Excel
| A | B | C | D | E | F | G | H |
---|-----------------|----------|--------|--------|-----------|-------------|---------|----------|---
1 | Loan | 50.000 | Year | Start | Interests | Repayment | Annuity | End |
2 | Interests p.a. | 2% | 1 | 50.000 | -1.250 | -1.750 | -3.000 | 48.250 |
3 | Annuity p.a. | 3.000 | 2 | 48.250 | -1.206 | -1.794 | -3.000 | 46.456 |
4 | Maturity | ?? | 3 | 46.456 | -1.161 | -1.839 | -3.000 | 44.618 |
5 | | | 4 | 44.618 | -1.115 | -1.885 | -3.000 | 42.733 |
| | | | | | | | |
| | | | | | | | |
21 | | | 20 | 8.094 | -202 | -2.798 | -3.000 | 5.297 |
22 | | | 21 | 5.297 | -132 | -2.868 | -3.000 | 2.429 |
23 | | | 22 | 2.429 | -61 | -2.939 | -3.000 | 0 |
The above loan of 50.000 has an interest rate of 2% and an annuity of 3.000.
In the table from C1:H23 the annual development of the remaining loan is displayed.
Based on this helper table I know that the maturity of the loan is 22 years by using the following formula in Cell B4:
B4 = COUNTA(C1:C22)
However, my question is if there is an Excel-Formula that can calculate the maturity in one cell so I do not need the helper table in C1:H23?
Oil Blending
An oil company produces three brands of oil: Regular, Multigrade, and
Supreme. Each brand of oil is composed of one or more of four crude stocks, each having a different lubrication index. The relevant data concerning the crude stocks are as follows.
+-------------+-------------------+------------------+--------------------------+
| Crude Stock | Lubrication Index | Cost (€/barrell) | Supply per day (barrels) |
+-------------+-------------------+------------------+--------------------------+
| 1 | 20 | 7,10 | 1000 |
+-------------+-------------------+------------------+--------------------------+
| 2 | 40 | 8,50 | 1100 |
+-------------+-------------------+------------------+--------------------------+
| 3 | 30 | 7,70 | 1200 |
+-------------+-------------------+------------------+--------------------------+
| 4 | 55 | 9,00 | 1100 |
+-------------+-------------------+------------------+--------------------------+
Each brand of oil must meet a minimum standard for a lubrication index, and each brand
thus sells at a different price. The relevant data concerning the three brands of oil are as
follows.
+------------+---------------------------+---------------+--------------+
| Brand | Minimum Lubrication index | Selling price | Daily demand |
+------------+---------------------------+---------------+--------------+
| Regular | 25 | 8,50 | 2000 |
+------------+---------------------------+---------------+--------------+
| Multigrade | 35 | 9,00 | 1500 |
+------------+---------------------------+---------------+--------------+
| Supreme | 50 | 10,00 | 750 |
+------------+---------------------------+---------------+--------------+
Determine an optimal output plan for a single day, assuming that production can be either
sold or else stored at negligible cost.
The daily demand figures are subject to alternative interpretations. Investigate the
following:
(a) The daily demands represent potential sales. In other words, the model should contain demand ceilings (upper limits). What is the optimal profit?
(b) The daily demands are strict obligations. In other words, the model should contain demand constraints that are met precisely. What is the optimal profit?
(c) The daily demands represent minimum sales commitments, but all output can be sold. In other words, the model should permit production to exceed the daily commitments. What is the optimal profit?
QUESTION
I've been able to construct the following model in Excel and solve it via OpenSolver, but I'm only able to integrate the mix for the Regular Oil.
I'm trying to work my way through the book Optimization Modeling with Spreadsheets by Kenneth R. Baker but I'm stuck with this exercise. While I could transfer the logic from another blending problem I'm not sure how to construct the model for multiple blendings at once.
I modeled the problem as a minimization problem on the cost of the different crude stocks. Using the Lubrication Index data I built the constraint for the R-Lub Index as a linear constraint. So far the answer seems to be right for the Regular Oil. However using this approach I've no idea how to include even the second Multigrade Oil.
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| Decision Variables | | | | | | | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| | C1 | C2 | C3 | C4 | | | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| Inputs | 1000 | 0 | 1000 | 0 | | | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| | | | | | | | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| Objective Function | | | | | | Total | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| Cost | 7,10 € | 8,50 € | 7,70 € | 9,00 € | | 14.800,00 € | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| | | | | | | | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| Constraints | | | | | | LHS | | RHS |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| C1 supply | 1 | | | | | 1000 | <= | 1000 |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| C2 supply | | 1 | | | | 0 | <= | 1100 |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| C3 supply | | | 1 | | | 1000 | <= | 1200 |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| C4 supply | | | | 1 | | 0 | <= | 1100 |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| R- Lub Index | -5 | 15 | 5 | 30 | | 0 | >= | 0 |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| R- Output | 1 | 1 | 1 | 1 | | 2000 | = | 2000 |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| | | | | | | | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| Blending Data | | | | | | | | |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
| R- Lub | 20 | 40 | 30 | 55 | | 25 | >= | 25 |
+--------------------+--------+--------+--------+--------+--+-------------+----+------+
Here is the model with Excel formulars:
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| Decision Variables | | | | | | | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| | C1 | C2 | C3 | C4 | | | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| Inputs | 1000 | 0 | 1000 | 0 | | | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| | | | | | | | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| Objective Function | | | | | | Total | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| Cost | 7,1 | 8,5 | 7,7 | 9 | | =SUMMENPRODUKT(B5:E5;B8:E8) | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| | | | | | | | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| Constraints | | | | | | LHS | | RHS |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| C1 supply | 1 | | | | | =SUMMENPRODUKT($B$5:$E$5;B11:E11) | <= | 1000 |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| C2 supply | | 1 | | | | =SUMMENPRODUKT($B$5:$E$5;B12:E12) | <= | 1100 |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| C3 supply | | | 1 | | | =SUMMENPRODUKT($B$5:$E$5;B13:E13) | <= | 1200 |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| C4 supply | | | | 1 | | =SUMMENPRODUKT($B$5:$E$5;B14:E14) | <= | 1100 |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| R- Lub Index | -5 | 15 | 5 | 30 | | =SUMMENPRODUKT($B$5:$E$5;B15:E15) | >= | 0 |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| R- Output | 1 | 1 | 1 | 1 | | =SUMMENPRODUKT($B$5:$E$5;B16:E16) | = | 2000 |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| | | | | | | | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| Blending Data | | | | | | | | |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
| R- Lub | 20 | 40 | 30 | 55 | | =SUMMENPRODUKT($B$5:$E$5;B19:E19)/SUMME($B$5:$E$5) | >= | 25 |
+--------------------+------+-----+------+----+--+----------------------------------------------------+----+------+
A nudge in the right direction would be a tremendous help.
I think you want your objective to be Profit, which I would define as the sum of sales value - sum of cost.
To include all blends, develop calculations for Volume produced, Lube Index, Cost, and Value for each blend. Apply constraints for volume of stock used, volume produced, and lube index, and optimize for Profit.
I put together the model as follows ...
Columns A through D is the information you provided.
The 10's in G2:J5 are seed values for the stock volumes used in each blend. Solver will manipulate these.
Column K contains the total product volume produced. These will be constrained in different ways, as per your investigation (a), (b), and (c). It is =SUM(G3:J3) filled down.
Column L is the Lube Index for the product. As you noted, it is a linear blend - this is typically not true for blending problems. These values will be constrained in Solver. It is {=SUMPRODUCT(G3:J3,TRANSPOSE($B$2:$B$5))/$K3} filled down. Note that it is a Control-Shift-Enter (CSE) formula, required because of the TRANSPOSE.
Column M is the cost of the stock used to create the product. This is used in the Profit calculation. It is {=SUMPRODUCT(G3:J3,TRANSPOSE($C$2:$C$5))}, filled down. This is also a CSE formula.
Column N is the value of the product produced. This is used in the Profit calculation. It is =K3*C8 filled down.
Row 7 is the total stock volume used to generate all blends. These values will be constrained in Solver. It is =SUM(G3:G5), filled to the right.
The profit calculation is =SUM(N3:N5)-SUM(M3:M5).
Below is a snap of the Solver dialog box ...
It does the following ...
The objective is to maximize profit.
It will do this by manipulating the amount of stock that goes into each blend.
The first four constraints ($G$7 through $J$7) ensure the amount of stock available is not violated.
The next three constraints ($K$3 through $K$5) are for case (a) - make no more than product than there is demand.
The last three constraints ($L$3 through $L$5) make sure the lube index meets the minimum specification.
Not shown - I selected options for GRG Nonlinear and selected "Use Multistart" and deselected "Require Bounds on Variables".
Below is the result for case (a) ...
For case (b), change the constraints on Column K to be "=" instead of "<=". Below is the result ...
For case (c), change the constraints on Column K to be ">=". Below is the result ...
I think I came up with a solution, but I'm unsure if this is correct.
| Decision Variables | | | | | | | | | | | | | | | | |
|--------------------|---------|--------|--------|--------|-------------|--------|--------|--------|--------|--------|--------|--------|---|--------------------------------|----|------|
| | C1R | C1M | C1S | C2R | C2M | C2S | C3R | C3M | C3S | C4R | C4M | C4S | | | | |
| Inputs | 1000 | 0 | 0 | 800 | 0 | 300 | 0 | 1200 | 0 | 200 | 300 | 600 | | | | |
| | | | | | | | | | | | | | | | | |
| Objective Function | | | | | | | | | | | | | | Total Profit (Selling - Cost) | | |
| Cost | 7,10 € | 7,10 € | 7,10 € | 8,50 € | 8,50 € | 8,50 € | 7,70 € | 7,70 € | 7,70 € | 9,00 € | 9,00 € | 9,00 € | | 3.910,00 € | | |
| | | | | | | | | | | | | | | | | |
| Constraints | | | | | | | | | | | | | | LHS | | RHS |
| Regular | -5 | | | 15 | | | 5 | | | 30 | | | | 13000 | >= | 0 |
| Multi | | -15 | | | 5 | | | -5 | | | 20 | | | 0 | >= | 0 |
| Supreme | | | -30 | | | -10 | | | -20 | | | 5 | | 0 | >= | 0 |
| C1 Supply | 1 | 1 | 1 | | | | | | | | | | | 1000 | <= | 1000 |
| C2 Supply | | | | 1 | 1 | 1 | | | | | | | | 1100 | <= | 1100 |
| C3 Supply | | | | | | | 1 | 1 | 1 | | | | | 1200 | <= | 1200 |
| C4 Supply | | | | | | | | | | 1 | 1 | 1 | | 1100 | <= | 1100 |
| Regular Demand | 1 | | | 1 | | | 1 | | | 1 | | | | 2000 | >= | 2000 |
| Multi Demand | | 1 | | | 1 | | | 1 | | | 1 | | | 1500 | >= | 1500 |
| Supreme Demand | | | 1 | | | 1 | | | 1 | | | 1 | | 900 | >= | 750 |
| | | | | | | | | | | | | | | | | |
| | | | | | | | | | | | | | | | | |
| Selling | | | | | | | | | | | | | | | | |
| Regular | 8,50 € | x | 2000 | = | 17.000,00 € | | | | | | | | | | | |
| Multi | 9,00 € | x | 1500 | = | 13.500,00 € | | | | | | | | | | | |
| Supreme | 10,00 € | x | 900 | = | 9.000,00 € | | | | | | | | | | | |
| | | | | | 39.500,00 € | | | | | | | | | | | |
I have an excel sheet table with a structure like this:
+------------+-----+----------+----------+------------------+------------------+------------------+------------------+------------------+------------------+----------------+----------------+------------------+------------------+------------------+------------------+------------------+------------------+----------------+----------------+
| date | Day | StoreDdg | StoreR/H | DbgCategory1Dpt1 | R/HCategory1Dpt1 | DbgCategory2Dpt1 | R/HCategory2Dpt1 | DbgCategory3Dpt1 | R/HCategory2Dpt1 | DbgDepartment1 | R/HDepartment1 | DbgCategory1Dpt2 | R/HCategory1Dpt2 | DbgCategory2Dpt2 | R/HCategory2Dpt2 | DbgCategory3Dpt2 | R/HCategory2Dpt2 | DbgDepartment2 | R/HDepartment2 |
+------------+-----+----------+----------+------------------+------------------+------------------+------------------+------------------+------------------+----------------+----------------+------------------+------------------+------------------+------------------+------------------+------------------+----------------+----------------+
| 1-Jan-2017 | Sun | 138,894 | 133% | 500 | 44% | 12,420 | 146% | | | | 11,920 | 104% | #DIV/0! | 13,580 | 113% | 9,250 | 92% | 6,530 | 147% |
| 2-Jan-2017 | Mon | 138,894 | 270% | 500 | 136% | 12,420 | 277% | 11,920 | | | | 193% | #DIV/0! | 13,580 | 299% | 9,250 | 225% | 6,530 | 181% |
+------------+-----+----------+----------+------------------+------------------+------------------+------------------+------------------+------------------+----------------+----------------+------------------+------------------+------------------+------------------+------------------+------------------+----------------+----------------+
I would like to convert this into
+------------+-----+--------+-------------+---------------+---------+------+
| date | Day | Store | Department | Category | Dpt | R/H |
+------------+-----+--------+-------------+---------------+---------+------+
| 1-Jan-2017 | Sun | Store1 | Department1 | Category1Dpt1 | 138,894 | 133% |
| 1-Jan-2017 | Sun | Store1 | Department1 | Category2Dpt1 | 500 | 44% |
| 1-Jan-2017 | Sun | Store1 | Department1 | Category3Dpt1 | 12,420 | 146% |
| 1-Jan-2017 | Sun | Store1 | Department2 | Category1Dpt2 | 11,920 | 104% |
| 1-Jan-2017 | Sun | Store1 | Department2 | Category2Dpt2 | 13,580 | 44% |
| 1-Jan-2017 | Sun | Store1 | Department2 | Category3Dpt2 | 9,250 | 92% |
| 2-Jan-2017 | Mon | Store1 | Department1 | Category1Dpt1 | 138,894 | 270% |
| 2-Jan-2017 | Mon | Store1 | Department1 | Category2Dpt1 | 500 | 136% |
| 2-Jan-2017 | Mon | Store1 | Department1 | Category3Dpt1 | 12,420 | 277% |
| 2-Jan-2017 | Mon | Store1 | Department2 | Category1Dpt2 | 13,580 | 299% |
| 2-Jan-2017 | Mon | Store1 | Department2 | Category2Dpt2 | 9,250 | 225% |
| 2-Jan-2017 | Mon | Store1 | Department2 | Category3Dpt2 | 6,530 | 181% |
+------------+-----+--------+-------------+---------------+---------+------+
any recommendation about how to do this?
You can do this by taking the excel file as source. You might have to save as the excel in 2005 or 2007 format depending upon the version you are using of the visual studio if it is already in 2007 format then its good .
Now extracting the data for DbgDepartment1 and DbgDepartment2 , you may create 2 different source in the DFT. In one , you may select column which are related to DbgDepartment1 and in the second ,you may choose DbgDepartment2. You might have to use the Derived Column depending on the logic you will use further . Then you may use the Union Transformation, as the source file is the same and can load the data into the destination .Try it , you will get a solution .
I used R statistic language to solve this issue by using data tidying packages ("tidyr", "devtools")
for more info check the link: http://garrettgman.github.io/tidying/
I'm trying to model some outbound calling data in PowerPivot. We have reps across multiple locations, and in general we breakdown our outbound calling into two periods of the day (before and after 12pm).
We can export data from our phone system a list of every call made for a day -- let's say an example is as follows:
+------------+-------------+-------+-----------+-------------+
| Date | Call Length | Agent | Workgroup | Call Period |
+------------+-------------+-------+-----------+-------------+
| 01.01.2016 | 00:05:26 | Sam | Sydney | 1 |
| 01.01.2016 | 00:15:05 | Sam | Sydney | 1 |
| 01.01.2016 | 00:55:22 | John | Sydney | 2 |
| 01.01.2016 | 00:45:11 | Sam | Sydney | 2 |
| 01.01.2016 | 00:04:52 | John | Sydney | 1 |
| 01.01.2016 | 00:01:52 | Timmy | London | 1 |
| 01.01.2016 | 00:02:21 | Timmy | London | 2 |
| 01.01.2016 | 00:05:21 | Karen | London | 1 |
| 02.01.2016 | 00:15:21 | Sam | Sydney | 1 |
| 02.01.2016 | 00:42:44 | Sam | Sydney | 2 |
| 02.01.2016 | 01:52:22 | John | Sydney | 1 |
| 02.01.2016 | 00:53:24 | John | Sydney | 1 |
| 02.01.2016 | 00:05:53 | Kerry | Sydney | 2 |
| 02.01.2016 | 00:43:43 | Sam | Sydney | 2 |
| 02.01.2016 | 01:08:00 | John | Sydney | 2 |
| 02.01.2016 | 00:13:52 | Timmy | London | 2 |
| 02.01.2016 | 00:25:44 | Timmy | London | 1 |
| 02.01.2016 | 02:58:31 | Karen | London | 1 |
| 02.01.2016 | 00:08:37 | Timmy | London | 2 |
| 02.01.2016 | 00:12:28 | Karen | London | 2 |
+------------+-------------+-------+-----------+-------------+
What I'm trying to calculate is the average daily time spent on phone per Workgroup, eg. on average how long is each agent on the phone at each location.
I'm guessing the arithmetic is as follows:
Measure 1: Total talk time for each Agent (eg. sum of all talk time for the day)
Measure 2: Average agent total talk time per workgroup (eg. sum of the above grouped by workgroup, divided by number of agents in that workgroup)
The output might look something like this (but doesn't have to be):
+------------+-----------+-----------------------+-----------------+-----------------------------+
| Date | Workgroup | Total Number of Calls | Total Talk Time | Average Talk Time per Agent |
+------------+-----------+-----------------------+-----------------+-----------------------------+
| 01.01.2016 | Sydney | 11 | 03:02:42 | 1:34:53 |
| | London | 4 | 02:24:51 | 01:13:41 |
| 02.01.2016 | Sydney | 5 | 01:52:05 | 00:56:51 |
| | London | 52 | 10:11:23 | 03:51:11 |
+------------+-----------+-----------------------+-----------------+-----------------------------+
Apologies if I'm unclear it what I'm asking.
Slicing your data on a pivot table will do the calculations.
you only need the following calculations:
DurationOfCall :=sum(MyTable[CallLength])
NrOfCalls :=countrows(MyTable)
AvgDuration :=DIVIDE([DurationOfCall],[NrOfCalls])
this will give the following result (on your sample dataset):
Workbook with testcase: attachment
I have a file that has data stored in it the following way (weekly data example)
+----------+----------+----------+----------+----------+----------+
| | WK1 | WK2 | WK3 | WK4 | WK5 |
+----------+----------+----------+----------+----------+----------+
| DT Begin | 29.12.14 | 05.01.15 | 12.01.15 | 19.01.15 | 26.01.15 |
| DT End | 04.01.15 | 11.01.15 | 18.01.15 | 25.01.15 | 01.02.15 |
| XData | 50 | 10 | 10 | 10 | 50 |
+----------+----------+----------+----------+----------+----------+
My problem ist to aggregate the XData on a monthly basis. For that I want to break the data down for days and then calculate the average.
Edit: I changed the table as it was not clear what I meant. This averages to ((50*4)+(10*21)+(5*50))/31 = 22.90
+------------+-------+
| Date | Value |
+------------+-------+
| 01.01.2015 | 50 |
| 02.01.2015 | 50 |
| 03.01.2015 | 50 |
| 04.01.2015 | 50 |
| 05.01.2015 | 10 |
| 06.01.2015 | 10 |
| 07.01.2015 | 10 |
| 08.01.2015 | 10 |
| 09.01.2015 | 10 |
| 10.01.2015 | 10 |
| 11.01.2015 | 10 |
| 12.01.2015 | 10 |
| 13.01.2015 | 10 |
| 14.01.2015 | 10 |
| 15.01.2015 | 10 |
| 16.01.2015 | 10 |
| 17.01.2015 | 10 |
| 18.01.2015 | 10 |
| 19.01.2015 | 10 |
| 20.01.2015 | 10 |
| 21.01.2015 | 10 |
| 22.01.2015 | 10 |
| 23.01.2015 | 10 |
| 24.01.2015 | 10 |
| 25.01.2015 | 10 |
| 26.01.2015 | 50 |
| 27.01.2015 | 50 |
| 28.01.2015 | 50 |
| 29.01.2015 | 50 |
| 30.01.2015 | 50 |
| 31.01.2015 | 50 |
+------------+-------+
| Average | 22.90 |
+------------+-------+
After having done this calculation I want to summarize the data as follows for the entire year:
+-------+-------+-------+------+------+
| | Jan | Feb | Mar | ... |
+-------+-------+-------+------+------+
| XData | 22.90 | 22.00 | 23.1 | ... |
+-------+-------+-------+------+------+
Being a newbie in Excel VBA, I have extreme trouble doing this.
I know how to get to the value of a cell (Range.Value) but not how to find data in a particular week (as WK1 is there for 2014 as well) Range.Find with a date other than the one in the cell itself does not seem to work.
Whar I am asking for is a way to approach this problem. My particular difficulties are to:
Find the data in the worksheet
split the week values into day values (see table above)
Copy the data or hold it in some sort of data structure
calculate the average (this should be ease then)
fill in the data on a monthly basis
As you can see, I have trouble even getting started - any hints would be greatly appreciated. Maybe I'm thinking of this entirely too complicated? Thank you!