Lecture 01 Introduction to Modelling and Simulation Peer-Olaf Siebers pos@cs.nott.ac.uk Container Terminal of Novorossiysk G54SIM 2 Module Mission Statement • This module will explain the main systems simulation methods in detail so that students will be competent in choosing and implementing the right method for their particular problem. • Students will learn the general principles and techniques used in modelling and simulation and will gain some practical experience
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construction and the company’s internal documents and policies. By using the Monte Carlo simulation technique‚ simulation of risks in completed infrastructural projects will be performed. This project will involve the performance of Monte Carlo simulations on two accomplished infrastructural projects. A Monte Carlo simulation is a problem solving technique used to approximate the probability of certain outcomes by running multiple trial runs‚ called simulations‚ using random variables. The simulation will generate both a probability and a range of the outcome
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into four broad approaches: Empirical models‚ simulation models‚ deterministic models‚ and stochastic models. The first three models can very much be integrated in teaching high school mathematics. The last will need a little stretching. Empirical modelling involves examining data related to the problem with a view of formulating or constructing a mathematical relationship between the variables in the problem using the available data. However‚ simulation modelling involve the use of a computer program
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A. The simulation model B. The number of patients treated every week and the waiting time for each patient | Number of patients treated | Waiting time for each patient (Min) | Outpatient department | 39 | 2465 | Inpatient department | 542 | 3616 | C. Some changes in the resource levels I. Each shift includes 2 admission secretaries | Number of patients treated | Waiting time for each patient (Min) | Outpatient department | 39 | 2515 | Inpatient department | 544
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Number: (443)956-6067 E-mail Address: surban1@jhu.edu Teaching Assistant Ben Brock: bbrock1@jhu.edu Office Hours Saturdays‚ 10:00am – 12:30pm at DC Center (room 201) or by appointment Required Text and Learning Materials: Stochastic Simulation and Applications in Finance with MATLAB Programs‚ (2008)‚ Huu Tue Huynh‚ Van Son Lai‚ Issouf Soumare (HLS) [this book may be available as an e-book for students] MATLAB: An Introduction with Applications 4th Edition (2010) or earlier Editions
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This simulation game was developed by Sisk (1976). Basically‚ there are ten imaginary countries (Shima‚ Myna‚ Ila‚ Usa‚ Pam‚ Bonay‚ Shivey‚ Lani‚ Ranu and Bili) and each of these countries has three key elements to be considered: defense‚ resources and demography. The main purpose of this game‚ understanding the importance of negotiation and interdependence between countries. I would like to make some changes in the game for my class. Every student or group will choose their imaginary country.
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Operations Management: Process Analysis and Applications Module Changing Sources of Competitive Advantage Targeting Improvement: Operational Measures - Time T‚ Inventory I‚ Throughput rate R » Link through Little’s Law » Link to Financial Measures » Targeting improvement: CRU Computer Rentals Capacity and Flow Time Analysis » Pizza Pazza » Levers for Improvement Multi-product Capacity Management and Investment » Joint Marketing & Production Decisions » Optimal Capacity
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Biography Catherine Littlefield - known to family and friends as Kitty or Caty - was born on Block Island‚ Rhode Island‚ on February 17‚ 1755. She was the daughter of Phebe Ray and John Littlefield. After her mother’s death‚ Catherine went to live with her aunt‚ Catherine Ray Greene. 19-year-old Catherine married Nathanael Greene on July 20‚ 1774‚ in Greenwich‚ Rhode Island after they had met at one of her aunt parties. Nathaniel was 14 years older than her at the time. Following their wedding‚
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Global Supply Chain Management Simulation Debrief Slides ©© Enspire Enspire Learning Learning and and Harvard Harvard Business Business School School (revised Dec 2010) 1 Board Members’ Objectives Member Objective Betty Forecasting: choice of options (consensus vs. mean) Doug Forecasting: choice of options (role of risk) Yvonne Stocking Levels: Weighing the costs of over/understocking Meryl Production flexibility: accurate response/ sourcing strategy (focus on flexibility) Paul Production
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Stanford University Graduate School of Business April 29‚ 2009 * * Managing a Short Product Life Cycle at Littlefield Labs Background In early January‚ Littlefield Labs (LL) opened its first and only highly automated lab to test blood samples. LL receives the samples from local hospitals and clinics and processes the samples using disposable kits. After 360 days of operation the lab will cease operations‚ shut down‚ and dispose of any remaining kit inventories. Neither capacity nor inventory has
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