Tag Archives: teaching

Improving a Homework Problem from Ragsdale’s Textbook

UPDATE (1/15/2013): Cliff Ragsdale was kind enough to include the modification I describe below in the 7th edition of his book (it’s now problem 32 in Chapter 6). He even named a character after me! Thanks, Cliff!

When I teach the OR class to MBA students, I adopt Cliff Ragsdale’s textbook entitled “Spreadsheet Modeling and Decision Analysis“, which is now in its sixth edition. I like this book and I’m used to teaching with it. In addition, it has a large and diverse collection of interesting exercises/problems that I use both as homework problems and as inspiration for exam questions.

One of my favorite problems to assign as homework is problem number 30 in the Integer Linear Programming chapter (Chapter 6). (This number refers to the 6th edition of the book; in the 5th edition it’s problem number 29, and in the 4th edition it’s problem number 26.) Here’s the statement:

The emergency services coordinator of Clarke County is interested in locating the county’s two ambulances to maximize the number of residents that can be reached within four minutes in emergency situations. The county is divided into five regions, and the average times required to travel from one region to the next are summarized in the following table:

The population in regions 1, 2, 3, 4, and 5 are estimated as 45,000,  65,000,  28,000,  52,000, and 43,000, respectively. In which two regions should the ambulances be placed?

I love this problem. It exercises important concepts and unearths many misconceptions. It’s challenging, but not impossible, and it forces students to think about connecting distinct—albeit related—sets of variables; a common omission in models created by novice modelers. BUT, in its present form, in my humble opinion, it falls short of the masterpiece it can be. There are two main issues with the current version of this problem (think about it for a while and you’ll see what I mean):

  1. It’s easy for students to eyeball an optimal solution. So they come back to my office and say: “I don’t know what the point of this problem is; the answer is obviously equal to …” Many of them don’t even try to create a math model.
  2. Even if you model it incorrectly, that is, by choosing the wrong variables which will end up double-counting the number of people covered by the ambulances, the solution that you get is still equal to the correct solution. So when I take points off for the incorrect model, the students come back and say “But I got the right answer!”

After a few years of facing these issues, I decided I had had enough. So I changed the problem data to achieve the following (“evil”) goals:

  1. It’s not as easy to eyeball an optimal solution as it was before.
  2. If you write a model assuming every region has to be covered (which is not a requirement to begin with), you’ll get an infeasible model. In the original case, this doesn’t happen. I didn’t like that because this isn’t an explicit assumption and many students would add it in.
  3. If you pick the wrong set of variables and double-count the number of people covered, you’ll end up with an incorrect (sub-optimal) solution.

These improvements are obtained by adding a sixth region, changing the table of distances, and changing the population numbers as follows:

The new population numbers (in 1000’s) for regions 1 through 6 are, respectively, 21, 35, 15, 60, 20, and 37.

I am now much happier with this problem and my students are getting a lot more out of it (I think). At least I can tell you one thing: they’re spending a lot more time thinking about it and asking me intelligent questions. Isn’t that the whole purpose of homework? Maybe they hate me a bit more now, but I don’t mind practicing some tough love.

Feel free to use my modification if you wish. I’d love to see it included in the 7th edition of Cliff’s book.

Note to instructors: if you want to have the solution to the new version of the problem, including the Excel model, just drop me a line: tallys at miami dot edu.

Note to students: to preserve the usefulness of this problem, I cannot provide you with the solution, but if you become an MBA student at the University of Miami, I’ll give you some hints.


Filed under Books, Integer Programming, Modeling, Teaching

Choosing Summer Camps for Your Kids

Today I’m going to write about a decision that’s made by many American families each year: how to pick summer camps for our kids. There are several issues to take into account, such as cost, benefit, hours, and kids’ preferences. I’ll introduce an optimization model for summer camp selection through a numerical example. The example portrays a large family, but the same ideas apply if a few smaller families want to get together and solve this problem. This way they can take advantage of the discounts and take turns driving the kids around.

The Joneses have six kids: Amy, Beth, Cathy, David, Earl and Fred (yes, their first names are alphabetically sorted, matching their increasing order of age; Mr. and Mrs. Jones always knew they’d have six kids and hence named their firstborn with an ‘F’ name). This year, they’ve narrowed down their list of potential summer camps to the following ten: Math, Chess, Nature, Crafts, Cooking, Gymnastics, Soccer, Tennis, Diving, and Fishing. The Nature camp takes kids on a hike through the woods with the guidance of a biologist; they make frequent stops upon encountering specific plants and animals, during which a mini science lecture is delivered (pretty cool!). The Cooking camp involves cooking chemistry instruction, à la Alton Brown (also pretty cool).

The following table contains some data related to each camp:

The Cost column indicates the cost per child. The Discount column indicates the percentage discount that each child enrolled after the first would receive on the cost of each camp. For instance, if three children are enrolled in Math camp, the first would cost $1100, and the second and third would cost $770 each (30% less). The Hours column is self-explanatory and the last two columns indicate whether or not that particular camp develops mental and physical abilities, respectively (a value of one = yes, zero=no).

The next table shows some of the child-specific requirements:

For example, the Joneses want Fred to attend at least 3 camps that develop mental abilities, and at least 1 camp that develops physical abilities. The last two columns in the above table indicate the minimum and maximum number of camp hours for each child over the 9-week summer break.

The next thing parents need to take into account are their children’s preferences. So here they are:

The smaller the number in the above table, the more desirable a particular camp is. For example, Amy is a bit of a math nerd, and if we were to flip David’s preference scores for Math and Tennis, he could be classified as a bit of a jock. Some conflicts exist, in the sense that not all camps are compatible with each other in terms of time schedules. In this particular case, let’s assume that no child can attend both the Soccer and Tennis camps, or both the Nature and Soccer camps. Here’s how we are going to use this preference table to create a sense of fairness among the children: whenever a child that prefers camp X to camp Y goes to camp Y and doesn’t go to camp X, nobody else gets to go to camp X either. For example, if Amy goes to Nature camp and isn’t sent to either Math or Chess camp, none of her siblings are allowed to go to Math or Chess either. Conversely, if the Joneses decide to send Earl to Chess camp and Fred to Tennis camp, they must also send Earl to Tennis camp (because Earl prefers Tennis to Chess, and “Fred is going! Why can’t I go too!”). Clearly, there are other ways to use/interpret this table, such as trying to send everyone to at least one of their top N choices, but we won’t consider those alternatives here.

After taking all of the above issues and conditions into account, here’s a solution that satisfies all the requirements while resulting in the minimum cost of $22,180.00 (You guessed it…the Joneses are probably *not* among the 99%):

Amy goes to Math, Crafts, Cooking, and Tennis; Beth goes to Math, Cooking, Tennis, and Fishing; Cathy goes to Math, Crafts, Tennis, and Fishing; David goes to Math, Cooking, and Fishing; Earl goes to Math, Tennis, and Fishing; and Fred goes to Math, Crafts, Cooking, and Tennis. Mmm…interestingly, everyone goes to Math camp. I think the Joneses are on to something…

Depending on your own requirements, preferences, and costs your solution may differ, of course. But this should give you an idea of how this simple problem can easily become very complicated to solve. No need to fear, though! Operations Research is here!

Food for Thought: Here’s an interesting question that helps illustrate how high-quality solutions can be counterintuitive: by looking at the preferences table, we see that everyone prefers Soccer to Tennis. In addition, Soccer camp is less expensive than Tennis camp. So how come we send almost everyone to Tennis camp? Isn’t that strange? Let me know what you think in the comments below! That’s one of the advantages of using an analytical approach to decision making: it helps us find solutions we wouldn’t even consider otherwise because they don’t seem to make sense (at least not at first).

If you’re curious about how I managed to find the optimal solution, read on!

Details of the Analysis:

To find the minimum-cost solution, we can create a mathematical representation of the problem, a.k.a. a model, and then solve this model with the help of a computer. Let’s see how.

The first obvious decision to make is who goes where. So let the binary variable x_{ij} equal 1 when child i goes to camp j, and equal to 0 otherwise. We’ll also need another binary variable y_j that is equal to 1 when at least one child goes to camp j and equal to 0 when none of the children go to camp j. We are now ready to write our objective function and constraints. I’ll refer to the problem data using the column headings of the tables above. The subscript i will always refer to a child, and the subscript j will always refer to a camp.

To minimize the total cost, we write the following objective function:

\displaystyle \min \sum_i \sum_j (1-\mathrm{Discount}_j)\mathrm{Cost}_j x_{ij} + \sum_j \mathrm{Discount}_j \mathrm{Cost}_j y_j

Note how we are using the y_j variable to handle the discount for sending more than one child to camp j: we charge every child the discounted price in the double summation and add the discount back in only once if y_j=1.

Now we have to deal with the four requirements: minimum and maximum hours, mental activity, and physical activity. For every child i, we have to write the following four constraints:

\displaystyle \sum_j \mathrm{Hours}_j x_{ij} \geq \mathrm{MinTimeReq}_i

\displaystyle \sum_j \mathrm{Hours}_j x_{ij} \leq \mathrm{MaxTimeReq}_i

\displaystyle \sum_j \mathrm{IsMental}_j x_{ij} \geq \mathrm{MentalReq}_i

\displaystyle \sum_j \mathrm{IsPhysical}_j x_{ij} \geq \mathrm{PhysicalReq}_i

Next, we enforce the preference rules. Let’s recall the example involving Earl and Fred: if Earl goes to Chess camp and someone else (it doesn’t matter who) goes to Tennis camp, then Earl has to go to Tennis camp as well. Here’s what this constraint would look like:

x_{\mathrm{Earl},\mathrm{Chess}} + y_{\mathrm{Tennis}} - x_{\mathrm{Earl},\mathrm{Tennis}} \leq 1

Of course, we have to repeat this constraint for every child i and every pair of camps j_1 and j_2 such that child i prefers j_1 to j_2 in the following way:

x_{ij_2} + y_{j_1} - x_{ij_1} \leq 1

The camp compatibility constraints say that no child i can attend both Soccer and Tennis, or both Nature and Soccer, therefore:

x_{i,\mathrm{Soccer}} + x_{i,\mathrm{Tennis}} \leq 1

x_{i,\mathrm{Nature}} + x_{i,\mathrm{Soccer}} \leq 1

Finally, we need to relate the x_{ij} and y_j variables by stating that unless y_j=1 , no x_{ij} can be equal to 1 . So we write the following constraint for all values of i and j :

x_{ij} \leq y_j

And that’s the end of our model. Here’s a representation of this mathematical model in AMPL in case you want to play with it yourself. This is the model I used to obtain the numerical results reported above. Enjoy!


Filed under Applications, INFORMS Monthly Blog Challenge, Integer Programming, Mathematical Programming, Modeling, Motivation, Promoting OR, Summer camp

Big Bang Theory Party Planning

Penny is organizing a party at her apartment, but she is on a tight budget. Having a working knowledge of all of the important things in the universe, Sheldon knows everything about linear programming and offered to help her. He postulates that it’s ideal to have two kinds of mixed nuts: a plain party mix, and a luxury mix (for those with a distinct taste like himself). Based on the expected number of guests, Howard quickly calculates that they’ll need a total of at least 10 pounds of snacks, but no more than 6 pounds of each kind of mix. On his white board, Sheldon has already come up with the following table:

Raj wants to dip the hazelnuts into liquor, but that’s not in the budget, so he gives up. Leonard reminds everyone that, because of their allergies, it’s important to keep the average allergenicity level per pound in both mixes to no more than 3. Write an optimization model to help Penny prepare the two kinds of snacks at minimum cost. But be careful: Sheldon will check it later for correctness!

This post is part of my series “Having Fun with Exam Questions”. Previous questions dealt with Farmville, vampires, and (potentially) Valentine’s day.

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Filed under Big Bang Theory, Exam Fun, Linear Programming, Modeling, Teaching

Find Out What Happens to Mr. Lovr

I’ve had a number of people tell me that they like my Valentine’s Day post, but many of them did not solve the Excel Spreadsheet. That’s the most important part of the post! You have to see what happens to Mr. Lovr! There’s no set-up necessary; just open Excel Solver (it’s an add-in you have to enable in Windows and a separate program in the Mac), and click on the “Solve” button. You’ll be glad you did.

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Filed under INFORMS Monthly Blog Challenge, Love, Valentine's Day

Transporting Flowers with Love

Mr. Lovr, a lonely gentleman, does not want to spend Valentine’s day alone in 2011. As one of his New Year’s resolutions, he intends to send roses to nine of his lady friends. Being an avid procrastinator, however, he waits until the last minute and finds out that only eight flower shops in his city still have roses available. For lack of better names, let’s call those flower shops F1, F2, F3, …, F8. The number of bouquets of roses available at each shop are, respectively, 4, 4, 3, 2, 2, 2, 2, and 1. Based on how well he knows each of his potential valentines, whom we’re going to call V1, V2, V3, …, V9, he calculates how many bouquets he needs to send to each of them to increase his chances of going on at least one date. The numbers are, respectively, 3, 2, 2, 2, 2, 2, 2, 2, and 3. At this point, a light bulb goes off in Mr. Lovr’s head, and he remembers from his Operations Research class that this is a transportation problem. But there’s something missing…Ah! The costs! He calls each of the eight flower shops and asks how much it would cost to ship one bouquet of roses to the addresses of each of his nine lady friends. He then compiles the following table of costs (in dollars):

He also remembers that because the total supply is equal to the total demand, he can write all of the constraints in this problem as equalities. Essentially, he has to say that, for each flower shop, the number of bouquets that it ships has to be equal to the number of bouquets that it has. Similarly, he needs one constraint for each valentine saying that the number of bouquets that they receive has to be equal to the number that they want (according to his estimates above). To avoid suspicion, he also decides that it’s better for each flower shop to send no more than one bouquet to the same person. So far, so good, but he needs a specific shipment plan because he’s running out of time.

He opens up an Excel spreadsheet and creates the following layout of cells (he chose pink to make it more romantic):

Somewhere else in his spreadsheet he also typed the table of costs shown above. To his surprise, he even remembered to use the SUMPRODUCT function to calculate the total cost expression. He clicks “Solve” and finds out that the cheapest way to send all 20 bouquets will cost him $38. Not bad…but wait a second…something amazing happened! He cannot believe his eyes! The optimal solution exhibits a very curious pattern! Could it be a Valentine’s Day miracle? Could it be the power of love? If you want to see for yourself, download Mr. Lovr’s spreadsheet, open Excel Solver, and solve the model (no setup necessary, just click the “Solve” button). (NOTE: this spreadsheet has been tested with Excel 2007 under Windows XP, and with Excel 2008 under Mac OS X Snow Leopard. I’m not sure the “trick” will work in earlier Excel versions. For a free download of Excel Solver for Mac OS X, go here.)

Thanks to my love for giving me the idea for this blog post.


Filed under Exam Fun, INFORMS Monthly Blog Challenge, Love, Valentine's Day

Ten Freshmen + 46 Slides = 1 Hour of OR Fun

I just finished my presentation to business undergraduate students and, from what I could tell by looking at them, I think it was successful. Of course the real test will be whether someone stops by my office saying “I love OR! Can I work with you?”. I want to thank our vice dean for this opportunity and I am looking forward to doing it again next year.

I closed the presentation with a little “quiz” based on a very nice paper by Brown, Klein, Rosenthal and Washburn entitled Steaming on Convex Hulls. Here’s how it goes (you can open the image on a new window to make it larger):

An aircraft carrier can run with 2 or 4 engines online. The graph below shows gallons of gasoline used per hour versus possible speeds for each engine configuration. How would you run the ship to cover 100 miles in 4 hours?

According to the article, the Navy spends over 1 billion dollars a year on surface combatants alone. An officer who became a ship commander after graduating from the academy was smart enough to solve the above problem the right way. His ship was saving so much fuel that it had to be inspected under the suspicion that it was violating safety regulations. But we all know it wasn’t. It was just a case of using analytical techniques to make better decisions.


Filed under Applications, Linear Programming, Motivation, Promoting OR

Getting Freshmen Excited About OR

The vice dean for undergraduate programs at the school of business asked me to make a presentation to a group of freshmen. My job is to tell them about the field of Management Science and the research that goes on in my department. The main goal is to get these students excited about research early on. Hopefully, they’ll get involved in undergraduate research projects and even consider joining our PhD program further down the road. My understanding is that every department in the school will make a similar presentation, but I’ll tell the students that OR is by far “the coolest topic” (sorry “other departments”, but I think it is!).

I think this is a great idea, especially because Management Science (or OR) is not a required class for all business majors and I believe that every business school graduate should at least know what OR is and what it can do for you (fortunately, OR is a required class for all MBA students in our school).

I’m putting together a presentation with the following outline:

  1. Introduction (who I am, my background, etc.)
  2. What is Management Science? (that’s where I tell them to use the name OR instead :-)
  3. Real-life applications of OR
  4. Research interests of the Management Science department (with a focus on my interests, at the request of the vice dean)
  5. Research opportunities for undergraduate students

The room is booked for 1 hour and 45 minutes, and I was told I can use as much time as I want. Boy, that’s a lot! For item number 3, I’ll pick a diverse collection of applications covering a wide range of topics. For item 4, I’ll tell them about things my colleagues have worked on, things I’ve done, and things I’m currently doing. Then I’ll move on to item 5 and close the presentation with problems on which I’d like to work with an undergraduate student (nothing that requires advanced OR knowledge, of course). One caveat is that I must tell them that my projects require some knowledge of computer programming and basic understanding of linear and integer programming (which they could get by taking one of our classes or by reading on their own).

I’ve also put together a Google document entitled A Hyperlinked Introduction to the World of Operations Research and Management Science, which I’m going to hand out at the end of the talk.

The purpose of this document is to function as an organized list of links to OR resources and interesting applications that the students can easily navigate to. It contains a superset of the real-world applications I’m going to tell them about, and it’s supposed to complement my talk. I hope this turns out to be useful to other people as well. Feel free to use it and let me know if you have any suggestions for improvement. I’m sure there are many interesting links that I forgot to include there.


Filed under Motivation, Promoting OR, Research

Beautiful and Useful Scale of the Universe

Thanks to my former MBA student Max Marty, I came across this interactive scale of the universe (click on the word “Play” below the video game ad). It’s easy to lose track of how big 10^10 is, and this scale helps to put things in perspective. Next time you’re teaching a course in optimization or constraint satisfaction and you tell your students a certain problem has 10^100 possible solutions, bring this scale up on the screen! (Tip from the creators: it’s better to use the arrow keys rather than the mouse to scroll left and right.)

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Filed under Scale, Teaching

Twilight Network Flow

CAUTION: Spoiler Alert!

Part of a professor’s job is to come up with new exam questions each year. That may be a time-consuming (and sometimes tedious) task. You want the question to be just right: not too easy, not too difficult, and capable of testing whether or not the students understood a given concept. This year, I figured I might as well have some fun while doing it. Inspired by Laura McLay’s insightful (and very popular) post on vampire populations, I decided to create a Twilight-themed network flow question:

Alice is in charge of planning Edward and Bella’s wedding and she ordered 2000 roses to be delivered to three locations as shown in the network below. The Cullen’s house (node 2) needs 1000 roses, Charlie’s house (node 4) needs 800 roses, and Billy’s house (node 7) is supposed to get 200 roses (just to tease Jacob). The numbers next to the arcs of the network represent shipping costs per rose (in cents); they’re proportional to the distance between each node. The roses are coming from two local growers in Forks (nodes 1 and 3). Each of them can supply 1000 roses. Arrows with two heads indicate that shipments can be made in both directions.

Write down the supply and/or demand values next to each node and write a linear programming model to determine the shipment plan that minimizes the total cost of delivering all the roses (include all the necessary constraints). (Note: Alice already knows whether you’re going to get the right answer.)

The second half of the fun is to see if any students react to it. In fact, I got a couple of interesting written comments: “How dare you incorporate Twilight into Management Science?”, and a Harry Potter enthusiast wrote “Team Harry!”, while at the same time substituting the names of Hermione, Harry, Ginny and Malfoy for Alice, Edward, Bella, and Jacob.


Filed under Exam Fun, Linear Programming, Modeling, Network Flows, Teaching

Facebook’s Farmville: What’s the Fastest Way to Get Rich?

One can’t help but notice that a lot of people have been playing Farmville on Facebook lately. Some of my relatives, friends, students and co-workers have taken the plunge. I don’t have time to play Farmville, but I thought it would be interesting to study it. It’s a simple and fun game, easy to explain, and apparently addictive.

You begin the game with a 12 by 12 piece of land. That gives you 144 one by one squares in which to plant crops. To simplify the explanation, I’m going to (1) restrict my attention to the four crops initially available (strawberries, eggplant, wheat, and soybeans); (2) assume crops will be harvested as soon as they are ripe (that is, ignore wilting); (3) disregard the random bonuses and gifts that show up every now and then. Blogger Mark Newheiser has a nice post about Farmville in which he includes a useful table. The cost column includes the cost of plowing the land (15 coins) plus the cost of purchasing the seeds. Because the harvest times of all four crops are multiples of four hours, we can think in terms of 4-hour periods.

Question: What’s the largest amount of cash one can obtain in 6 days? (need to rest on the 7th day).

You initially have 200 coins, one ripe and one half-grown square of strawberries, one ripe and one half-grown square of eggplant. By harvesting the two ripe squares, your fortune grows to 323 coins. I’ll also go ahead and clean (delete) the two half-grown squares so that we can begin with a clean slate. It turns out that the answer to the above question is to plant strawberries only, and as much as you can. Here’s the planting schedule:

Period Strawberry Squares
0 12
1 17
2 24
3 34
4 47
5 66
6 92
7 129

At period 8 (after 32 hours), you’ll have enough money to fill all your 144 squares with strawberries and you just continue to plow, plant and harvest them all every 4 hours. After 6 days (36 four-hour periods), you’ll have 44,913 coins (adjusted to take into account that 4 squares were already plowed at time zero). This solution is not very exciting and, moreover, easy to predict. Taking a second look at the cost table, we can see that the profit per hour of strawberries, eggplant, wheat, and soybeans are 2.5, 1, 0.903, and 1.375, respectively. This fact, combined with the fact that strawberries are the fastest to grow, yields the above result. As one progresses through the game, other crops become available and strawberries’ dominance goes away. However, we can show that things can get very complicated by changing the numbers a little bit. If we increase the cost of strawberries to 31 (including plowing), their profit per hour changes to 1 (worse than soybeans). Here’s the new optimal planting schedule for 6 days, which yields a profit of 15,725 coins:

Period Strawberry Squares Soybean Squares
0 2 8
1 3
6 1 15
7 7 1
8 6 2
9 7
12 5 26
13 8
14 13
15 29
16 25 8
17 2 27
18 57
22 16
23 75
24 69
29 16 59
30 85
35 59

Note that the best course of action now is to mix strawberries and soybeans and, at least to me, the proper way of doing it isn’t so obvious (hence the value of using Operations Research). If you’re now wondering whether there would be a situation in which the best idea is to use three different crops, the answer is yes! If we decrease the cost of eggplant to 19 (including plowing), for instance, the optimal planting schedule results in a profit of 18,157 coins and looks like this:

Period Strawberry Squares Soybean Squares Eggplant Squares
0 17
12 1 37 18
13 1
14 1 1
15 1
16 1
17 2
18 123
24 18
26 1
27 1
28 1
29 3
30 123
35 3

In conclusion, I believe that Farmville is a rich decision-making environment that can be used to illustrate some of the analytical techniques from the field of Operations Research. This simple example shows how complicated things can become, even with only four crops!

One possibility of extending this analysis would be to consider that one isn’t willing to log in every 4 hours or so in order to harvest crops, so here’s another question:

Question: Given a schedule of off-line hours (i.e. not available for harvesting), how much money can you make in 6 days?

Finally, I haven’t actually answered the question that appears in the title of this blog post: what’s the fastest way to obtain X coins? I’ll leave these last two questions as an exercise.

Technical Details:

This section of the post is dedicated to those who want to know more about how this analysis was conducted. I used an integer programming model written in AMPL (I think dynamic programming would work too). My variables are: P_{it} = number of squares planted with crop i in period t , H_{it} = number of squares of crop i harvested in period t , A_t = area in use in period t , M_t = money available in period t . Periods go from 0 to T (in our example, T=36 ). So we are interested in maximizing M_T .

The constraints update the values of A_t and M_t based on what you do in period t . Here they are (I’m omitting the details of boundary conditions):

\displaystyle A_t = A_{t-1} + \sum_{i} P_{it} - \sum_{i} H_{it} \enspace \forall \; t

\displaystyle M_t = M_{t-1} - \sum_{i} c_i P_{it} + \sum_{i} r_i H_{it} \enspace \forall \; t

where c_i and r_i are, respectively, the cost and revenue of crop i . Because we don’t consider wilting, H_{it}=P_{i(t-g_i)} , where g_i is the time it takes crop i to grow and ripen. Therefore, the H_{it} variables can be eliminated. Finally, we require M_t \geq 0 and 0 \leq A_t \leq 144 . Here’s the mixed-integer programming (MIP) model in AMPL in case anyone wants to play with it. When it is optimal to use more than one crop, the MIP can get pretty hard to solve, considering its size.


Filed under Applications, Exam Fun, Facebook, Farmville, Integer Programming, Modeling, Teaching