Скачать презентацию Systems for Planning Control in Manufacturing X

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Systems for Planning & Control in Manufacturing X Supporting Slides Systems for Planning & Control in Manufacturing: Systems and Management for Competitive Manufacture Professor David K Harrison Glasgow Caledonian University ISBN 0 7506 49771 Dr David J Petty The University of Manchester Institute of Science and Technology Produced by D K Harrison and D J Petty 21/04/02 Sld - 0000

Systems for Planning & Control in Manufacturing 13 Evaluating Alternatives • Decision Trees and Risk • Expected Monetary Value • Value of Perfect Information • Uncertainty • Time Value of Money Define the Problem List the Possible Alternatives Produced by D K Harrison and D J Petty 21/04/02 Identify the Possible Outcomes List the Benefits in Numerical Terms Select a Mathematical Decision Making Model Apply the Mathematical Model Sld - 1301

Systems for Planning & Control in Manufacturing 13 Example 1. Define the Problem. A student is considering moving out of rented accommodation and taking on a mortgage. 2. List the Possible Alternatives. The alternatives are: Buy a house. Buy a flat. Remain in rented accommodation. • Do not ignore any alternatives, including doing nothing. • Do not ignore possible outcomes (positive or negative). Outcomes over which you have no control are called “states of nature”. 3. Identify the Possible Outcomes. Conditions could be favourable or otherwise. 4. List the Benefits in Numerical Terms Favourable House Flat Rent Unfavourable £ 10, 000 £ 6, 000 £ 0 -£ 2, 000 -£ 1, 000 £ 0 5/6. Select a Mathematical Decision Making Model and Apply. Produced by D K Harrison and D J Petty 21/04/02 Sld - 1302

Systems for Planning & Control in Manufacturing Decision Making Situations 13 • Decision Making Under Certainty • Decision Making Under Uncertainty • Decision Making Under Risk Produced by D K Harrison and D J Petty 21/04/02 Sld - 1303

Systems for Planning & Control in Manufacturing 13 Decision Trees and Risk Decision Node State of Nature Node . 75) ble (0 oura Fav Unfavourable (0. 25) uy a B e ous H Fav Buy a Flat Do . 75) ble (0 oura Unfavourable (0. 25) £ 10, 000 £ 7, 500 -£ 2, 000 -£ 500 £ 6, 000 £ 4, 500 -£ 1, 000 -£ 250 Not hing £ 0 -£ 0 *EMV = Expected Monetary Value • Multi-Stage Decisions Can be Analysed Produced by D K Harrison and D J Petty 21/04/02 Sld - 1304

Systems for Planning & Control in Manufacturing Value of Perfect Information 13 Should the Student Take Professional Advice? This is the Value Where Bad Decisions Have Been Avoided. This is the Value that Would be Obtained Under Conditions of Risk. This is the Most that the Advice would be Worth - An Offer of Advice at £ 1000 should be declined. EVOPI = Expected Value of Perfect Information, EVWPI = Expected Value with Perfect Information. How Would You Make the Decision? Produced by D K Harrison and D J Petty 21/04/02 Sld - 1305

Systems for Planning & Control in Manufacturing Uncertainty 13 House Flat Do Nothing • Maximax (Optimistic) - House • Maximin (Pessimistic) - Nothing • Equally Likely (Balanced) - House Produced by D K Harrison and D J Petty 21/04/02 Sld - 1306

Systems for Planning & Control in Manufacturing Decision Making Example – Question 13 Two sisters plan to go to a party. A lift is available to get to the party, but there is no guarantee this will be available for the return journey. It may be necessary to use a taxi therefore, and this will cost £ 7. 50. Taking the car will cost £ 2. 00. • Produce a decision table for this case • What is the best decision based on the Maximax, Maximin and Equally Likely criteria? • The probability of obtaining a lift is 0. 4. Draw a decision tree for this case • Should the sisters take the car based on the probability information given? Produced by D K Harrison and D J Petty 21/04/02 Sld - 1307

Systems for Planning & Control in Manufacturing 13 Decision Making Example – Answer States of Nature Alternatives Lift Take Car Row Average £-2. 00 £ 0. 00 £-7. 50 £-3. 75 Maximin = Take Car . 4) (0 Lift Tak Row Min £-2. 00 Maximax = Leave Car ar Row Max £-2. 00 Leave Car e. C eav L No Lift (0. 6) -£ 0. 00 -£ 7. 50 e. C ar Produced by D K Harrison and D J Petty 21/04/02 Equally Likely = Take Car -£ 2. 00 But are There Any Other Factors? Sld - 1308

Systems for Planning & Control in Manufacturing Summary 13 • Evaluating Alternatives is a Common Management Task • There are Three Conditions for Evaluating Alternatives Decision Making Under Certainty Decision Making Under Uncertainty Decision Making Under Risk • Analytical Approaches Can Support Decision Making • Ultimately, Decisions Must be Taken by Managers Produced by D K Harrison and D J Petty 21/04/02 Sld - 1309

Systems for Planning & Control in Manufacturing Overview • Definition The Imitation of a Real World System • Examples 14 Flow of People Through an Airport Nuclear War Traffic Flow in a Large City Behaviour of a Suspension System World Economy Temperature/Stress in a Piston Forces in a Metal Cutting Process Queues within a Manufacturing System Produced by D K Harrison and D J Petty 21/04/02 Sld - 1401

Systems for Planning & Control in Manufacturing Reasons for Simulation 14 • Practicality Large Systems • Safety Extreme of Systems • What-if Analysis Cost Time Repeatability • Understanding Visualisation (VR) Verification of Analytical Solutions Simulation is Becoming an Increasingly Common Engineering Technique Produced by D K Harrison and D J Petty 21/04/02 Sld - 1402

Systems for Planning & Control in Manufacturing Terminology Produced by D K Harrison and D J Petty 21/04/02 14 Sld - 1403

Systems for Planning & Control in Manufacturing 14 Simulation Methodology Step 1. Build Conceptual Model Step 2. Covert the Conceptual Model Step 3. Verify the Model Step 4. Model Experimentation Inputs (Procedures) Simulation Model Outputs (Responses) Experimentation Step 5. Draw Conclusions Produced by D K Harrison and D J Petty 21/04/02 Sld - 1404

Systems for Planning & Control in Manufacturing Hierarchy of Simulation Techniques Produced by D K Harrison and D J Petty 21/04/02 14 Sld - 1405

Systems for Planning & Control in Manufacturing Continuous Simulation x k (N/m) c (Ns/m) m (Kg) 14 Linear Second Order Differential Equation Analytical Solution Produced by D K Harrison and D J Petty 21/04/02 Sld - 1406

Systems for Planning & Control in Manufacturing Kinematic Simulation Produced by D K Harrison and D J Petty 21/04/02 14 Sld - 1407

Systems for Planning & Control in Manufacturing Static Simulation Produced by D K Harrison and D J Petty 21/04/02 14 Sld - 1408

Systems for Planning & Control in Manufacturing Simple Example Produced by D K Harrison and D J Petty 21/04/02 14 Sld - 1409

Systems for Planning & Control in Manufacturing 14 Simple Example - 10 Coins 9 8 Frequency 7 6 5 4 3 2 1 0 1 Produced by D K Harrison and D J Petty 21/04/02 2 3 4 5 Score 6 7 8 9 10 Sld - 1410

Systems for Planning & Control in Manufacturing Probability Distribution Function PDF 14 CDF Discrete Distribution Continuous Distribution Mapping Random Number to PDF: Produced by D K Harrison and D J Petty 21/04/02 Sld - 1411

Systems for Planning & Control in Manufacturing PDF/CDF Example (1) Produced by D K Harrison and D J Petty 21/04/02 13 Sld - 1412

Systems for Planning & Control in Manufacturing Random Numbers Produced by D K Harrison and D J Petty 21/04/02 13 Sld - 1413

Systems for Planning & Control in Manufacturing 13 PDF/CDF Example (2) 0. 785 0. 744 0. 119 2. 65 Produced by D K Harrison and D J Petty 21/04/02 5. 55 5. 78 Sld - 1414

Systems for Planning & Control in Manufacturing 13 PDF/CDF Example (3) 9 An Accurate Picture Takes Many Iterations – This is Called “Warm-Up” 8 Frequency 7 6 5 4 3 2 1 0 1 2 3 4 2. 65 Produced by D K Harrison and D J Petty 21/04/02 5 Score 6 7 5. 55 8 9 10 5. 78 Sld - 1415

Systems for Planning & Control in Manufacturing Dynamic Discrete Event Simulation 13 • Behaviour of a System Over Time • Concerned with Discrete Variables • Applied in a Variety of Fields • Useful for Examining Material Flow • Two forms: Deterministic and Stochastic • Originally Used Standard Languages (Fortran, Pascal) • Specialised Systems Now Exist (HOCUS, Pro. Model) Produced by D K Harrison and D J Petty 21/04/02 Sld - 1416

Systems for Planning & Control in Manufacturing 13 Dynamic Simulation - Example – 1 Time Period Initial Queue = 15 10 Every 5 Hours Starting 5 th Hr Queue (Q) Process Output Produced by D K Harrison and D J Petty 21/04/02 Start-up End of 1 st Hour End of 2 nd Hour End of 3 rd Hour End of 4 th Hour End of 5 th Hour End of 6 th Hour End of 7 th Hour End of 8 th Hour End of 9 th Hour End of 10 th Hour End of 11 th Hour End of 12 th Hour End of 13 th Hour End of 14 th Hour End of 15 th Hour End of 16 th Hour End of 17 th Hour End of 18 th Hour End of 19 th Hour End of 20 th Hour End of 21 st Hour End of 22 nd Hour Q 15 15 8 8 8 11 11 11 4 4 14 7 7 7 0 10 10 3 3 3 10 10 10 Event(s) 7 Units Used, 10 Units Arrive 7 Units Used 10 Units Arrive 7 Units Used 3 Units Used, 10 Units Arrive Sld - 1417

Systems for Planning & Control in Manufacturing Dynamic Simulation - Example - 2 14 Initial Queue = 15 10 Every 5 Hours Starting 5 th Hr Queue (Q) 7 Every 3 Hours (Max) Starting 2 nd Hr Process Output Produced by D K Harrison and D J Petty 21/04/02 Sld - 1418

Systems for Planning & Control in Manufacturing 14 Simulation Method - Time Slicing t 0 Initial Model Definition ti Re-define Entity States Display Model Status ti = ti + t End Time is incremented by “t” Statistics Produced by D K Harrison and D J Petty 21/04/02 Sld - 1419

Systems for Planning & Control in Manufacturing 14 Simulation Method – Advanced t 0 Initial Model Definition Calculate Next Event Re-Define Entity Status Display Model Status End Next ti Future Event List Produced by D K Harrison and D J Petty 21/04/02 Statistics Update ti Sld - 1420

Systems for Planning & Control in Manufacturing Stochastic Simulation 14 • Stochastic Processes are Uncertain • Many Processes are Stochastic • Stochastic Variation can be Modelled • General Conclusions • CDFs Model Stochastic Variation Produced by D K Harrison and D J Petty 21/04/02 Sld - 1421

Systems for Planning & Control in Manufacturing Example - Waiting Line Model Produced by D K Harrison and D J Petty 21/04/02 14 Sld - 1422

Systems for Planning & Control in Manufacturing Simulation – Limitations 14 • Simulations Themselves Can be Costly • Base Data can be Difficult to Collect • Simulations are Not Reality • A False Sense of Security Produced by D K Harrison and D J Petty 21/04/02 Sld - 1423

Systems for Planning & Control in Manufacturing Simulation - Summary 14 • It Uses Models of Real-World Situations • It Can be Applied to Many Different Problems • It is a Powerful Tool • Standard Packages are Now Available Produced by D K Harrison and D J Petty 21/04/02 Sld - 1424

Systems for Planning & Control in Manufacturing Project Management 15 • Common Activity for Engineers • Essential for Any Complex Task Allows Tasks to be Divided Allows Progress Monitoring Provides a Critical Path • Formal Methods Available • Techniques and Good Practice Produced by D K Harrison and D J Petty 21/04/02 Sld - 1501

Systems for Planning & Control in Manufacturing 04 Activities and Events Event Building Site Activity Construct Frame Event Completed Frame Activity AON Event Activity on Node (AON) Event AOA Activity on Arrow (AOA) Activity Produced by D K Harrison and D J Petty 21/04/02 Sld - 1502

Systems for Planning & Control in Manufacturing Modelling Projects – Example (1) 04 1. Boil Water 2. Locate Coffee 3. Locate Sugar 4. Locate Milk 5. Add Coffee and Sugar 6. Add Boiling Water 7. Add Milk 8. Serve Coffee 9. Drink Coffee Produced by D K Harrison and D J Petty 21/04/02 Sld - 1503

Systems for Planning & Control in Manufacturing 15 Modelling Projects – Example (2) Locate Milk 4 Add Milk Boil Water • Activity Precedence is Critical Locate Coffee Locate Sugar 6 1 2 3 1 Boil Wate r te Co ffee Lo ca te Su 2 ga r 3 Produced by D K Harrison and D J Petty 21/04/02 9 Serve Coffee AON Add Coffee And Sugar Locate Milk Loca 8 7 Add Boiling Water 5 Drink Coffee ee off ar d. C g Ad d Su An 4 5 d Ad g ilin Bo ater W Add Milk 6 Serve Coffee 7 Drink Coffee 8 Dummy Activity AOA Sld - 1504

Systems for Planning & Control in Manufacturing 15 Modelling Projects – Example (3) Locate Milk Boil Water • Activity Precedence is Critical Locate Coffee Locate Sugar 4 Add Milk 2 5 3 Boil Wate r Lo ca te Su 2 ga r 3 Produced by D K Harrison and D J Petty 21/04/02 ee off ar d. C g Ad d Su An 4 8 Add Boiling Water Add Coffee And Sugar Locate Milk ffee Drink Coffee 6 1 1 Loca te Co 7 9 Serve Coffee AON Note Change in Diagrams 5 Add Milk Add g Boilin Water 6 Serve Coffee 7 Drink Coffee 8 Dummy Activity AOA Sld - 1505

Systems for Planning & Control in Manufacturing Modelling Projects – Example (4) 15 • CPM – AON • PERT – AOA • AOA – Easy to Visualise • AOA – Needs Dummy Activities • Infinite Resources Assumed Produced by D K Harrison and D J Petty 21/04/02 Sld - 1506

Systems for Planning & Control in Manufacturing 15 AON Diagram (MS Project Format) Locate Milk 4 60 secs 60 0 Boil Water Add Boiling Water Add Milk 1 6 7 0 300 secs 300 301 Locate Coffee 5 316 15 Secs 330 Drink Coffee 8 9 331 120 Secs 450 451 540 Secs 990 Add Coff. + Sug. 2 15 Secs 315 Serve Coffee 0 60 Secs 60 61 15 Secs 75 Add Coff. + Sug. Locate Sugar 3 60 Secs 0 60 5 Activity Number Earliest Start Time 15 Secs 61 75 Activity Name Duration Earliest Finish Time Note: MS Project Does Not Handle Seconds Produced by D K Harrison and D J Petty 21/04/02 Sld - 1507

Systems for Planning & Control in Manufacturing 15 Use of a Gantt Chart Time (in Seconds) 100 200 300 400 500 600 700 800 900 Locate Milk Locate Coffee Locate Sugar Boil Water Add Coffee +Sugar Add Boiling Wat. Add Milk Serve Coffee Drink Coffee Critical Path Produced by D K Harrison and D J Petty 21/04/02 Sld - 1508

Systems for Planning & Control in Manufacturing Activity Scheduling - Definitions 15 • Critical Path – Longest Path Through a Network • Critical Activity – Activity on the Critical Path • Slack – Length of Time Available Before an Activity Needs to Start • ESD – Earliest Date that an Activity Could Start • EFD - Earliest Date that an Activity Could Finish • LSD – Latest Date that an Activity Could Start Without Extending Project • LFD – Latest Date that an Activity Could Finish Without Extending Project ESD = LSD, EFD = LFD, Slack = 0 for Critical Activities Produced by D K Harrison and D J Petty 21/04/02 Sld - 1509

Systems for Planning & Control in Manufacturing 15 Activity Scheduling – Principles Forward Pass Backward Pass • ESD = LSD, EFD = LFD, Slack = 0 for Critical Activities • ESD for an Activity is the Largest EFD of Immediate Predecessors • LFD for an Activity is the Smallest LSD of Immediate Successors Produced by D K Harrison and D J Petty 21/04/02 Sld - 1510

Systems for Planning & Control in Manufacturing 15 Activity Scheduling – Standard Form D 0 60 60 255 315 Slack =255 A X ESD EFD Duration Dur. LSD LFD 0 300 A. B. C. D. E. F. G. H. I. Activity 0 300 S 0 60 75 F 300 315 G 315 330 H 330 450 60 225 285 15 285 300 15 300 315 15 330 120 330 450 Slack =225 B C 0 E 60 Boil Water Locate Coffee Locate Sugar Locate Milk Add Coffee and Sugar Add Boiling Water Add Milk Serve Coffee Drink Coffee I 450 990 540 450 990 F 60 60 225 285 Slack =225 Activity Imm. Pred. Dur. ESD LSD EFD LFD Slack Crit. A 0 0 300 B • Note the Slight Difference to MS Project 300 60 0 225 60 285 225 C 60 0 225 60 285 225 D 60 0 255 60 315 255 225 Yes Produced by D K Harrison and D J Petty 21/04/02 B, C 15 60 285 75 300 F A, E 15 300 315 Yes G H D, F G 15 315 330 Yes 120 330 450 Yes I • Slack Cannot be Used Twice in All Cases E H 540 450 990 Yes Sld - 1511

Systems for Planning & Control in Manufacturing 15 Activity Scheduling – Logic Summary Greater of Two EFDs A 0 20 E 30 40 20 10 40 50 C 30 10 S 20 20 30 F B 0 10 E 30 50 10 10 20 20 30 50 Greater of Two EFDs Smaller of Two LSDs Produced by D K Harrison and D J Petty 21/04/02 Sld - 1512

Systems for Planning & Control in Manufacturing 15 Stochastic Activity Times • Activity Times Generally Stochastic • This Implies Project Times are Stochastic • What are the Chances of Success? • Need to Use Probability Distributions • Traditional to Use a Beta Distribution • Only Provides an Estimate P(x) a m, t P(x) b a x Produced by D K Harrison and D J Petty 21/04/02 Where: a = Pessimistic Estimate b = Optimistic Estimate m = Most Probable Time t = Expected Time Sigma = Standard Dev. mt P(x) b a x tm b x Sld - 1513

Systems for Planning & Control in Manufacturing 15 Project Uncertainty – Example - 1 D 0 What are the Chances of this Project Being Completed in Less than 17 Minutes (1020 Seconds)? 60 60 255 315 Slack =255 A 0 300 S 0 75 F 300 315 G 315 330 H 330 450 60 225 285 15 285 300 15 300 315 15 330 120 330 450 Slack =225 B C 0 60 E 60 I 450 990 540 450 990 F 60 60 225 285 Slack =225 = = = 1020 990 30 Slack as = 30 42 = Produced by D K Harrison and D J Petty 21/04/02 Project Deadline Project Time Project Slack 0. 715 Sld - 1514

Systems for Planning & Control in Manufacturing 15 Normal Distributions x 1 x 2 Produced by D K Harrison and D J Petty 21/04/02 Sld - 1515

Systems for Planning & Control in Manufacturing Project Uncertainty – Example - 2 15 By Linear Interpolation: y (x 2, y 2) (x 1, y 1) (xv, yv) More Accurate Tables for Standard Deviation Yield a Probability of 0. 7625 x Produced by D K Harrison and D J Petty 21/04/02 Sld - 1516

Systems for Planning & Control in Manufacturing 15 Project Uncertainty – Review Determine Project Times Determine Critical Path Step 2 Calculate Total Project Time and Total Slack Step 3 Calculate Variances and Total Variance Step 4 Express Project Slack In Terms of T Step 5 Determine Probability of T for Normal Distribution Produced by D K Harrison and D J Petty 21/04/02 Step 1 Step 6 Sld - 1517

Systems for Planning & Control in Manufacturing Limitations of the Method 15 • Distributions May Not be Appropriate • Non-Critical Paths May be Significant • Tasks May not be Independent • Summation of Variances May not be Appropriate • Assumptions in Calculations Must be Taken into Account • Still a Useful Technique Produced by D K Harrison and D J Petty 21/04/02 Sld - 1518

Systems for Planning & Control in Manufacturing Use of Project Planning Packages 15 • Now Very Common (e. g. , MS Project) • Minimises Manual Effort • Improves Reporting Quality • Can Provide Multi-User Access • Can Encourage Over-Planning • Large Benefits for Complex Projects Produced by D K Harrison and D J Petty 21/04/02 Sld - 1519

Systems for Planning & Control in Manufacturing Good Project Planning Practice 15 • Good Planning is Usually Rewarded • Lack of Planning is Usually Punished • Uncertain Situations Still Need Plans • Avoid Over Simplification • Avoid Over Complication • Do Not Allow Plans to Inhibit Action Produced by D K Harrison and D J Petty 21/04/02 Sld - 1520

Systems for Planning & Control in Manufacturing X Course Book Systems for Planning & Control in Manufacturing: Systems and Management for Competitive Manufacture Professor David K Harrison Dr David J Petty ISBN 0 7506 49771 Produced by D K Harrison and D J Petty 21/04/02 Sld - 0000