Kilgore's Deli is a small delicatessen located near a major university. Kilgore does a large walk-in carry-out lunch business. The deli offers three luncheon chili specials, Wimpy, Dial 911, Fire Bowl. At the beginning of the day, Kilgore needs to decide how much of each special to make (he always sells out of whatever he makes). The profit on one serving of Wimpy is $0.6, on one serving of Dial 911 is $0.55, and on one serving of Fire Bowl is $0.65.
The following table shows the required ingredients to make one serving of each chili special, and the available amount of each ingredient for today :
Ingredient Required ingredient for each serving of Wimpy Required ingredient for each serving of Dial 911 Required ingredient for each serving of Fire Bowl Available Amount
Beef (lbs) 1 1 1.5 15
Onions (cup) 2 2 2 20
Special Sauce (oz) 5 2 3 90
Hot Sauce (oz) 0 5 6 60
Based on the historical sales data, Kilgore's Deli has the following considerations:
the number of servings for Fire Bowl must be at least 10% of the total number of servings for all three luncheon chili specials.
the number of servings for Fire Bowl, however, cannot exceed the number of Dial 911 by more than 3.
the available number of servings for Dial 911 must be at least 2.
(1.1) Develop the linear programming model to help determine the number of servings of Wimpy, Dial 911, and Fire Bowl to make in order to maximize his profit today. Type your algebraic formulation in the specified area in Q1 tab of the Excel template. Note you will not get full credits without including the algebraic formulation.
(1.2) Solve your LP model in Excel and fill in the following blanks (show your work in Q1 tab):
The optimal number of servings of Wimpy is
The optimal number of servings of Dial 911 is
The optimal number of servings of Fire Bowl is
The maximum possible profit is
Is there any leftover of ingredients as a result of this optimal solution? if yes, enter the amount. Otherwise, just type 0.
Amount of beef leftover
Amount of onion leftover
Amount of special sauce leftover
Amount of hot sauce leftover

z (max)  =  6.15

x₁  =  1       x₂ =  3    x₃  =  6

Amount of beef leftover     2 lb

Amount of onions leftover   0

Amount of  Special Sauce leftover 61

Amount of Hot Sauce leftover   9

Step-by-step explanation:

Ingredients              Beef      Onions    Special S   Hot S      Profit  

Wimpy  (x₁)                   1              2                5              0             0.6

Dial 911 (x₂)                   1              2                2              5             0.55

Fire Bowl (x₃)                1.5           2                3              6             0.65

Available                       15            20             90             60        

Objective Function z:

z  =  0.6*x₁  +  0.55*x₂  +  0.65*x₃      to maximize

Subject to:

1) Quantity of beef : 15

x₁  +  x₂  + 1.5*x₃  ≤  15

2) Quantity of onions:  20

2*x₁  +  2*x₂  +  2*x₃  ≤  20

3) Quantity of Special sauce: 90

5*x₁  + 2*x₂  + 3*x₃  ≤  90

4) Quantity of hot sauce:  60

0*x₁   + 5*x₂  + 6*x₃  ≤  60

5) Condition: The number of servings for Fire Bowl must be at least 10% of the total number of servings for all three luncheon chili specials.

x₃  ≥  0.1 ( x₁  +  x₂   +  x₃ )     or    x₃    ≥   0.1*x₁  +  0.1 *x₂  + 0.1*x₃

x₃   -   0.1*x₁  -  0.1 *x₂  - 0.1*x₃   ≥  0

-   0.1*x₁  -  0.1 *x₂   +    0.9 *x₃   ≥  0

6)Condition: The number of servings for Fire Bowl, however, cannot exceed the number of Dial 911 by more than 3.

x₃  -  x₂  ≤  3

7)the available number of servings for Dial 911 must be at least 2.

x₂  ≥  2

General constraints:

x₁  ≥   0           x₃   ≥ 0    all integers

With on-line solver solution  is:

z (max)  =  6.15

x₁  =  1       x₂ =  3    x₃  =  6

By sbstitution on the constraints

1)   x₁  +  x₂  + 1.5*x₃  ≤  15               1 + 3 + 9  = 13

Amount of beef leftover     2 lb

2)  2*x₁  +  2*x₂  +  2*x₃  ≤  20          2 + 6  + 12 = 20

Amount of onions leftover   0

3) 5*x₁  + 2*x₂  + 3*x₃  ≤  90             5  + 6  + 18 = 29

Amount of  Special Sauce leftover 61

4)0*x₁   + 5*x₂  + 6*x₃  ≤  60             15  +  36  = 51

Amount of Hot Sauce leftover   9

repeat the question

step-by-step explanation:

a significant challenge in working with any kind of data is how to present this data to answer questions of interest to human observers. this challenge is especially critical for big data streams because of the volume, variety, or velocity of the data involved. data of large volume has the potential to overwhelm an observers ability to extract meaning from the data. how should the voting data for every us citizen be portrayed to be of interest to political scientists? how should all of the genome data for a species be represented to answer interesting biological questions? how can all of the works of romantic poets be presented for literary analysis? data of highly diverse variety is a challenge because the data forms of data might best be conveyed by different means. integrating different representations in a meaningful way is difficult. for example health-related information contains family history, genomic data, medical test results, descriptions of exercise patterns, records of diet and nutrition, list of stress factors. how can all of this health-related information even for a single individual be displayed for best use by doctors? data with high velocity is changing so rapidly that interesting or significant aspects of the data might be lost. for example, how can stock market data be depicted so that meaningful trends can be seen by a stock analyst? how can data on the movement of vehicles in an urban road system be exhibited so that traffic engineers can determine better ways to avoid traffic jams?

data visualization is often a useful way to present complex data. the adage that “a picture is worth a thousand words” reflects the power of the human visual system. presenting data in a visual form leverages the ability of our visual systems to us see patterns or to detect special cases of interest that are distinctive in some way. finding a good visualization is, of course, not always easy. the picture game “where’s waldo” shows how easy it is to create a visual representation that hides the feature of interest.

there are a rich variety of ways to visualize data in both two and three dimensions. for example, here is a gallerylinks to an external site. of visualization that can be produced by the tools we will be using. developing the knowledge and skills to build intricate and compelling visualizations is a study on its own. however, some basic forms of visualization are very useful for a number of “first step” explorations of big data. these basic forms are:

line graphs: showing how data changes over time (e.g. how the value of a stock changes over the trading days).

histograms: showing the distribution over a range (e.g., how many earthquakes are there in a month in each of the six units on the richter scale).

scatter plots: showing the relationship between two different factors (e.g., how the temperature and humidity for a given city are related),

bar charts: showing the comparative values in certain categories (e.g., display the average forecast temperature for new york, ny, blacksburg, va, and seattle,  

map plots: showing the distribution over geographic space (e.g., where on the globe have earthquakes in the last month occurred).

you can also think of visualizations that you see every day as other examples of how data is presented: weather maps, hurricane tracks, election maps.

we will be using matplotlib to illustrate visualization of data. matplotlib is widely used and offers the capability to generate many different kinds of visualization in python. we saw examples of its variety of visualization in the matplotlib gallery above. however, this utility comes at a price. matplotlib is complicated. fortunately, matplotlib is designed so that many common cases can be done with minimal or reasonable effort. in addition to matplotlib we will also look at a library for geographical plotting that is built on top of matplotlib.

step-by-step explanation: