1. In this Slump moderl, self compacting concrete is investigated by using industrial by products such as fly ash, slag, water, SP and coarse aggregate and fine aggregate as shown in the charts
Cement and Slump Chart
Slag and Slump chart
Fly ash and slum chart
Water and Slump chart
SP and slum chart
Coarse Aggregate and Fine aggregate
Final Chart
2.
|
Task |
Predecessors |
Time Estimate |
|
A |
none |
Within 10 to 12 days. |
|
B |
A |
On an average 5 days with a standard deviation of 1 day. |
|
C |
A |
Within 11 to 21 days. |
|
D |
B |
Within 2 to 4 days. |
|
E |
C |
On an average 15 days with a standard deviation of 2.5 day. |
|
F |
D and E |
On an average 8 days with a standard deviation of 0.5 day. |
|
Name |
Min |
Max |
Mean |
Expected Time |
Actual Time |
|
A |
0 |
12 |
1 |
10 |
12 |
|
B |
12 |
17 |
2.416667 |
5 |
5 |
|
C |
17 |
29 |
3.833333 |
11 |
12 |
|
D |
29 |
33 |
5.166667 |
2 |
4 |
|
E |
33 |
44 |
6.416667 |
15 |
15 |
|
F |
44 |
52 |
8 |
8 |
8 |
Critical task Marked in Red, i.e. D & E task
3. The advantages of decomposing process models are self-evident, the inquiry what really describes a “decent” decay of a business procedure display has been given little consideration regarding date. What’s more, the procedure of decomposing itself is considered just like a “workmanship” in writing. Our approach for accomplishing a “decent” decomposition is disintegration display for data frameworks. As an initial phase in employee projections, we intend to investigate in how far the decomposition model can be adjusted for business process displaying by any means. The potential this model may bear for assessing disintegrations of process models has been advanced in writing frequently, while a comparing examination is as yet absent because of data compatibility. We address this hole by the accompanying exploration. In the long haul, we expect to set up rules for deteriorating business process models structurally for greater good.
Total Employee Projections
First we plot the data points on the graph and then fit a straight line. The reader can fit a straight line in a manner different from this. Naturally then his estimates are likely to vary a bit. We can also fit a straight line by the method of least squares.
|
Total Trade Per year |
Year |
Average trade |
|
3929 |
1 |
327.4167 |
|
3986 |
2 |
332.1667 |
|
4164 |
3 |
347 |
|
4373 |
4 |
364.4167 |
|
4519 |
5 |
376.5833 |
4. The problem wants to maximize the pro_t obtained from making and selling the two types of blends of fuel.
Defne the (decision) variables:
x1 = amount of A oil used in blend 1
x2 = amount of B oil used in blend 1
x3 = amount of C oil used in blend 1
x4 = amount of A oil used in blend 2
x5 = amount of B oil used in blend 2
x6 = amount of C oil used in blend 2
The total revenue and cost is given by
Revenue = 1:1(x1 + x2 + x3) + 1:2(x4 + x5 + x6)
Cost = 0:3(x1 + x4) + 0:4(x2 + x5) + 0:48(x3 + x6)
and the objective function to be maximized is the linear function
Profit = Revenue ???? Cost
Next we identify the constrains:
Amount of oil available:
x1 + x4 _ 6; 000
x2 + x5 _ 10; 000
x3 + x6 _ 12; 000
Long term contract requirements:
x1 + x2 + x3 _ 10; 000
x4 + x5 + x6 _ 10; 000
Requirement on the composition of the fuels:
x1=(x1 + x2 + x3) _ 0:3
x2=(x1 + x2 + x3) _ 0:5
x3=(x1 + x2 + x3) _ 0:3
x4=(x4 + x5 + x6) _ 0:4
x5=(x4 + x5 + x6) _ 0:35
x6=(x4 + x5 + x6) _ 0:4
Each of the inequalities above need to be rewritten in linear form ak1x1 + : : : ak6x6 _ bk, for example the first one
becomes:
x1 _ 0:3(x1 + x2 + x3); or, equivanently, ???? 0:7×1 + 0:3×2 + 0:3×3 _ 0:
We will use the optimal solution to solve the equations one by one:
min fT x; subject to Ax _ b
R=[1.1; 1.1; 1.1; 1.2; 1.2; 1.2];
C=[0.3; 0.4; 0.48; 0.3; 0.4; 0.48];
f=R-C;
A=[1 0 0 1 0 0;
0 1 0 0 1 0;
0 0 1 0 0 1;
-1 -1 -1 0 0 0;
0 0 0 -1 -1 -1;
-0.7 0.3 0.3 0 0 0;
-0.5 0.5 -0.5 0 0 0;
0.3 0.3 -0.7 0 0 0;
0 0 0 0.6 -0.4 -0.4;
0 0 0 0.35 -0.65 0.35;
0 0 0 -0.4 -0.4 0.6];
b=[6000; 10000; 12000; -10000; -10000; 0; 0; 0; 0; 0; 0];
lb=zeros(6,1);
We employ first the Large Scale Method
Optimization terminated.
x =
3414.41
1483.99
5101.60
2585.59
8516.01
6898.40
fmax =
21040.00
slack =
0.00
0.00
0.00
0.00
8000.00
414.41
3516.01
2101.60
4614.41
2216.01
301.60
shadowprice =
0.90
0.80
0.72
0.10
0.00
0.00
0.00
0.00
0.00
0.00
-0.00
This shows the maximum pro_t is $21,040, obtained when x1 = 3414:41, x2 = 1483:99, x3 = 5101:60, x4 = 2585:59, x5 = 8516:01 and x6 = 6898:40. In making these blends, the _rst 4 constrains are binding. The shadow prices indicate that if one can make additional A oil available at a cost of less than .90 ($ per litre) then one would make an additional profit. Similarly for B and C oil. More over, if one would relax the long-term contract from 10,000 litres to less for blend 1, at a cost of less than 0.72 ($ per litre), then one could make additional pro_t. Notice that the 5th constraint is not binding, with a slack of 8,000, which means that blend 2 is produced in a larger quantity (18,000 litres).
Next, we employ the Medium Scale (simplex method) and compare with the previous results:
Optimization terminated.
x =
3000.00
0
7000.00
3000.00
10000.00
5000.00
fmax =
21040.00
slack =
0
0
0
0
8000.00
0.00
5000.00
4000.00
4200.00
3700.00
2200.00
shadowprice =
0.90
0.80
0.72
0.10
0
0
0
0
0
0
0
Again, the maximum profit is $ 21,040, but with a different values of the decision variables. The shadow prices being the same, the only difference between the two methods are the slacks for the constraints indicating the requirements on the composition of each blend. For example, in the second method, the percentage of A oil in Blend 1 is exactly 30%, whereas in the first method is more. So the choice of the method should be dictated by other factors (which are not specified in this problem).
5. z = (X – Mean)/SD
z = (38 – 35)/2 = + 2
Required probability = P(X < 38)
= P(z < 1.5)
= 0.9332
b)Scrapped if (X <30)
P (X <30)= p[X-35/2<30-35/2]
= P (Z<-2.50)=0.0062
Scrapped percentage= 0.0062 X 100/100=0.62%
6. Let X measure the number of people (out of 130) who show up for a work. For each person we have a 50% chance of showing up, so X is a binomial random variable with n = 130 and p = 0 . 50.
P(x<130)=P(X-65/2<130-65/2)
= P (Z<32.5)= 0.054
Risk %= 0.054X100%=5.4%
P(X<125 or X<130) = P[1 % X 130 + 1% X 125}
= 1.3 + 1.25=2.55~=3
Therefore we require to call=130-3=127 People
7. Regression Analysis
The task supervisor need to bode well on the numerous information that they have. Diagnostic devices are utilized as a part of task administration to accomplish such need. Such apparatuses are utilized to make an estimate of potential results in view of the varieties exhibit in the natural and venture factors. There are distinctive sorts of expository apparatuses utilized and a standout amongst the most widely recognized devices is relapse examination.
The relapse strategies in civil construction are usually utilized for tending to entangled forecast as well as grouping issues in structural building on account of its straightforwardness. For a given dataset, the straight relapse from the info space to the yield factors can be accomplished by utilizing the “slightest square blunder” approach, which limits the distinction between the anticipated and real yields. The “slightest mean square” run can likewise be utilized as a bland way to deal with determining arrangements on linear or non-direct relapses. The major calculations of “minimum square blunder” as well as “slightest mean square” with a specific end goal to encourage the expectation and grouping of process durations of development operations. The great XOR issue is chosen to confirm and approve their exhibitions. A viaduct connect was introduced by propelling precast supports with a versatile gantry sitting on two wharfs. The viability of relapse strategies in characterizing and determining the process duration of introducing one traverse of viaduct considering the most significant info factors regarding operations, coordination’s as well as assets for construction shall be illustrated.
Utilizing this instrument gives venture administrators a clearer understanding on the relationship of two things. Utilized together with the other investigative apparatuses, they can utilize data for the better basic leadership.
Simulation in Civil Construction
Simulation in Civil Construction enable directors to make more exact expectations about the calendar, assets and the aggregated expenses related with a program or an undertaking by making a strong task plan. This begins with building up a structure for demonstrating and situation examinations of the program design. The system is instituted preceding the point by point arranging and work break down structures sessions.
The undertaking layout is planned with specific accentuation on the deliverables of civil construction, the tasks as they identify with the construction work, the mind boggling data as well as re-work conditions between Goals, the main meaning of venture stages along with any abnormal state conditions that may exist amongst them, and a comprehension of the work exertion that is required to accomplish the objectives that are characterized in the model. This first reproduction result isn’t exact or ideal, however brings up zones of outline that need illumination along with change to effectively mirror the genuine conditions as well as connections.
The following stage starts with the production of particular gauge models and reenactments for the program and each of the sub ventures utilizing the layouts and determinations from the primary stage. Additionally, a procedure is considered for the extent of situations once these sub ventures are joined into a typical program show. The most convincing of the situations are then investigated and various “imagine a scenario in which” questions are asked, demonstrated, and mimicked. The gauges demonstrate all that really matters cost, term, and danger of the general program and the sub ventures given these distinctive situations.
The greatest preferred standpoint is that right on time in the arranging stage the official group can get a more conclusive comprehension of the between Goal or between Phase conditions, a comprehension of the conceivable authoritative amendments or adaptability that is conceivable, consciousness of the capacities and gifts of the groups that are included, learning of changes or necessities in Goal needs that are conceivable and an unmistakable comprehension of the work procedure and the capacity to adjust it.
Example:
The simulation investigation is the reason for development control of link stayed spans, it screens the procedure of the scaffold development. The relationship as well as significance of reproduction along with development control shall be presented during the simulation analysis. In view of the limited component hypothesis plus joined development hypothesis of the link – stayed spans, we will utilize the product for Civil works, the development attributes of the Bridge is looked into by making utilization of the forward computation strategy as well as the regressive estimation technique. The outcomes demonstrate that the framework change of the scaffold is happened amid the development, subsequently, it is vital to complete watchful as well as itemized forward recreation to guarantee the well-being of development structure along with construction prerequisites are outlined. The sensible outcomes can be drawn when the regressive count technique is joined with the forward computation strategy during this simulation analysis.
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