Lessons from Project Phase-I zGood modeling practices yExtensive model documentation yGenerality of the model yWriting constraints zGood reporting practices.

Slides:



Advertisements
Similar presentations
Chapter 9 Analyzing Results Using The Income Statement
Advertisements

BA 452 Lesson B.3 Integer Programming 11ReadingsReadings Chapter 7 Integer Linear Programming.
Linear Programming Models & Case Studies
Session 3a Decision Models -- Prof. Juran.
Introduction to Management Science
Linear Programming Using the Excel Solver
Basic Feasible Solutions: Recap MS&E 211. WILL FOLLOW A CELEBRATED INTELLECTUAL TEACHING TRADITION.
Consumer Packaged Goods Manufacturing Industry Team: Aymaras Pan American Advanced Studies Institute Simulation and Optimization of Globalized Physical.
Project Plans CSCI102 - Systems ITCS905 - Systems MCS Systems.
July 11 th, 2005 Software Engineering with Reusable Components RiSE’s Seminars Sametinger’s book :: Chapters 16, 17 and 18 Fred Durão.
Computational Methods for Management and Economics Carla Gomes
Reproduced with permission from BESTEAMS 2004
Transportation Problems Dr. Ron Tibben-Lembke. Transportation Problems Linear programming is good at solving problems with zillions of options, and finding.
[BA3750 PRODUCT or SERVICE] Your Logo Here Insert Product Photograph Here.
Midterm Notes Modeling and Best Decision SAILS & Manugistics Powerpoint Slides Presentations.
Lesson Components of an Effective Marketing Plan
Solver & Optimization Problems n An optimization problem is a problem in which we wish to determine the best values for decision variables that will maximize.
3 Components for a Spreadsheet Linear Programming Problem There is one cell which can be identified as the Target or Set Cell, the single objective of.
Personal Software Process Overview CIS 376 Bruce R. Maxim UM-Dearborn.
Graphical Solutions Plot all constraints including nonnegativity ones
Optimization II. © The McGraw-Hill Companies, Inc., 2004 Operations Management -- Prof. Juran2 Outline Optimization Extensions Multiperiod Models –Operations.
Two Discrete Optimization Problems Problem: The Transportation Problem.
Solver & Optimization Problems n An optimization problem is a problem in which we wish to determine the best values for decision variables that will maximize.
Network Models II Shortest Path Cross Docking Enhance Modeling Skills Modeling with AMPL Spring 03 Vande Vate.
Chapter 3 Supply Chain Drivers and Obstacles
The Supply Chain Customer Supplier Manufacturer Distributor
Designing Spreadsheet LP Models
1 1 Project #1 Optimization Model John H. Vande Vate Spring, 2001.
Types of IP Models All-integer linear programs Mixed integer linear programs (MILP) Binary integer linear programs, mixed or all integer: some or all of.
Total Cost in the Supply Chain zIn the past a focus on departmental budgets yPurchasing -- piece price yTransportation -- freight bill yMaterial Handling.
Transportation Problems Dr. Ron Lembke. Transportation Problems Linear programming is good at solving problems with zillions of options, and finding the.
Spring 03 Vande Vate1 Agenda Practice Exam to help you prepare for Tuesday Questions.
New Stage zCourse Phases yDescription of Systems and Issues yPrescriptive tools and modeling yInternational Aspects.
Linear Programming last topic of the semester What is linear programming (LP)? Not about computer programming “Programming” means “planning” “Linear” refers.
Roll Up Your Sleeves and Start Writing Writing and Presenting a Business Plan Chapter 3.
Word problems DON’T PANIC! Some students believe they can’t do word problem solving in math. Don’t panic. It helps to read the question more than once.
Weight and Cube, Frequency zExtend Network Flows to Multicommodity zMore than one product zDifferent products share conveyance capacity zDelivery Schedules.
15.053Tuesday, April 9 Branch and Bound Handouts: Lecture Notes.
Performance Mean 65. Solutions to Exam 2 zWe have a single commodity in warehouses around the country and wish to ship it to our customers at minimum.
Two Discrete Optimization Problems Problem: The Transportation Problem.
What-If Analysis for Linear Programming
1 1 Solutions to Exam #1 John H. Vande Vate Fall, 2002.
1 1 Exam I John H. Vande Vate Spring, Question 1 … centers to minimize its total transportation costs from its 2 plants to its 5 markets. We.
1 1 Modeling Flows John H. Vande Vate Spring, 2006.
Intelligent Supply Chain Management Strategic Supply Chain Management
IT Applications for Decision Making. Operations Research Initiated in England during the world war II Make scientifically based decisions regarding the.
Class Project zWork in groups of 4 or 5 zProject has 3 parts zPart 1 due October 18th. zPart 2 due November 6th. zPart 3 due November 29th.
1 1 Transportation & Supply Chain Systems John H. Vande Vate Spring, 2001.
Logistics Strategy & Implementation
3 Components for a Spreadsheet Optimization Problem  There is one cell which can be identified as the Target or Set Cell, the single objective of the.
Main Function of SCM (Part I)
Introduction to Integer Programming Integer programming models Thursday, April 4 Handouts: Lecture Notes.
World of Wokcraft The very best in Single pan cooking themed fantasy gaming!
Modeling Flows and Information
Chapter 3 Supply Chain Drivers and Obstacles
Chapter 6 The Traditional Approach to Requirements.
Location Case Study Shanghai GM Service Parts Part II
The assignment problem
Insert Product Photograph Here
Product Name.
Chapter 3 Supply Chain Drivers and Obstacles
Product Name.
Product Name.
Tonga Institute of Higher Education IT 141: Information Systems
Tonga Institute of Higher Education IT 141: Information Systems
Chapter 3 Supply Chain Drivers and Obstacles
Project #1 Optimization Model
Overview of Intermodal (Multimodal) Supply Chain Optimization and Logistics Scott J. Mason, Ph.D. Fluor Endowed Chair in Supply Chain Optimization and.
Modeling Flows and Information
Presentation transcript:

Lessons from Project Phase-I zGood modeling practices yExtensive model documentation yGenerality of the model yWriting constraints zGood reporting practices yTo a manager, not a professor ! yExecutive Summary - about the business yClarity, brevity, …………

Model documentation zIncludes defining meaningful variable names zDoesn’t hurt in any way zWhat if somebody has to continue the work you started ? zWhat if you have to take over somebody else’s work ? zEver debugged somebody’s code ? That should be a good lesson !

Model generality zEXTREMELY IMPORTANT zWhat do you do when (not “IF” !) the world changes ? yAddition/deletion of ports, DCs, etc. yPrices, duties, etc. change frequently y…………

Model generality zIntegrating model and data is a very, very bad idea. zWe won’t deduct too many points for that this time, but from Phase-II onwards, if you have data in your model………

A word of caution on writing constraints z5x 1 + x 2  10 z x 1 + 5x 2  10 against z6x 1 + 6x 2  20 zWhich is better ?

Reporting zWhenever, you make or solve a model in the real world, you will present it to higher management (senior managers, VPs, etc.) zThey’re NOT interested in methodology, or your math, unless………… zYou show them the impact on business !!

Reporting zMake sure your solution makes sense yLogically follows from the data (at least some major decisions) yIncludes all relevant components, e.g. xPurchasing xTransportation xInventory xCapital xDuties and taxes x…………………

Executive summary zSolution overview (“Best” strategy) z“Take the solution back through your calculations”………… zWhere is the money going ? yCost breakdowns by logical categories xTypes, Segments, Regions, etc. zWhat are other key issues to consider ? zAny alternatives ? With what tradeoffs ? zAt most 1-2 pages with charts, tables, etc.

Main report and appendices zDetailed solution and discussion, along with methodology, assumptions, etc. + zYour VALUE ADDITION !!! yCan you suggest something beyond the solution of the problem given to you ? zAppendices - for the “nerds” !

Project Phase-I Model: Sets zset SOURCES; zset PORTS; zset DCS; zset EDGES := (SOURCES cross PORTS) union (PORTS cross DCS);

Project Phase-I Model: Variables /* The Variables in the model are the no. of containers shipped each year on each edge */ zvar Shipments{EDGES} integer >= 0; zThis is a network flow problem, so integral data implies integral solutions.

Project Phase-I Model: Constraints zDo not exceed supply at any source s.t. ObserveCapacity {source in SOURCES}: sum{(source, t) in EDGES} Shipments[source, t] <= SourceCap[source]; zConserve Flow at Ports s.t. ConserveFlow {port in PORTS}: sum{(f, port) in EDGES} Shipments[f, port] = sum{(port, t) in EDGES} Shipments[port, t]; zMeet Demand at DCS s.t. MeetDemand {dc in DCS}: sum{(t, dc) in EDGES} Shipments[t,dc] >= Demand[dc];

Project Phase-I Model: Objective function minimize TotalCost: zPurchase Price + Duties zFreight Costs zMoving Inventory Costs (pipeline) zWaiting Inventory Costs (Note: these should include the waiting time at the sources, but we forgot to tell you how often ships are scheduled to sail)

A not so good way for variables, but it works too zIf you don’t want to work with EDGES, you may define variables by segment, like : ShipSourcePort {SOURCES, PORTS}; ShipPortDC {PORTS, DCS}; zNot the best way, but it’s acceptable too. zUsing EDGES is much more elegant.

A caution on number of variables and constraints zStudent versions of Xpress and AMPL support 300 variables and 300 constraints. zTo see how many you’ve used, use the AMPL command : option show_stats 1;

Project Phase-I Answer z18000 containers/year - Brazil to Norfolk z2000 containers/year - Brazil to Long Beach z10000 containers/year - China to Long Beach z18000 containers/year - Norfolk to NY z12000 containers/year - Long Beach to LA zObvious solution ! Why ?

Project Phase-I Answer zTotal cost : $ billion, depending on your assumptions y$1.2 billion purchase cost y$72 million duties y………………… zIn this phase, purchase cost (excluding duties) doesn’t affect the decision, so we can remove it from the objective function zHowever, still need to report it !

Project Phase-II zSomething close to the real thing zMuch more detailed model than Phase-I, so get on it ASAP ! yMultiple products yMultiple modes of transport yWeight and cube constraints y………………… zUse “option solver cplex;” before “solve;”

Project Phase-II zRecall this ? /* set…………param………var…………s.t…………… Solve the problem You may need a command like option solver cplex; */ solve; zWhy ?

Project Phase-II zNot a network flow problem anymore, so… zIntegral data does not imply integral solutions ! zAMPL solvers yMINOS - default, doesn’t solve IPs yCPLEX