forging related R&D projects. Both Centers work closely with industry under contract. Thus, the graduate students (MS and PhD level) as well as post...
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...Complete the simulation "Using Perceptual Maps in Marketing" and prepare a summary in which you address the following: Formulate a differentiation strategy for Cruiser Thorr by changing the market mix at the Price, Place, Promotion and Services levels. My Performances: Marketing Mix, Lifestyle, price, service offering and quality engineering. - Repositioning, financial options- price parameter, increasing service My Decisions: Maintaining price- Publicize through Hollywood Films- Internet (manufacturer's Web Site), Financial Service and Customization Options, A. For each of the three major phases in the simulation, describe: 1) The situation 2) Your recommended solution(s), including why 3) Your results B. Summarize the different marketing components addressed in this simulation by answering the following questions: 1) What is the relationship between differentiation and positioning of products or services? Is the repositioning of the product in the simulation as you had expected it to be? Why or why not? Simulation Paper 3 Running head: Simulation Paper 2) What is the impact of the product life cycle on marketing? What impact did the product life-cycle have on the product in the simulation? A. For the first major phase is to identify the parameters that are relevant to Thorr Cruiser. The first parameter is that of the extent of contemporariness of Thorr Cruiser. Thorr Cruiser is not perceived to be modern, apart from...
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...INTRODUCTION: The purpose of this paper is to bring out the use of Simulation by Managers of various organizations. Under discussion will be: Definition of simulation. Model construction. When and why it becomes handy to use simulation. Steps used in simulation technique. Random selection in simulation. Advantages / benefits derived from use of simulation technique. Disadvantages /Challenges associated with simulation technique. Role of computer in simulation. Conclusion / comments. Definition of simulation: Simulation can be defined as a quantitative technique which describes a process by developing a model of that process and then conduct a series of organized trial and error experiments to predict the behavior of the process through time often with the aid of a computer. It is a method that is used to solve a problem in many areas of management, for example inventory management, queuing problems, profit analysis and project management among others. Model construction: A successful simulation model has to be carefully designed in order to achieve the intended predictive purpose. Therefore important factors have to be considered such as; i. It must be objective oriented. It must be constructed for a purpose and be able to achieve that purpose. ii. Critical variables and relationships. These must be identified and incorporated in model. However it is not essential or indeed desirable to include all variables in the model. iii. Simplicity...
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...County Hospital Simulation Project Abstract The paper offers a simulation model describing the X-ray departments operations in the county hospital. It can be used for testing various process scenarios, for allocation of resources and also conduct activity based cost analysis. The simulation model is used for demonstrating a new operational method that makes the operations at the X-ray department more effective. The operational method is referred to as Triage team method. This method has been it is been studied from two view points. The results showed that the method enhances the X-ray department if properly implemented and it incorporates all the required tasks. County hospital simulation project Introduction The department of x-ray in the county hospital is facing a challenge in the x-ray department. With the increased amount of patients visiting the x-ray department, the challenge to the health care managers is becoming serious. The process undergone and the resources need to be reconsidered to make the operations more efficient. This will also maintain the quality of care. The objective of the department is to ensure that the patients take minimal time in the process. The resources also need to be allocated in the most effective way and by use of more advanced technology. Since X-ray department and the processes therewith are somewhat complicated, and have dynamic variables and has random features, definitely there is a need for a method that is effective. The...
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...JET Copies Case Problem In analyzing the case, JET Copies Case Problem, the following categories will be addressed: Model number of days to repair 1. In Excel, use a suitable method for generating the number of days needed to repair the copier, when it is out of service, according to the discrete distribution shown. JET copies needs to know the number of days they need to repair, when the copy machine is out of service. Using the given table, there are three columns of probability, repair time and cumulative. I used the figures given in case problem for probability and repair time days but for the cumulative column we started at 0.00 cumulative, then added the 0.20 to that to get the cumulative for day 2. For each probability subsequent to the previous we added, so for the 0.45 probability we added the 0.20 to get 0.65. Then repeating the same for the rest of probabilities, after 0.65 the cumulative resulted in 0.90. Then we used the table with ten columns. In the first column starting at A5, the formula entered was the =RAND() function to create random number for the first cell. That function formula was then dragged down to the below cells and locked to have the values be fixed. In the next column, I use the numbers copied from my first column. Then in the G5 column, we entered the function VLOOKUP to find the number of days needed to repair =VLOOKUP(F5,Lookup,2) and dragged the cell to have the same formula for the below cells in the column. Look up, in the...
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...Littlefield Technologies Assignment 5 PM on February 22 . Starting at 5 PM on Wednesday, February 27, the simulation will begin The game will end at 9 PM on Sunday, March 3. Accessing your factory http://quick.responsive.net/lt/toronto3/entry.html Littlefield Technologies’ Operations board stuffing testing tuning Operations Policies at Littlefield Purchasing Supplies Processing in Batches Contract Pricing Borrowing from the Bank Cash Balance The winning team is the team with the most cash at the end of the game (cash on hand less debt). Current State of the System and Your Assignment At the end of day 350, the factory will shut down and your final cash position will be determined. Starting at 5 PM on Wednesday, February 27, the simulation will begin The game will end at 9 PM on Sunday, March 3. PLEASE DO NOT WAIT UNTIL THE FINAL SECONDS TO MAKE YOUR CHANGES. SOMETIMES THEY TAKE A FEW MINUTES TO BE PROCESSED. Written Assignment: Analysis of Game 2 of Littlefield Technologies Simulation Due March 14, 8:30 am in eDropbox Your group is going to be evaluated in part on your success in the game and in part on how clear, well structured and thorough your write-up is. The write-up only covers the second round, played from February 27 through March 3. It should not discuss the first round. Your write-up should address the following points: • A brief description of what actions you chose and when. Not a full list of every action, but the major...
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...Chapter 51. Enhancing Patient Safety in Nursing Education Through Patient Simulation Carol Fowler Durham, Kathryn R. Alden Background The alarming rise in morbidity and mortality among hospitalized patients throughout the United States heightens concerns about professional competency.1 Nurses and other health care professionals are under increased scrutiny to provide safe, effective care. Likewise, nursing education programs are faced with increased pressure to produce graduates who are capable of providing safe patient care. Toward that end, nursing education programs develop curricula, hire qualified faculty, and select learning experiences for students in an effort to train and graduate competent, effective nurses. The instructional strategies utilized in both didactic and clinical components of nursing education courses are highly influential in determining critical thinking and clinical decisionmaking ability as well as in developing the psychomotor skill performance of new graduates. Of course, it is unrealistic to think that graduates of nursing education programs have received all the training they need when they depart the doors of academia. Orientation programs for new graduates and continuing education for nurses are essential tools to help practitioners improve their knowledge, skills, and expertise so that quality patient care is provided and outcomes are optimized while errors are minimized. Ongoing evaluation of nursing competence is necessary...
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...TOPICS IN AD HOC AND SENSOR NETWORKS Simulations in Wireless Sensor and Ad Hoc Networks: Matching and Advancing Models, Metrics, and Solutions Ivan Stojmenovic, University of Birmingham and University of Ottawa ABSTRACT The objective of this article is to give advice for carrying out a proper and effective simulation activity for protocol design. It challenges some of the existing criticisms of simulation practices that emphasized validation aspects. This article advocates the use of simple models, matching assumptions and metrics in the problem statement and simulation to provide a basic “proof of concept,” and comparison with truly competing solutions, which is possible only after a thorough and critical literature review. Then the complexity of the models can be increased (one parameter at a time), revising the algorithms themselves by adapting them to new assumptions, metrics, and the corresponding simulation environment. Selected independent variables should explain performance under a wide range of scenarios. unclear which protocol will perform well under a wide range of scenarios. It is our view that each article should be judged on its overall contribution, including the assumptions used, theory developed, new algorithms introduced, protocol details, simulation results, and relevance to an ultimate goal of staying on a path toward creating applications. We begin with a literature review of existing criticism for simulation practices, and then discuss what we believe...
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...This report provides an in-depth analysis of the two Everest Simulations conducted by Group 10 of MGMT1001 Thursday Tutorial. This task required students to form teams consisting of five to six members whose goals were to summit Mount Everest. While it provided us with a rich experience in team dynamics and collaboration, it also enabled us to explore key managerial concepts taught in the course, consisting of: • Communication • Groups and Teams • Leadership In this report, we examine the effectiveness of Face to Face Communication (FTFC) versus Computer Mediated Communication (CMC), and the problems encountered through the utilisation of the virtual medium including efficiency of the feedback system, loss of personal focus and other emergent issues. It includes personal reflections on attitudes and perceptions, as well as group performance and strategies adopted in the second Simulation in order to create a more positive team experience. Theories which relate to interpersonal communication have also been integrated in the report to illustrate its relation to certain situations encountered during the Simulation. Additionally, we provide a multifaceted analysis on the notion of team cohesiveness and how it attributes to better performance outcomes. An overview on the different intragroup conflicts encountered in the Simulation has been included, examining the positive and negative impact that conflict had on team experience and performance, and how mutual agreements were reached...
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...is a computer necessary in conducting a realworld simulation? Answer It is important because there are many different types of outcomes that comes in with simulation. Computers are used in daily life activities and it is necessary. 14-11 What is operational gaming? What is systems simulation? Give examples of how each may be applied. Answer Operational gaming is the use of simulation in competitive situations such as military games and business or management. System simulation ls that deal with the dynamics of large organizational or governmental systems. Validation The process of comparing a model to the real system that it represents to make sure that it is accurate. 14-17 (a) Resimulate the number of stockouts incurred over a 20-week period (assuming Higgins maintains a constant supply of 8 heaters). (b) Conduct this 20-week simulation two more times and compare your answers with those in part (a). Did they change significantly? Why or why not? (c) What is the new expected number of sales per week? Answer A. The number of stockouts incurred over a 20 week period is HOT WATER NUMBER OF HEATER SALES WEEKS THIS PER WEEK NUMBER WAS SOLD 3 2 4 9 5 10 6 15 7 25 8 12 9 12 10 10 B, Two more times would give us the value of a multiplied by 2. c. 25 14-18 A. 15 days of barge uploadings and average number of barges delayed B, They both are probabilistic simulations. Chapter 5 HW 5-14 Using MAD, determine whether...
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...A SYSTEM SIMULATION STUDY ON THE THREE FAST MOVING PRODUCTS (MARLBORO, C2, VIVA) OF THE COLLEGE VARIETY SHOPPE USING THE MONTE CARLO SIMULATION IN INVENTORY MANAGEMENT EXECUTIVE SUMMARY This study shows how the selected three fast moving products (Marlboro cigarettes, C2, Viva mineral water) move from the current Inventory Management technique of the College Variety Shoppe from its distributors to its warehouse storage to the end user or customer. An excel program and a simulation model was made to observed its current performance. After the observation, the group performs an experimentation that will improve the current technique of the College Variety Shoppe. After simulating the experimentation, the group then give conclusions and recommendations on how to improve the College Variety Shoppe’s current Inventory Management. TABLE OF CONTENTS Title Page ........................................................ 1 Executive Summary ........................................................ 2 Table of Contents ........................................................ 3 Introduction ........................................................ 4 Methodology ........................................................ 6 Model Development ........................................................ 7 Model Validation ........................................................ 11 Experimentation, Results .......
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...Initial operations strategy Prior to the commencement of the simulation, we examined the 50 days of historical data to glean as much information as we could about the operations. We performed some analysis in Excel and created a dashboard to illustrate various data. Specifically, we regressed the prior 50 days of jobs accepted to forecast demand over the next 2 - 3 months within a 95% confidence interval. The yellow and grey lines represent the maximum and minimum variability, respectively, based on two standard deviations (95%). Exhibit 1: Forecasted and actual demand by Day 50 and Day 270 Our two primary goals at the beginning of the simulation were as follows: 1) Eliminate bottlenecks and increase capacity in order to meet forecasted demand 2) Decrease lead time to 0.25 days in order to satisfy Contract 2 and maximize revenue In order to achieve these goals, we would need to know the capacity and throughput time of the entire system. We used the time required by each machine to process a lot to calculate capacity per station and then capacity of the entire production line (380 kits/day or 6 orders/day). In Exhibit 2 we can observe that the capacity of the production line is given by the station that produces the least number of units per day. Exhibit 2: Capacity of the production line The Decisions We decided to work with the maximum variability of demand because there was a penalty for late jobs and because there was no revenue for orders that took more than “x”...
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...Introduction This chapter describes our work in evolution of buildable designs using miniature plastic bricks as modular components. Lego 1 bricks are well known for their flexibility when it comes to creating low cost, handy designs of vehicles and structures. Their simple modular concept make toy bricks a good ground for doing evolution of computer simulated structures which can be built and deployed. Instead of incorporating an expert system of engineering knowledge into the program, which would result in more familiar structures, we combined an evolutionary algorithm with a model of the physical reality and a purely utilitarian fitness function, providing measures of feasibility and functionality. Our algorithms integrate a model of the physical properties of Lego structures with an evolutionary process that freely combines bricks of different shape and size into structures that are evaluated by how well they perform a desired function. The evolutionary process runs in an environment that has not been unnecessarily constrained by our own preconceptions on how to solve the problem. The results are encouraging. The evolved structures have a surprisingly alien look: they are not based in common knowledge on how to build with brick toys; instead, the computer found ways of its own through the evolutionary search process. We were able to assemble the final designs manually and confirm that they accomplish the objectives introduced with our fitness functions. This chapter...
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...developed, operated and maintained the Concept Simulator since 1998. The Concept Simulator models the functional operation and inter-operation of key components of train control system architecture and the external systems to which the system interfaces. The simulator is valuable in the development and analysis of operational principles, and in assessing design trade-offs. The SEA-designed facility simulates operation at ERTMS Levels 1, 2, and 3. Components include a Communications Network, a Network Management Centre, a generic Interlocking, a Radio Block Centre, a Track Simulator including both conventional and TCS equipment, a Driver Desk, a European Vital Computer and a Driver MMI. The components are modelled using software-based simulations hosted on networked PCs. The simulator has been valuable in the engineering evaluation and validation of emergent system architectures, and enables system constraints to be explored and defined. ERTMS operational modes and the transitions between them are simulated and ERTMS principles and procedures are followed. Innovative Customer Information System (ICIS) SEA's Innovative Customer Information System (ICIS) is capable of managing and displaying customer information, including real time information, in a visually dynamic manner. The system utilises intelligent screens and wireless technology to distribute data, thus minimising installation and commissioning efforts. The system will be suitable for any transport environment, mobile...
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