1. Session 5b

2. Decision Models -- Prof. Juran2 Overview Evolutionary Solver (Genetic Algorithm) Advertising Example Product Design Example –Conjoint Analysis

3. Decision Models -- Prof. Juran3 Nonlinear Problems Some nonlinear problems can be formulated in a linear fashion (i.e. some network problems). Other nonlinear functions can be solved with our basic methods (i.e. smooth, continuous functions that are concave or convex, such as portfolio variances). However, there are many types of nonlinear problems that pose significant difficulties.

4. Decision Models -- Prof. Juran4 Nonlinear Problems The linear solution to a nonlinear (say, integer) problem may be infeasible. The linear solution may be far away from the actual optimal solution. Some functions have many local minima (or maxima), and Solver is not guaranteed to find the global minimum (or maximum).

5. Decision Models -- Prof. Juran5 3 Solvers Simplex LP Solver GRG Nonlinear Solver Evolutionary Solver

6. Decision Models -- Prof. Juran6 Radio Advertising Example Music radio WABC has commercials of the following lengths (in seconds): 15, 15, 20, 25, 30, 35, 40, 57 The commercials must be assigned to 60-second breaks. What is the fewest number of breaks that are needed to air all of the commercials?

7. Decision Models -- Prof. Juran7 Managerial Problem Definition Decision Variables Which commercials get assigned to which programming breaks. Objective Minimize the total number of breaks. Constraints Every advertisement must be aired. No break can be longer than 60 seconds.

8. Decision Models -- Prof. Juran8 Formulation Decision Variables Define x i to be an integer variable identifying the break to which commercial i is assigned. For example, if commercial 1 is assigned to break 4, then x 1 = 4. It should be clear that we won’t need any more than eight breaks, because there are only eight commercials. These eight x variables are the decision variables.

9. Decision Models -- Prof. Juran9 Define y j to be a binary variable, such that y j = 0 if no commercials are assigned to break j, and y j = 1 if any commercials are assigned to break j. Define v ij to be a binary variable such that v ij = 1 if commercial i is assigned to break j, and v ij = 0 otherwise. Define w i to be the duration of commercial i. Formulation

10. Decision Models -- Prof. Juran10 Formulation Note that the duration of break j is equal to Let t j be the amount of “overtime” in break j. That is,

11. Decision Models -- Prof. Juran11 Formulation Objective Our objective, then, is to: Minimize Z = where m is a “large number”.

12. Decision Models -- Prof. Juran12 Formulation This is a good example of an advanced optimization trick: taking a constraint and building it into the objective function. It doesn’t really matter what value we use for m, as long as it is sufficiently large as to prevent any t j > 0. As it happens, in this problem m = 100 works fine.

13. Decision Models -- Prof. Juran13 Formulation Constraints For all x i, 1≤ x i ≤ 8. For all x i, x i is an integer. For all y j, y j is binary.

14. Decision Models -- Prof. Juran14 Solution Methodology

15. Decision Models -- Prof. Juran15 Solution Methodology The objective function is in B25, including a penalty of 100 units per second if any breaks go over 60 seconds. The decision variables ( x i ) are in the range E5:E12 (in the spreadsheet shown, all commercials are assigned to break 1, so x i = 1 for all i ). The range B5:B12 contains the durations of each commercial ( w i ), and the range B16:B23 uses the Excel SUMIF function to calculate the duration of each commercial break.

16. Decision Models -- Prof. Juran16 Solution Methodology The range C16:C23 keeps track of which breaks have any assignments (the y i variables), while the range D16:D23 keeps track of how much the breaks go over the maximum limit (the t j variables). Recall that the y i and t j variables are the basic ingredients of the productive function. Notice how the use of the IF function in C16:C23 precludes the need to have an explicit binary constraint in Solver for the y i variables.

17. Decision Models -- Prof. Juran17 Solution Methodology The standard simplex algorithm (Solver’s default method) won’t work on this problem. The GRG Nonlinear algorithm will make an honest effort, but is likely to give up without finding the optimal solution. This is because of our use of MAX, IF, and SUMIF functions, resulting in discontinuities in our productive function and constraints as functions of the decision variables. However, the Evolutionary Solver, a genetic algorithm, can do a good job with a problem like this.

18. Decision Models -- Prof. Juran18

19. Decision Models -- Prof. Juran19 Solution Methodology The Evolutionary Solver operates in a completely different way from the other types. Instead of searching in a structured way guaranteed to reach the optimal solution, genetic algorithms operate somewhat like biological evolutionary processes, with some degree of randomness in the steps taken from one solution to the next. In a finite period of time, the Evolutionary Solver is not guaranteed to find the optimal solution, but it will find very good solutions and try to improve upon them.

20. Decision Models -- Prof. Juran20 Optimal Solution

21. Decision Models -- Prof. Juran21 Conclusions The solution indicates that commercials 1, 2, and 5 should go in one break, 3 and 7 should go in another, 4 and 6 should go in another, and 8 should go by itself. A reasonably bright person could solve this problem in their head, of course. The trick here was to set it up so that a computer could solve it, providing a method for the solution of much larger problems with the same basic structure.

22. Decision Models -- Prof. Juran22 Product Design Example Conjoint Analysis is a multivariate technique used specifically to understand how respondents develop preferences for products or services. It is based on the simple premise that consumers evaluate the value or utility of a product (real or hypothetical) by combining the utility provided by each attribute characterizing the product. -- Prof. Pradeep Chintagunta, Univ. of Chicago

23. Decision Models -- Prof. Juran23 Conjoint Analysis Conjoint Analysis is a decompositional method. Respondents provide overall evaluations of products that are presented to them as combinations of attributes. These evaluations are then used to infer the utilities of the individual attributes comprising the products. In many situations, this is preferable to asking respondents how important certain attributes are, or to rate how well a product performs on each of a number of attributes.

24. Decision Models -- Prof. Juran24 Conjoint Analysis After determining the contribution of each attribute to the consumer’s overall evaluation, one could 1.Define the product with the optimal combination of features 2.Predict market shares of different products with different sets of features 3.Isolate groups of customers who place differing importances on different features 4.Identify marketing opportunities by exploring the market potential for feature combinations not currently available 5.Show the relative contributions of each attribute and each level to the overall evaluation of the product

25. Decision Models -- Prof. Juran25 Product Design Example

26. Decision Models -- Prof. Juran26 Product Design Example Assume that a consumer's purchase decision on an electric razor is based on four attributes, each of which can be set at one of three levels (1, 2, or 3). Using conjoint analysis, our analysts have divided the market into five segments (labeled as customers 1, 2, 3, 4, and 5) and have determined the "part- worth" that each customer gives to each level of each attribute.

27. Decision Models -- Prof. Juran27 We assume here that all customers within a particular segment view electric razors more or less the same in terms of which levels of which attributes constitute an attractive product. We also assume that customers in a segment conduct a sort of mathematical analysis (perhaps unconsciously) in which they weigh the various attributes of a product to come up with an overall value with respect to competing products. Conjoint analysis usually assumes the customer buys the product yielding the highest total part-worth.

28. Decision Models -- Prof. Juran28

29. Decision Models -- Prof. Juran29 For example, consider Segment 1 and these two products: We assume that customers in Segment 1 will not buy Product B, because they value Product A at 1 + 4 + 4 + 4 = 13 and Product B at 1 + 1 + 1 + 2 = 5.

30. Decision Models -- Prof. Juran30

31. Decision Models -- Prof. Juran31 Currently there is a single product in the market that sets all four attributes equal to 1 (call it Razor 0). We want to introduce two new types of electric razors, and capture as much of the market as possible. We want to design a two-product line that maximizes the number of market segments that will buy one of our two products. Assume that in the case of a tie, the consumer does not purchase our product.

32. Decision Models -- Prof. Juran32 Managerial Formulation Decision Variables Which levels of each attribute to design into each of our two products. Objective Maximize the number of customer segments who will buy one of our products. Constraints There are only four attributes, each of which must be assigned to one of three existing levels for each product. (In other words, no product can have more or less than one level per attribute.)

33. Decision Models -- Prof. Juran33 Formulation: Preliminaries There are five customer segments, and we will index them from 1 to 5 with the subscript letter i. There are three products (the existing Razor 0, plus our Razors 1 and 2 to be designed), and we will index them from 1 to 3 with the subscript letter j. There are four product attributes, and we will index them from 1 to 4 with the subscript letter k. There are three possible levels for each attribute, and we will index them from 1 to 3 with the subscript letter l.

34. Decision Models -- Prof. Juran34 Symbol Variable Description ij x A binary variable; 1 if segment i will buy product j, 0 otherwise. ij v The total “value” that segment i places on product j. For our two products ( j = 1, 2), 1  ij x if ji ij vv not products  jkl  A binary variable; 1 if product j has level l of attribute k, 0 otherwise. There are 12 of these per product; 36 total in this problem. ikl  The “value” placed by segment i on level l of attribute k. There are 12 of these per segment; 60 total for this problem (as shown in the table onslide 27).

35. Decision Models -- Prof. Juran35 Example: Using the example on slide 28, consider Segment 1’s evaluation of Razors A and B. For Razor A:

36. Decision Models -- Prof. Juran36 For Razor B: Since,, and. In English, customer segment 1 will buy Razor A and not buy Razor B.

37. Decision Models -- Prof. Juran37 Formulation

38. Decision Models -- Prof. Juran38 Solution Methodology

39. Decision Models -- Prof. Juran39 Solution Methodology

40. Decision Models -- Prof. Juran40

41. Decision Models -- Prof. Juran41 Optimal Solution

42. Decision Models -- Prof. Juran42 Conclusions It turns out that there is a line of two products that can capture all five segments! Razor 1, with attribute levels (1, 1, 3, 1), captures segments 4 and 5. Razor 2, with attribute levels (3, 1, 1, 1), captures segments 1, 2, and 3.

43. Decision Models -- Prof. Juran43 Summary Evolutionary Solver (Genetic Algorithm) Advertising Example –Integer and Binary tricks –Moving Constraints into the productive Function –MAX, IF, SUMIF Product Design Example –Conjoint Analysis –VLOOKUP, MAX, IF