Reading Assignment For next class: Case 8: Lot Sizing at Altametal (pg. 111)

Slides:



Advertisements
Similar presentations
Supply Decisions.
Advertisements

Optimization problems using excel solver
BU Decision Models Integer_LP1 Integer Optimization Summer 2013.
Linear Programming. Introduction: Linear Programming deals with the optimization (max. or min.) of a function of variables, known as ‘objective function’,
Planning with Linear Programming
© 2004 Prentice-Hall, Inc. 8-1 Chapter 8 Aggregate Planning in the Supply Chain Supply Chain Management (3rd Edition)
Operations Management Linear Programming Module B - Part 2
Introduction to Management Science
1 Ardavan Asef-Vaziri Nov-2010Theory of Constraints 1- Basics Practice; Follow the 5 Steps Process Purchased Part $5 / unit RM1 $20 per unit RM2 $20 per.
Linear Programming Excel Solver. MAX8X 1 + 5X 2 s.t.2X 1 + 1X 2 ≤ 1000 (Plastic) 3X 1 + 4X 2 ≤ 2400 (Prod. Time) X 1 + X 2 ≤ 700 (Total Prod.) X 1 - X.
Example 6.2 Fixed-Cost Models | 6.3 | 6.4 | 6.5 | 6.6 | Background Information n The Great Threads Company is capable of manufacturing.
QM B Linear Programming
Integer Programming Integer programming is a solution method for many discrete optimization problems Programming = Planning in this context Origins go.
Classification of Costs
Chapter 5 Aggregate Planning Operations Analysis Using MS Excel.
Linear Goal Programming
Lecture 6 The Chinagro agricultural supply model at county level P.J. Albersen Presentation available:
Lecture outline Support vector machines. Support Vector Machines Find a linear hyperplane (decision boundary) that will separate the data.
Introduction to Management Science
Lecture 10: Support Vector Machines
Example 15.2 Blending Oil Products at Chandler Oil
9/1 More Linear Programming Collect homework Roll call Review homework Lecture - More LP Small Groups Lecture - Start using MS Excel Assign Homework.
Graduate Program in Business Information Systems Integer and Goal Programming Aslı Sencer.
Systems of Equations and Inequalities
9-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Multicriteria Decision Making Chapter 9.
Multicriteria Decision Making
Financial and Cost-Volume-Profit Models
1 Capacity Planning under Uncertainty Capacity Planning under Uncertainty CHE: 5480 Economic Decision Making in the Process Industry Prof. Miguel Bagajewicz.
Product Mix Problem Monet company makes four types of frames.
A GAMS TUTORIAL. WHAT IS GAMS ? General Algebraic Modeling System Modeling linear, nonlinear and mixed integer optimization problems Useful with large,
Integer Programming Models
Decision Making via Linear Programming: A simple introduction Fred Phillips
Modeling and Optimization of Aggregate Production Planning – A Genetic Algorithm Approach B. Fahimnia, L.H.S. Luong, and R. M. Marian.
0 A Toy Production Problem  How many units to produce from each product type in order to maximize the profit? ProductMan-PowerMachineProfit Type A3 h1.
Chapter 9 - Multicriteria Decision Making 1 Chapter 9 Multicriteria Decision Making Introduction to Management Science 8th Edition by Bernard W. Taylor.
CDAE Class 11 Oct. 3 Last class: Result of Quiz 2 2. Review of economic and business concepts Today: Result of Quiz 2 3. Linear programming and applications.
Contemporary Engineering Economics, 6 th edition Park Copyright © 2016 by Pearson Education, Inc. All Rights Reserved Classification of Costs Lecture No.
CDAE Class 12 Oct. 5 Last class: Quiz 3 3. Linear programming and applications Today: Result of Quiz 3 3. Linear programming and applications Next.
Minimax Open Shortest Path First (OSPF) Routing Algorithms in Networks Supporting the SMDS Service Frank Yeong-Sung Lin ( 林永松 ) Information Management.
Exam Covers everything not covered by the last exam. Starts with simulation If you cant’ get the optimal solution – get a pretty good one. You may run.
Planning Horizons Today3 Months 1 year5 years Planning Horizon Short-range plans Job assignments Ordering Job scheduling Dispatching Intermediate-range.
CSC 2535 Lecture 8 Products of Experts Geoffrey Hinton.
1 CA202 Spreadsheet Application Analyzing Alternative Data Sets Lecture # 8 Dammam Community college.
 Review the principles of cost-volume-profit relationships  Discuss Excel what-if analysis tools 2.
Contents Introduction Aggregate planning problem
Reading Assignment For after break read: Case 6: WestPlast (pg. 104) Case 8: Lot Sizing at Altametal (pg. 111)
MGTSC 352 Lecture 14: Aggregate Planning WestPlast Case H ow to deal with multiple objectives How to use binary variables AltaMetal Case Aggregating into.
IT Applications for Decision Making. Operations Research Initiated in England during the world war II Make scientifically based decisions regarding the.
Linear Programming Optimal Solutions and Models Without Unique Optimal Solutions.
PowerPoint presentation to accompany Chopra and Meindl Supply Chain Management, 5e 1-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall.
OPSM 301 Operations Management Class 13&14: Linear Programming using Excel Koç University Zeynep Aksin
Introduction to Integer Programming Integer programming models Thursday, April 4 Handouts: Lecture Notes.
-114- HMP654/EXECMAS Linear Programming Linear programming is a mathematical technique that allows the decision maker to allocate scarce resources in such.
Chapter 5, Section 3 Cost, Revenue, and Profit Maximization.
Chapter 6 Integer, Goal, and Nonlinear Programming Models © 2007 Pearson Education.
1 Simplex algorithm. 2 The Aim of Linear Programming A Linear Programming model seeks to maximize or minimize a linear function, subject to a set of linear.
Linear Programing Problem
Classification of Costs
MGTSC 352 Lecture 15: Aggregate Planning Altametal Case
Using Variable Domain Functions
Excel Solver.
Frank Yeong-Sung Lin (林永松) Information Management Department
Lecture 19: MGTSC 352 Distribution Planning
LINGO LAB 3/4.
Perfect Competition Long Run Overheads.
Linear Programming Excel Solver.
Lecture 5 Binary Operation Boolean Logic. Binary Operations Addition Subtraction Multiplication Division.
Frank Yeong-Sung Lin (林永松) Information Management Department
Linear Programming Integer Linear Models.
Linear Optimization using Excel
Presentation transcript:

Reading Assignment For next class: Case 8: Lot Sizing at Altametal (pg. 111)

MGTSC 352 Lecture 13: Aggregate Planning WestPlast Case H ow to deal with multiple objectives How to use binary variables

WestPlast (based on a true story) Plastic pellets Continuous chemical process –Product switching results in waste Capacity < Demand –Cap: 335,000 tonnes (est.) (could be as high as 425,000 tonnes) Contractual obligations and forecasts

Active Learning Pairs, 1 min. How does Westplast evaluate the “goodness” of a production plan? What do you think of their approach?

WestPlast Criteria Maximize Revenue Maximize Plant Capability Index (PCI) 1.plant output rate 2.quality compared to industry standards 3.raw material quality needed 4.overhead burden 5.process aggravation each subcriteria has a “weight ” (10% - 30%)

PCI Example 1.plant output rate –Product X: 100 –Product Y: 70 (slower) 2.quality compared to industry standards –Product G: 100 (excellent quality) –Product H: 80 (slightly lower quality) 3.raw material quality needed –Product Q: 100 (least expensive raw material) –Product W: 60 (more expensive raw material) 4.overhead burden (low OH = 100) 5.process aggravation (low aggravation = 100)

PCI Example continued Product Score Card.20 (output score) +.10 (quality score) +.30 (raw mat. score) +.15 (overhead score) +.25 (aggravation score) The result is a score between 0 and 100 with more desirable products scoring closer to 100.

Questions Is their plan (pg. 80, column G) a good plan? Can we find a better plan? How? What is ‘better’? Excel Pgs

Questions Our “better plan” produces 9 products. Suppose that, on the average, adding a product takes machine time equivalent to 10,000 lbs of output per product. Does WestPlast want to make more than 9 products? Less than 9 products? How can we find out? Pgs

Using Binary Variables to Limit # of Products Add binary decision variable for each product (1 = produce, 0 = don’t produce) Add binary constraints Want: –If binary variable = 0 then amt. produced = 0 –If binary variable = 1 then amt. produced ≤ demand IF() formulas would make the problem nonlinear Instead: –Add constraint amt. produced ≤ (binary variable)  demand Pgs

WestPlast wants to: Maximize Revenue Maximize PCI = Plant Capability Index By changing product mix Subject to: –Contractual obligations –Don’t produce more than forecast demand How can we optimize two criteria at the same time?