IE 469 Manufacturing Systems 469 صنع نظم التصنيع II - Modeling Systems Tutorial.

Slides:



Advertisements
Similar presentations
Production and Operations Management Systems
Advertisements

IE 469 Manufacturing Systems 469 صنع نظم التصنيع III- Assembly Line Tutorial.
11–1. 11–2 Chapter Eleven Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
INDR 343 Problem Session
1 Session 16 Scheduling System Examples The PAC Data Base Twin Disc Shop-Floor Control Report Finite Loading Example Steelcase Vendor Scheduling Report.
NEW CAIRO EDUCATIONAL ZONE ELSHOROUK FUTURE INTEGRATED LANGUAGE SCHOOL MATHS DEPARTMENT.
An Operations Scoreboard. Measures of Performance Processing rate (productivity) Value added productivity Throughput time Utilization Waiting time % defective.
Queueing Theory Chapter 17.
Simulating Single server queuing models. Consider the following sequence of activities that each customer undergoes: 1.Customer arrives 2.Customer waits.
To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved. Chapter 16 Waiting Line Models and.
LESSON 5.1: RATIOS AND RATES Chapter Five: Ratios and Proportions.
Spreadsheet Modeling & Decision Analysis
Introduction to Management Science
CA200 Quantitative Analysis for Business Decisions.
ECON 103 Tutorial 15 Rob Pryce
Chapter 2 -Test for one and two means
11DSCI4743 Capacity Management Capacity management is planning & controlling resources needed to meet production objectives –Planning: determining resources.
1 Operations Research Prepared by: Abed Alhameed Mohammed Alfarra Supervised by: Dr. Sana’a Wafa Al-Sayegh 2 nd Semester ITGD4207 University.
McGraw-Hill/Irwin © 2006 The McGraw-Hill Companies, Inc., All Rights Reserved. 1.
Lecture 14 – Queuing Networks Topics Description of Jackson networks Equations for computing internal arrival rates Examples: computation center, job shop.
1 Queuing Analysis Overview What is queuing analysis? - to study how people behave in waiting in line so that we could provide a solution with minimizing.
1 1 Slide Short – Term Scheduling Professor Ahmadi.
1 Slides used in class may be different from slides in student pack Chapter 5 Process Analysis  Process Analysis  Process Flowcharting  Categories of.
Statistical Applications Binominal and Poisson’s Probability distributions E ( x ) =  =  xf ( x )
Section 12-1 Sequence and Series
Lesson Objective Understand how we can Simulate activities that have an element of chance using probabilities and random numbers Be able to use the random.
The Impact of Variability on Process Performance.
8/24/04 Paul A. Jensen Operations Research Models and Methods Copyright All rights reserved Material Movement The movement of material through the.
IE450 Models Relating Cycle-time, Throughput, WIP and Batch Sizes
IE 469 Manufacturing Systems 469 صنع نظم التصنيع I- Performance Measure Tutorial.
MGTSC 352 Lecture 25: Congestion Management MEC example Manufacturing example.
Introduction to Uncertainty Simulation of Operations.
Question 1 A.What are the types of flow line analyses? And why breakdown analysis is important? B.A two-week study is performed on a 10-stations transfer.
Managerial Decision Making Chapter 13 Queuing Models.
Scheduling Operations
Model Antrian Tunggal Pertemuan 20
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
Lecture 14 – Queuing Networks
Measures of Central Tendency
McGraw-Hill/Irwin ©2009 The McGraw-Hill Companies, All Rights Reserved
Results for p = 0.1 Graphs at different values of Call Arrival Rate for Call Blocking Probability (in %) System Utilization (Efficiency) (in %) Average.
Queuing Theory Jackson Networks
Jackson Ntw You have just taken over as plant manager for K-LOG manufacturing which makes playground equipment. There are four primary stations and parts.
Service Operations Management (SOM) Waiting Line Models
Queuing Systems Don Sutton.
Waiting Lines (Queuing Theory) Service Analysis Tutorial 1
Solutions Queueing Theory 1
The Impact of Variability on Process Performance
Chapter 5 Process Analysis.
Waiting Lines Queues.
Solutions Hwk Que3 1 The port of Miami has 3 docking berths for loading and unloading ships but is considering adding a 4th berth.
Process and Capacity Analysis Capacity Analysis Tutorial
IV-2 Manufacturing Systems modeling
System Performance: Queuing
Grade one First term Second term Learning unit Learning unit
Multinomial Experiments
Variability 8/24/04 Paul A. Jensen
Solutions Queueing Theory 1
Problem Markov Chains 1 A manufacturer has one key machine at the core of its production process. Because of heavy use, the machine.
Day 92 – Geometric sequences (day2)
A graphing calculator is required for some problems or parts of problems 2000.
4n + 2 1st term = 4 × = 6 2nd term = 4 × = 10 3rd term
Solutions Queueing Theory 1
Lecture 14 – Queuing Networks
Solutions Hwk Que3 1 The port of Miami has 3 docking berths for loading and unloading ships but is considering adding a 4th berth.
Multinomial Experiments
Multinomial Experiments
Sequences Example This is a sequence of tile patterns.
Chapter 5 Process Analysis.
Capacity Management Capacity management is planning & controlling resources needed to meet production objectives Planning: determining resources needed.
Presentation transcript:

IE 469 Manufacturing Systems 469 صنع نظم التصنيع II - Modeling Systems Tutorial

Question 1 A production line is used to produce a product. Number of products at the start of the line is 400 units per week. The line consists of four stations. It is arranged such that an inspection station (I) is placed after three process stations (S1, S2, S3). The processing time of stations (S1, S2, S4, I) are (8, 10, 13, 8) minute respectively. Inspection station (I) has defect rate of 10%. 4% of the defects are scraped and the reminders are returned to station (S2). Find : (a) Determine the effective arrival rate at each station. (b) Determine the number of machines at each station. (c) Find the average work in process. (d) Find the throughput time. Problem # % S1S2IS3 6% Question Graph (NOT GIVEN!):

S1S2S3I Problem #1 Solution Arrival Rate, λ unit/hr10 Service Rate, m unit/hr Arrival Rate, λ’ unit/hr Utilization Ratio, r=λ’/ m Number of machines, n2232 At this stage a decision to be taken; 1st alternative to have single production line with multi station at all staion, OR 2nd two have two production lines [each produce half the weekly production at 200. in this case the solution will be: Arrival Rate, λ = R p, unit/hr5555 Arrival Rate, λ’ = λ/(1-p), unit/hr Service Rate, m =1/T p, unit/hr Utilization Ratio, r=λ’/ m Number of machines, n =int(ρ) Utilization Ratio, r=λ’/n m Po =probability

Problem #1 Solution v= λ’/ l Lq = length of queue L =System Queue, unit Total Queue in system =ΣL, units13.98 Wq =queue throughput, W = System Throughput, hr v*w W=Snw, hr2.7954

ProductDemand / Week Routing Data [Centers, Process Time in hr] C3, 1C2, 2C1, 1C4, 2 210C4, 3C1, 3 C3, 1 36C4, 4C3, 2C1, 2 410C3, 2 C2, 2C1, 1 58C3, 3C2, 1C1, 2 Problem #2 Question 2 Five parts are processed using four machining centers (C1, C2, C3, and C4) as indicated in Table (1). (a) Determine the effective arrival rate at each center. (b) Determine the average processing time at each center. (c) Determine number of machines in each center (d) Find the average number of jobs in process. (e) Find the throughput time for each parts.

a- Calculate Arrival Rate Product, (i) Effective arrival Rate λ’ C1 λ’ C2 λ’ C3 λ’ C Sum Problem #2 Solution Process time data, Tp, Hr Product,(i) Machine centers C1C2C3C

c) Number of machines StationλmM(c)=l/μrPoLLqWWq C C C C Problem #2 Solution b- Calculate Process Times (λ’ i x T p )/(Sum (λ’i) x 40) Product,(i) Average Process Time m -1 C1 m -1 C2 m -1 C3 m -1 C Sum m m

e) Throughput time for each product (Troughput time, Wp = Σprocess time+ΣWq) d) Number of products in the system Problem #2 Solution

Problem #3 Question 3 A five departments manufacturing system is used to produce five parts according the data given in table (2). Find (a) Determine the effective arrival rate at each department. (b) Determine the number of machines at each station. (c) Find the average work in process. (d) Find the throughput time. TABLE (2) Part Weekly Demand Process Sequence Operation Time,hr LoadProcess StationUnload ABCDEA 16A > C > B > D > A A > B > D > E > A A > C > D > E > A A > C >E > B > A A > B > E > C > A Ʃ 25

a) Arrival rate calculation demand of part (i) at department (k), λ’ P1P2P3P4P5l d =Σ(l ki ) A B C D E Problem #3 Solution operation time of part (i) at department (k), T ki P1P2P3P4P5 A0.4 B C D E ΣToΣTo

b) Service rate calculation Calculate the operation time (service time) for a department, m d -1 =S(λ ki /λ d )*(T ki /40), P1P2P3P4P5 Ʃ (OT&AR) μ d -1 μdμd A B C D E Problem #3 Solution λμCρPoPo LLqLq WWqWq A B C D E

Throughput for each product T P = ΣT o /40Tp+ΣWqTp+ΣWq W W W W W Problem #3 Solution Average work in process L ƩLƩL