I have a pivot chart which currently has the data source referring to my table.
Every week I run my code and a new row of data is appended to the bottom of my table.
The pivot does pick up this new data every week as it refers to the table, however, I want to take one less week each week So I have a years worth of data. So I want to include around rows.
Is there anyway to adjust my table to only include the years worth of rows?
Here is my sample data:
+----------+------------------+-----------------+
| week | stack | overflow |
+----------+------------------+-----------------+
| 12/20/17 | -142,335,432.00 | -41,641,109.88 |
| 12/27/17 | -105,428,220.20 | -47,448,990.63 |
| 1/3/18 | -88,520,154.56 | -24,858,774.97 |
| 1/10/18 | -42,033,431.10 | 14,573,779.35 |
| 1/17/18 | -66,101,748.16 | -8,670,735.22 |
| 1/24/18 | -75,871,649.12 | -18,000,154.21 |
| 1/31/18 | -77,027,686.63 | -11,784,198.64 |
| 2/7/18 | -96,720,126.71 | -52,219,288.98 |
| 2/14/18 | -119,118,554.60 | -34,743,350.28 |
| 2/21/18 | -116,529,554.70 | -20,774,072.93 |
| 2/28/18 | -86,871,998.53 | -25,993,521.20 |
| 3/7/18 | -90,351,387.27 | -21,259,727.05 |
| 3/14/18 | -77,968,076.28 | -51,609,924.29 |
| 3/21/18 | -120,805,352.60 | -40,338,490.97 |
| 3/28/18 | -92,247,583.62 | -14,525,648.04 |
| 4/4/18 | -70,821,451.36 | -35,866,864.46 |
| 4/11/18 | -82,694,486.66 | -59,009,729.82 |
| 4/18/18 | -79,034,094.39 | -64,231,312.42 |
| 4/25/18 | -63,415,815.16 | -28,612,265.37 |
| 5/2/18 | -80,372,191.96 | -53,375,611.61 |
| 5/9/18 | -72,619,415.73 | -50,642,469.19 |
| 5/16/18 | -109,654,240.70 | -45,762,784.43 |
| 5/23/18 | -100,407,366.50 | -39,577,966.11 |
| 5/30/18 | -105,794,095.80 | -65,071,199.59 |
| 6/6/18 | -83,630,201.98 | -60,981,969.88 |
| 6/13/18 | -104,644,821.50 | -63,754,760.71 |
| 6/20/18 | -75,229,424.33 | -55,803,681.24 |
| 6/27/18 | -65,237,135.62 | -54,693,832.65 |
| 7/4/18 | -60,025,672.33 | -44,367,918.60 |
| 7/11/18 | -30,172,175.09 | -28,392,163.28 |
| 7/18/18 | -20,687,864.39 | 24,300,285.63 |
| 7/25/18 | -40,476,447.03 | 4,850,881.09 |
| 8/1/18 | -31,211,625.05 | -67,887,918.30 |
| 8/8/18 | -29,736,938.87 | -32,905,703.80 |
| 8/15/18 | -74,934,647.91 | -65,611,884.73 |
| 8/22/18 | -25,220,747.20 | -7,019,746.86 |
| 8/29/18 | -24,608,552.13 | -8,065,633.97 |
| 9/5/18 | -30,119,599.95 | -26,225,633.08 |
| 9/12/18 | -29,836,379.12 | -10,045,560.95 |
| 9/19/18 | -61,281,567.61 | -58,427,878.27 |
| 9/26/18 | -47,418,209.59 | -33,451,409.22 |
| 10/3/18 | -41,321,336.46 | -25,112,764.44 |
| 10/10/18 | -1,241,932.51 | 21,814,274.35 |
| 10/17/18 | -19,791,273.66 | -12,199,449.75 |
| 10/24/18 | -20,501,406.84 | 1,225,387.11 |
| 10/31/18 | -64,116,464.30 | -5,308,628.21 |
| 11/7/18 | -83,657,672.02 | -19,922,992.91 |
| 11/14/18 | -112,704,007.53 | -32,939,535.69 |
| 11/21/18 | -71,969,954.54 | -51,335,709.79 |
| 11/28/18 | -79,668,484.56 | -67,887,918.30 |
| 12/5/18 | -44,134,343.99 | -32,905,703.80 |
| 12/12/18 | -71,700,079.84 | -65,611,884.73 |
| 12/19/18 | -82,238,011.30 | -74,725,620.20 |
| 12/26/18 | -59,385,932.41 | -54,947,256.94 |
| 1/2/19 | -42,717,830.26 | -31,110,199.14 |
| 1/9/19 | -11,029,444.63 | 7,309,440.90 |
+----------+------------------+-----------------+
Changing the source range for the pivot will be tricky as Excel does not allow non-contiguous cells to be used in pivot tables. Instead you can create the pivot by selecting entire column to account for all future entries.
Then the pivot can be manipulated to show a changing range as shown in the code below.
Hope that works for you.
EDIT
The code is updated below to include 50(can be changed) from the bottom.
Sub MovingPivot()
Dim ws As Worksheet
Dim dtTop As Date
Dim i As Integer, n As Long
Const NumWeeks = 50 'Change this to set weeks range
Set ws = ActiveSheet 'Set reference to your worksheet here
'reset the pivot filters
ws.PivotTables("Table1").PivotFields("Date").ClearAllFilters
'remove blank values
ws.PivotTables("Table1").PivotFields("Date").PivotItems("(blank)").Visible = False
'find the date entry in 50 places from bottom.
i = 0
For n = ws.PivotTables("Table1").RowRange.Count To 1 Step -1
If i = NumWeeks Then
dtTop = ws.PivotTables("Table1").RowRange.Cells(n).Value
Exit For
End If
i = i + 1
Next n
ws.PivotTables("Table1").PivotFields("Date").PivotFilters.Add2 Type:=xlAfterOrEqualTo, Value1:=Format(dtTop, "dd-mmm-yyyy")
End Sub
If you want to continue using a Pivot Chart, you can use the time line slicer to include/exclude data. You'll need to adjust the time line or filter manually after the data has refreshed. Or write VBA to set the filters.
A non-vba version that does not require slicers can also be achieved with a standard chart (not a pivot chart). Create named ranges with Offset functions that grab just the rows of data that you are interested in, then plug these range names into the standard chart. When new data is added to the table, the named ranges that feed the standard chart will also be updated.
If you need a step by step, take a look at https://peltiertech.com/Excel/Charts/DynamicLast12.html
I need to analyze Weekly order frequencies over last 1 year period to find out what is the min/max/average frequencies of orders for each product.
whether it is new or old,system should calculate the first occurrence of the order in the year as the starting week of the order. Min order frequency is difference between successive ordering weeks. If the first order is in wk 3 and the second order is in wk6, implies the order frequency is 3 weeks (=>6-3). Orders can be at any week in the past 52 weeks. Average order frequency = (52 - First order week) / no of weeks that have orders.
Attaching the excel for better understanding the issue.
Original image
+---------+-----+-----+-----+-----+-----+-----+-----+-----+-----+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+----------------+-------------------------+-----+-----------------------------------+--+
| Product | wk1 | wk2 | wk3 | wk4 | wk5 | wk6 | wk7 | wk8 | wk9 | wk10 | wk11 | wk12 | wk13 | wk14 | wk15 | wk16 | wk17 | wk18 | wk19 | wk20 | wk21 | wk22 | wk23 | wk24 | wk25 | wk26 | wk27 | wk28 | wk29 | wk30 | wk31 | wk32 | wk33 | wk34 | wk35 | wk36 | wk37 | wk38 | wk39 | wk40 | wk41 | wk42 | wk43 | wk44 | wk45 | wk46 | wk47 | wk48 | wk49 | wk50 | wk51 | wk52 | Order start wk | Order frequency (Weeks) | | | |
+---------+-----+-----+-----+-----+-----+-----+-----+-----+-----+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+----------------+-------------------------+-----+-----------------------------------+--+
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Min | Max | Average | |
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | (End wk - Start week)/No of times | |
| SKU 1 | | | | | | | | | y | | y | | y | | y | | y | | y | | y | | y | y | | | y | | y | | y | | y | | | | | | y | | y | | y | | y | | y | | y | | y | | 9 | 1 | 6 | 2.15 | |
| SKU 2 | | | | | | | y | | | | | | y | | | | | | y | | | | | | y | | | | | | y | | | | | | y | | | | | | y | | | | | | y | | | | 1 | 0 | 0 | 7.29 | |
| SKU 3 | | | | | | | | | | | | | | | y | | | | | | | | | | | | | | | | y | | | | | | | | y | | | | | | | | y | | | | | | 15 | 8 | 15 | 9.25 | |
+---------+-----+-----+-----+-----+-----+-----+-----+-----+-----+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+------+----------------+-------------------------+-----+-----------------------------------+--+
So as mentioned #Barry Houdini solves the problem of finding the longest sequence of zeroes separated by ones elegantly here
You only have to change it slightly to check for repeated blank cells separated by 'y'. The only thing is that you don't want to include cells before the first 'y', and (although this isn't clear) may not want to include blank cells after the last 'y'.
The formula for MIN becomes
=MIN(IF((ROW(A$1:INDEX(A:A,COUNTA(B4:BA4)+1))>1)*(ROW(A$1:INDEX(A:A,COUNTA(B4:BA4)+1))<COUNTA(B4:BA4)+1),FREQUENCY(IF(B4:BA4="",COLUMN(B4:BA4)),IF(B4:BA4="y",COLUMN(B4:BA4)))))+1
and the formula for MAX becomes (the same)
=MAX(IF((ROW(A$1:INDEX(A:A,COUNTA(B4:BA4)+1))>1)*(ROW(A$1:INDEX(A:A,COUNTA(B4:BA4)+1))<COUNTA(B4:BA4)+1),FREQUENCY(IF(B4:BA4="",COLUMN(B4:BA4)),IF(B4:BA4="y",COLUMN(B4:BA4)))))+1
where you need to add 1 to make the results agree with the question because #Barry's formula counts numbers of blanks but OP wants interval between two successive y's. An array of ny+1 elements is generated where ny is the number of y's. This is because the FREQUENCY function returns an array with n+1 elements where n is the number of cut points (bins_array in documentation and because the column numbers of cells containing y are used as cut points so there are ny of them.
These are both array formulas and need to be entered with CtrlShiftEnter
The formula for the average is just
=(COLUMNS(B4:BA4)-MATCH("y",B4:BA4,0))/COUNTA(B4:BA4)
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 € | | | | | | | | | | | |