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Deterministic Models in OR Joseph Khamalah, Ph.D. K327:02.

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Presentation on theme: "Deterministic Models in OR Joseph Khamalah, Ph.D. K327:02."— Presentation transcript:

1 Deterministic Models in OR Joseph Khamalah, Ph.D. K327:02

2 2 Agenda: Week of August 26, 2010  Introduction – Syllabus – Student Info Sheet – Exams – Cases/Assignments – Peer Evaluation  Group Formation

3 3 Grade Weights Within Area Total a) INDIVIDUAL WORK (55-75%)…………...…… 60% Exams (2@ 20 – 35%) ………40% Assignments (10 – 20%) …….20% b) GROUP WORK (25-45%)……………….…..40% Exams (2@ 10 – 20%)…..…...20% Cases (4@ 01 – 03) …………..12% Project (01 – 10%) …....……...08% Grand Total…………...………..………... 100%

4 Deterministic Models in Operations Research Introduction – Chapter 1

5 5 Learning Objectives  Develop a general understanding of the management science / operations research approach to decision making.  Realize that quantitative applications begin with a problem situation.  Obtain a brief introduction to quantitative techniques and their frequency of use in practice.  Understand that managerial problem situations have both quantitative and qualitative considerations that are important in the decision making process.  Learn about models in terms of what they are and why they are useful (the emphasis is on mathematical models).  Identify the step-by-step procedure that is used in most quantitative approaches to decision making.  Learn about basic models of cost, revenue, and profit and be able to compute the breakeven point.

6 6 Introduction to Operations Research  Operations Research/Management Science – Winston: “a scientific approach to decision making, which seeks to determine how best to design and operate a system, usually under conditions requiring the allocation of scarce resources.” – Kimball & Morse: “a scientific method of providing executive departments with a quantitative basis for decisions regarding the operations under their control.”

7 7 Introduction to OR cont’  Provides rational basis for decision making – Solves the type of complex problems that turn up in the modern business environment – Builds mathematical and computer models of organizational systems composed of people, machines, and procedures – Uses analytical and numerical techniques to make predictions and decisions based on these models

8 8 Introduction to OR cont’  Draws upon – engineering, management, mathematics  Closely related to the "decision sciences" – applied mathematics, computer science, economics, industrial engineering and systems engineering

9 9 Body of Knowledge  The body of knowledge involving quantitative approaches to decision making is referred to as – Management Science – Operations Research – Decision Science – Operations Management – Industrial Engineering – Industrial Management – Systems Management – Systems Science  It has its early roots in World War II and is flourishing in business and industry with the aid of computers.

10 10 What is OR? Cont’  Origins in the 1940's with the management of the Cold War armed forces. Currently used in a very broad range of organizations and systems.  The linear programming model for solving OR problems was developed in the 1940's. The first computer implementation to solve linear programs, known as the simplex method was developed in 1947 by George Dantzig.  The Nobel Prize in Economics was awarded in 1975 to L.V. Kantorovich and T.C. Koopmans for their work in linear programming. Linear programming was also used in portfolio management by H.M. Marcowitz, who won the Nobel Prize in 1990.

11 11 Features of OR  Emphasis on: – large, complex operations – mathematical models – computer implementation  Extensive use in: – manufacturing – transportation – entertainment – construction/development – communication – computer/database systems – economics/investing – armed forces – biology/genomics

12 12 Examples of OR Problems  resource allocation  scheduling  routing  inventory management  system design and construction  production  pricing  forecasting  capital budgeting  customer service

13 13 The role of the OR Analyst 1. Formulate the Problem: Determine the nature and constraints of the problem, and the goal(s) of the client for the problem. Determine a model class appropriate for the problem. 2. Observe the system: Collect the facts and data necessary for precisely specifying and solving the model. 3. Formulate a mathematical model needed for the problem solution and identify the software tools used to solve this mathematical model. 4. Verify the Model: Check the results of the model on known situations to see if it gives reasonable and predictable answers in these cases. 5. Determine a solution to the new case according to the model developed above. 6. Present the solution in a form which the client can use. 7. Assist in the implementation of the solution

14 14  7 Steps of Problem Solving (First 5 steps are the process of decision making) – Define the problem. – Identify the set of alternative solutions. – Determine the criteria for evaluating alternatives. – Evaluate the alternatives. – Choose an alternative (make a decision). --------------------------------------------------------------------- – Implement the chosen alternative. – Evaluate the results. Problem Solving and Decision Making

15 15 Quantitative Analysis and Decision Making  Potential Reasons for a Quantitative Analysis Approach to Decision Making – The problem is complex. – The problem is very important. – The problem is new. – The problem is repetitive.

16 16 Quantitative Analysis  Quantitative Analysis Process – Model Development – Data Preparation – Model Solution – Report Generation

17 17 Model Development  Models are representations of real objects or situations  Three forms of models are: – Iconic models - physical replicas (scalar representations) of real objects; – Analog models - physical in form, but do not physically resemble the object being modeled; and – Mathematical models - represent real world problems through a system of mathematical formulae and expressions based on key assumptions, estimates, or statistical analyses.

18 18 Advantages of Models  Generally, experimenting with models (compared to experimenting with the real situation): – requires less time – is less expensive – involves less risk

19 19 Mathematical Models  Cost/benefit considerations must be made in selecting an appropriate mathematical model.  Frequently a less complicated (and perhaps less precise) model is more appropriate than a more complex and accurate one due to cost and ease of solution considerations. For every complex problem, there is a simple solution, which is neat and wrong!!

20 20 Mathematical Models, cont’  Relate decision variables (controllable inputs) with fixed or variable parameters (uncontrollable inputs)  Frequently seek to maximize or minimize some objective function subject to constraints  Are said to be stochastic if any of the uncontrollable inputs is subject to variation, otherwise are deterministic  Generally, stochastic models are more difficult to analyze.  The values of the decision variables that provide the mathematically-best output are referred to as the optimal solution for the model.

21 21 Transforming Model Inputs into Output Uncontrollable Inputs (Environmental Factors) Uncontrollable Inputs (Environmental Factors) Controllable Inputs (Decision Variables) Controllable Inputs (Decision Variables) Output (Projected Results) Output (Projected Results) Mathematical Model Mathematical Model

22 22 Example: Project Scheduling Consider the construction of the Phase III student housing complex. The project consists of hundreds of activities involving excavating, framing, wiring, plastering, painting, land- scaping, and more. Some of the activities must be done sequentially and others can be done at the same time. Also, some of the activities can be completed faster than normal by purchasing additional resources (workers, equipment, etc.).

23 23 Example: Project Scheduling, cont’  Question: What is the best schedule for the activities and for which activities should additional resources be purchased? How could management science be used to solve this problem?  ***** Management science can provide a structured, quantitative approach for determining the minimum project completion time based on the activities' normal times and then based on the activities' expedited (reduced) times.

24 24 Example: Project Scheduling, cont’  Question: What would be the uncontrollable inputs?  ***** – Normal and expedited activity completion times – Activity expediting costs – Funds available for expediting – Precedence relationships of the activities

25 25 Example: Project Scheduling, cont’  Question: What would be the: 1. objective function of the mathematical model? 2. the decision variables? 3. the constraints?  ***** 1. Objective function: minimize project completion time 2. Decision variables: which activities to expedite and by how much, and when to start each activity 3. Constraints: do not violate any activity precedence relationships and do not expedite in excess of the funds available.

26 26 Example: Project Scheduling, cont’  Question: Is the model deterministic or stochastic?  ***** Stochastic. Activity completion times, both normal and expedited, are uncertain and subject to variation. Activity expediting costs are uncertain. The number of activities and their precedence relationships might change before the project is completed due to a project design change.

27 27 Example: Project Scheduling, cont’  Question: Suggest assumptions that could be made to simplify the model.  ***** Make the model deterministic by assuming normal and expedited activity times are known with certainty and are constant. The same assumption might be made about the other stochastic, uncontrollable inputs.

28 28 Data Preparation  Data preparation is not a trivial step, due to the time required and the possibility of data collection errors.  A model with 50 decision variables and 25 constraints could have over 1300 data elements!  Often, a fairly large data base is needed.  Information systems specialists might be needed.

29 29 Model Solution  The analyst attempts to identify the alternative (the set of decision variable values) that provides the “best” output for the model.  The “best” output is the optimal solution.  If the alternative does not satisfy all of the model constraints, it is rejected as being infeasible, regardless of the objective function value.  If the alternative satisfies all of the model constraints, it is feasible and a candidate for the “best” solution.

30 30 Model Solution, cont’  One solution approach is trial-and-error. – Might not provide the best solution – Inefficient (numerous calculations required)  Special solution procedures have been developed for specific mathematical models. – Some small models/problems can be solved by hand calculations – Most practical applications require using a computer

31 31 Computer Software  OR would not have developed as a field without the computer.  Currently there are many software packages each of which handles a particular class of models. Examples of decision models for which there is extensive software available: – linear/integer programming models – network & routing models – decision tree models – simulation models – queuing models – investment models  A variety of these s/w packages are available on IPFW’s network – Spreadsheet packages such as Microsoft Excel – The Management Scientist, developed by the textbook authors – DS - Decision Science – POM – HOM

32 32 Quantitative Methods in Practice n Linear Programming n Integer Linear Programming n PERT/CPM n Inventory models n Waiting Line Models n Simulation n Decision Analysis n Goal Programming n Analytic Hierarchy Process n Forecasting n Markov-Process Models

33 33 Model Testing and Validation  Often, goodness/accuracy of a model cannot be assessed until solutions are generated.  Small test problems having known, or at least expected, solutions can be used for model testing and validation.  If the model generates expected solutions, use the model on the full-scale problem.  If inaccuracies or potential shortcomings inherent in the model are identified, take corrective action such as: – Collection of more-accurate input data – Modification of the model

34 34 Report Generation  A managerial report, based on the results of the model, should be prepared.  The report should be easily understood by the decision maker.  The report should include: – the recommended decision – other pertinent information about the results (for example, how sensitive the model solution is to the assumptions and data used in the model)

35 35 Implementation and Follow-Up  Successful implementation of model results is of critical importance.  Secure as much user involvement as possible throughout the modeling process.  Continue to monitor the contribution of the model.  It might be necessary to refine or expand the model.

36 36 Example: Austin Auto Auction  An auctioneer has developed a simple mathematical model for deciding the starting bid s/he will require when auctioning a used automobile.  Essentially, s/he sets the starting bid at seventy (70) percent of what s/he predicts the final winning bid will (or should) be. S/he predicts the winning bid by starting with the car's original selling price and making two deductions, one based on the car's age and the other based on the car's mileage.  The age deduction is $800 per year and the mileage deduction is $0.025 per mile.

37 37 Example: Austin Auto Auction, cont’  Question: Develop the mathematical model that will give the starting bid (B) for a car in terms of the car's original price (P), current age (A) and mileage (M).  ***** The expected winning bid can be expressed as: P - 800(A) - 0.025(M) The entire model is: B = 0.7(expected winning bid) B = 0.7(P - 800(A) - 0.025(M)) B = 0.7(P) - 560(A) - 0.0175(M) or B = 0.7P - 560A - 0.0175M

38 38 Example: Austin Auto Auction, cont’  Question: Suppose a four-year old car with 60,000 miles on the odometer is up for auction. If its original price was $12,500, what starting bid should the auctioneer require?  ***** B = 0.7(12,500) - 560(4) - 0.0175(60,000) = 8,750.00 - 2,240.00 – 1,050.00 = $5,460.00

39 39 Example: Austin Auto Auction, cont’  Question: The model is based on what assumptions?  *****  The model assumes that the only factors influencing the value of a used car are the original price, age, and mileage (not condition, rarity, or other factors).  Also, it is assumed that age and mileage devalue a car in a linear manner and without limit. (Note, the starting bid for a very old car might be negative!)

40 40 Example: Iron Works, Inc. Iron Works, Inc. manufactures two products made from steel. It has just received this month's allocation of b pounds of steel. It takes a 1 pounds of steel to make a unit of product 1 and a 2 pounds of steel to make a unit of product 2. Let x 1 and x 2 denote this month's production level of product 1 and product 2, respectively. Denote by p 1 and p 2 the unit profits for products 1 and 2, respectively. Iron Works has a contract calling for at least m units of product 1 this month. The firm's facilities are such that at most u units of product 2 may be produced monthly.

41 41 Example: Iron Works, Inc., cont’  Mathematical Model – The total monthly profit = (profit per unit of product 1) x (monthly production of product 1) + (profit per unit of product 2) x (monthly production of product 2) = p 1 x 1 + p 2 x 2 We want to maximize total monthly profit: Max p 1 x 1 + p 2 x 2

42 42 Example: Iron Works, Inc., cont’  Mathematical Model (continued) – The total amount of steel used during monthly production equals: (steel required per unit of product 1) x (monthly production of product 1) + (steel required per unit of product 2) x (monthly production of product 2) = a 1 x 1 + a 2 x 2 This quantity must be less than or equal to the allocated b pounds of steel: a 1 x 1 + a 2 x 2 < b

43 43 Example: Iron Works, Inc., cont’  Mathematical Model (continued) – The monthly production level of product 1 must be greater than or equal to m : x 1 > m – The monthly production level of product 2 must be less than or equal to u : x 2 < u – However, the production level for product 2 cannot be negative: x 2 > 0

44 44 Example: Iron Works, Inc., cont’  Mathematical Model Summary Max p 1 x 1 + p 2 x 2 s.t. a 1 x 1 + a 2 x 2 < b x 1 > m x 2 < u x 2 > 0 ObjectiveFunction “Subject to” Constraints

45 45 Example: Iron Works, Inc., cont’  Question: Suppose b = 2000, a 1 = 2, a 2 = 3, m = 60, u = 720, p 1 = 100, p 2 = 200. Rewrite the model with these specific values for the uncontrollable inputs.  ***** Substituting, the model is: Max 100x 1 + 200x 2 s.t. 2x 1 + 3x 2 < 2000 x 1 > 60 x 2 < 720 x 2 > 0

46 46 Example: Iron Works, Inc., cont’  Question: The optimal solution to the current model is x 1 = 60 and x 2 = 626 2/3. If the product were engines, explain why this is not a true optimal solution for the "real-life" problem.  *****  One cannot produce and sell 2/3 of an engine. Thus the problem is further restricted by the fact that both x 1 and x 2 must be integers. They could remain fractions if it is assumed these fractions are work in progress to be completed the next month.

47 47 Example: Iron Works, Inc., cont’ Uncontrollable Inputs $100 profit per unit Prod. 1 $200 profit per unit Prod. 2 2 lbs. steel per unit Prod. 1 3 lbs. Steel per unit Prod. 2 2000 lbs. steel allocated 60 units minimum Prod. 1 720 units maximum Prod. 2 0 units minimum Prod. 2 $100 profit per unit Prod. 1 $200 profit per unit Prod. 2 2 lbs. steel per unit Prod. 1 3 lbs. Steel per unit Prod. 2 2000 lbs. steel allocated 60 units minimum Prod. 1 720 units maximum Prod. 2 0 units minimum Prod. 2 60 units Prod. 1 626.67 units Prod. 2 60 units Prod. 1 626.67 units Prod. 2 Controllable Inputs Profit = $131,333.33 Steel Used = 2000 Profit = $131,333.33 Steel Used = 2000 OutputOutput Mathematical Model Max 100(60) + 200(626.67) s.t. 2(60) + 3(626.67) < 2000 60 > 60 626.67 < 720 626.67 > 0 Max 100(60) + 200(626.67) s.t. 2(60) + 3(626.67) < 2000 60 > 60 626.67 < 720 626.67 > 0

48 48 Example: Ponderosa Development Corp.  Ponderosa Development Corporation (PDC) is a small real estate developer that builds only one style house. The selling price of the house is $115,000.  Land for each house costs $55,000 and lumber, supplies, and other materials run another $28,000 per house. Total labor costs are approximately $20,000 per house.

49 49 Example: Ponderosa Development Corp.  Ponderosa leases office space for $2,000 per month. The cost of supplies, utilities, and leased equipment runs another $3,000 per month.  The one salesperson of PDC is paid a commission of $2,000 on the sale of each house. PDC has seven permanent office employees whose monthly salaries are given on the next slide.

50 50 Example: Ponderosa Development Corp. Employee Monthly Salary President$10,000 VP, Development 6,000 VP, Marketing 4,500 Project Manager 5,500 Controller 4,000 Office Manager 3,000 Receptionist 2,000

51 51 Example: Ponderosa Development Corp.  Question: Identify all costs and denote the marginal cost and marginal revenue for each house.  ***** – The monthly salaries total $35,000 and monthly office lease and supply costs total another $5,000. This $40,000 is a monthly fixed cost. – The total cost of land, material, labor, and sales commission per house, $105,000, is the marginal cost for a house. – The selling price of $115,000 is the marginal revenue per house.

52 52 Example: Ponderosa Development Corp.  Question: Write the monthly cost function c (x), revenue function r (x), and profit function p (x).  ***** c (x) = variable cost + fixed cost = 105,000x + 40,000 r (x) = 115,000x p (x) = r (x) - c (x) = 10,000x - 40,000

53 53 Example: Ponderosa Development Corp.  Question: What is the breakeven point for monthly sales of the houses?  ***** r (x ) = c (x ) 115,000x = 105,000x + 40,000 Solving for x gives: 115,000x - 105,000x = 40,000 10,000x = 40,000 Therefore, x = 40,000/10,000 = 4.

54 54 Example: Ponderosa Development Corp.  Question: What is the monthly profit if 12 houses per month are built and sold?  ***** Simply plug 12 into the profit formula and solve p (12) = 10,000(12) - 40,000 = $80,000 monthly profit

55 55 Example: Ponderosa Development Corp.  Graph of Break-Even Analysis 0 200 400 600 800 1000 1200 012345678910 Number of Houses Sold (x) Thousands of Dollars Break-Even Point = 4 Houses Total Cost = 40,000 + 105,000x Total Revenue = 115,000x

56 56 More Examples of Optimization Problems: A Product Mix Problem  Woody's Furniture Company makes chairs, tables, and desks. Chairs are made entirely out of pine, and use 8 linear feet of pine per chair. Tables and desks are made of pine and mahogany, tables using 12 linear feet of pine and 15 linear feet of mahogany, and desks use 16 linear feet of pine and 20 linear feet of mahogany. Chairs require 3 hours of labor to produce, tables 6 hours, and desks 9 hours. Chairs provide $35 profit, tables $60 profit, and desks $75 profit.  Woody has 120 linear feet of pine and 60 linear feet of mahogany delivered each day, and has a work force of 6 carpenters, each of whom puts in an 8-hour day. How can Woody make the best use of these resources?

57 57 Tabular description of Woody’s problem Amount of resource/profit perResource Available ChairTableDesk Pine81216120 Mahogany0152060 Man-hours36948 Profit356075

58 58 A Diet Problem  The minimum daily requirements of protein, carbohydrates, and iron in the diet are 500, 400, and 75 respectively, and the cost of beef, potatoes, and spinach are $0.35, $0.20, and $0.15 per ounce, respectively.  How should the dietician plan her menu? Food Nutrient ProteinCarbsIron Beef20105 Potatoes10306 Spinach6720 A dietician wishes to plan a meal using ground beef, potatoes, and spinach which satisfies minimum daily requirements of protein, carbohydrates, and iron. The nutritional makeup of each ounce of foodstuff (in the appropriate nutritional units) is given below.

59 59 Tabular description of the diet problem Amount of resource/cost per ounce of Demand BeefPotatoesSpinach Protein20106500 Carbs10307400 Iron562075 Cost$0.35$0.20$0.15

60 60 An Assignment Problem  The OR IDEAS Consulting Company has six crack OR analysts that it wants to assign to six projects it has contracted. The specialized skills of each analyst has been carefully evaluated with respect to each job to determine the amount of profit the company can expect to earn if the analyst is assigned to that particular job. The results are given in the table below: Project 123456 Anderson1050090008000100001200011500 Carlucci1200080007000110001150011000 Nataraja100009000600090001200010500 Chin12000110009000 1000011000 Yohana950070008000 90007000 Yamanaka125001200010000 1100014000 ♦ How should OR IDEAS put its analysts to best use?

61 61 A Transportation Problem  After a week of renting cars that travel all across the country, Avis finds that it has a shortage of cars in Los Angeles, San Diego, Pittsburgh, New York, Atlanta, and Chicago, while it has a surplus of cars in San Francisco, Denver, Miami, and Houston. There is a certain cost of shipping cars from each supply point to each demand point, as given in the chart in the following table: OutletSurplus LASDPghNYAtlChi SF54191617136 Denver479784250 Miami78171091220 Houston571081213 Shortage416109716  How can Avis return the cars to the correct locations?


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