SlideShare a Scribd company logo
Research Inventy: International Journal Of Engineering And Science
Vol.5, Issue 6 (June 2015), PP 24-28
Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.com
24
Time to Recruitment for a Single Grade Manpower System with
Two Thresholds, Different Epochs for Inter-Decisions as an
Order Statistics and Exits
G. Ravichandran1
; A. Srinivasan2
1
Assistant Professor in Mathematics, TRP Engineering College (SRM GROUP), Irungalur, Trichy- 621 105,
Tamil Nadu, India,
2
Professor Emeritus, PG & Research Department of Mathematics, Bishop Heber College, Trichy-620 017,
Tamil Nadu, India,
ABSTRACT: In this paper, the problem of time to recruitment is studied using a univariate policy of
recruitment involving two thresholds for a single grade manpower system with attrition generated by its policy
decisions. Assuming that the policy decisions and exits occur at different epochs, a stochastic model is
constructed and the variance of the time to recruitment is obtained when the inter-policy decision times and
inter- exit times form an order statistics and an ordinary renewal process respectively. The analytical results
are numerically illustrated with relevant findings by assuming specific distributions.
Keywords: Single grade manpower system; decision and exit epochs; order statistics; ordinary renewal
process; univariate policy of recruitment with two thresholds and variance of the time to recruitment.
I. INTRODUCTION
Attrition is a common phenomenon in many organizations. This leads to the depletion of manpower.
Recruitment on every occasion of depletion of manpower is not advisable since every recruitment involves cost.
Hence the cumulative depletion of manpower is permitted till it reaches a level, called the threshold. If the total
loss of manpower exceeds this threshold, the activities in the organization will be affected and hence recruitment
becomes necessary. In [1, 2, 3] the authors have discussed the manpower planning models using Markovian and
renewal theoretic methods. In [4] the author has studied the problem of time to recruitment for a single grade
manpower system and obtained the variance of the time to recruitment when the loss of manpower forms a
sequence of independent and identically distributed random variables, the inter- decision times form a geometric
process and the mandatory breakdown threshold for the cumulative loss of manpower is an exponential random
variable by using univariate cum policy of recruitment. In [5] the author has studied the work in [4] using
univariate and bivariate policies of recruitment both for the exponential mandatory threshold and for the one
whose distribution has the SCBZ property. In [6] the author has initiated the study of the problem of time to
recruitment for a single grade manpower system by incorporating alertness in the event of cumulative loss of
manpower due to attrition crossing the threshold, by considering optional and mandatory thresholds for the
cumulative loss of manpower in this manpower system. In [7] the authors have studied this problem when inter-
decision times form an order statistics. In all the above cited work, it is assumed that attrition takes place
instantaneously at decision epochs. This assumption is not realistic as the actual attrition will take place only at
exit points which may or may not coincide with decision points. This aspect is taken into account for the first
time in [8] and the variance of time to recruitment is obtained when the inter-decision times and exit times are
independent and identically distributed exponential random variables using univariate policy for recruitment and
Laplace transform in the analysis. In [9, 11, 12] the authors have extended their work in [8] when the inter-
decision times form a sequence of exchangeable and constantly correlated exponential random variables,
geometric process and order statistics respectively. Recently, in [13, 14, 15] the authors have studied the work in
[8, 9, 11] respectively by considering optional and mandatory thresholds which is a variation from the work of
[6] in the context of considering non-instantaneous exits at decision epochs. In the present paper, for a single
grade manpower system, a mathematical model is constructed in which attrition due to policy decisions takes
place at exit points and there are optional and mandatory thresholds as control limits for the cumulative loss of
manpower. A univariate policy of recruitment based on shock model approach is used to determine the variance
of time to recruitment when the system has different epochs for policy decisions and exits and the inter- exit
times form an ordinary renewal process. The present paper studies the research work in [15] when the inter-
policy decision times form an order statistics.
Time to Recruitment for a Single Grade Manpower System with…
25
II. MODEL DESCRIPTION
Consider an organization taking decisions at random epochs in (0, ∞) and at every decision making
epoch a random number of persons quit the organization. There is an associated loss of manpower if a person
quits. It is assumed that the loss of manpower is linear and cumulative. Let 𝑋𝑖 be the continuous random
variable representing the amount of depletion of manpower (loss of man hours) caused at the 𝑖 𝑡ℎ
exit point and
kS be the total loss of manpower up to the first k exit points. It is assumed that 𝑋𝑖’s are independent and
identically distributed random variables with probability density function m(.), distribution function M(.) and
mean )0(
1


. Let kU be the continuous random variable representing the time between the (k-1)th
and kth
policy decisions. It is assumed that sUk
'
are independent and identically distributed random variables with
distribution F(.) and probability density function (.)f .Let (.))( jaF and (.))( jaf be the distribution and the
probability density function of the
th
j order statistic ( rj ,...,2,1 ) selected from the sample of size r from the
population  
1kkU . Let 𝑊𝑖 be the continuous random variable representing the time between the (i-1)th
and ith
exit points. It is assumed that iW ’s are independent and identically distributed random variables with probability
density function g(.), probability distribution function G(.). Let )(tNe
be the number of exit points lying in
(0, t]. Let Y be the optional threshold level and Z the mandatory threshold level (Y< Z) for the cumulative
depletion of manpower in the organization with probability density function h(.) and distribution function H(.).
Let p be the probability that the organization is not going for recruitment when optional threshold is exceeded
by the cumulative loss of manpower. Let q be the probability that every policy decision has exit of personnel. As
q=0 corresponds to the case where exits are impossible, it is assumed that q  0. Let T be the random variable
denoting the time to recruitment with probability distribution function L(.), density function l (.), mean E(T)
and variance V(T). Let (.)a be the Laplace transform of a(.). The univariate CUM policy of recruitment
employed in this paper is stated as follows:
Recruitment is done whenever the cumulative loss of manpower in the organization exceeds the mandatory
threshold. The organization may or may not go for recruitment if the cumulative loss of manpower exceeds the
optional threshold.
III. MAIN RESULT
As in [15], we get
)()(])([)(])([)(
00
ZSPYSPktNPpYSPktNPtTP kk
k
ek
k
e  




(1)
)()()()()( 1
1k
1
1k
1
1k 





 








 k
k
k
k
k
k abtGabbtGbpatGatL (2)
and













)()1(1
)(
)()1(1
)(
)()1(1
)(
)(
sfqab
sfqab
sfqb
sfqb
p
sfqa
sfqa
sl
(3)
where
ababbbaa  1,1,1 (4)
Let rUUU ,...,, 21 be arranged in increasing order of magnitude so that we have the sequence as
)()2()1( ,...,, rUUU , here r...,,2,1j,...,2,1 r are called the order statistics and with )1(U as the first order
statistic and )(rU the rth
order statistic and the random variables )()2()1( ,...,, rUUU are not independent. The
probability density function of the th
j order statistic is given by
Time to Recruitment for a Single Grade Manpower System with…
26
jrj
ja tFtftF
j
r
jtf 






 )](1[)()]([)( 1
)( , (5)
where it is assumed that t
etF 
 1)( .
Case (i): )()( )1( tftf a
In this case, from (3) and (5), we get














)()1(1
)(
)()1(1
)(
)()1(1
)(
)(
)1(
)1(
)1(
)1(
)1(
)1(
sfqab
sfqab
sfqb
sfqb
p
sfqa
sfqa
sl
a
a
a
a
a
a
, (6)
where
sr
r
sf a




)()1(
(7)
Substituting (7) in (6) and on simplification, we get



















rqabsr
qrab
rqbsr
qrb
p
rqasr
rqa
sl
)1()()1()()1()(
)(
(8)
  






 
 qrabqrb
p
qra
sl
ds
d
TE s
111
)()( 0
(9)
and
   
2
22222
2
02
2
1
)(
1
)()()(
1
)(
2
)()()(














 
ab
p
b
p
aqrab
p
b
p
aqr
TEsl
ds
d
TV s

(10)
where abba ,, are given by (4).
Case (ii): )()( )( tftf ra .
In this case, from (3) and (5), we get














)()1(1
)(
)()1(1
)(
)()1(1
)(
)(
)(
)(
)(
)(
)(
)(
sfqab
sfqab
sfqb
sfqb
p
sfqa
sfqa
sl
ra
ra
ra
ra
ra
ra (11)
where
s)s)...(s)(2(
!
)(
r
)(




r
r
sf ra (12)
Substituting (12) in (11) and on simplification, we get












 r
r
r
r
r
r
rqabC
rqab
rqbC
rqb
p
rqaC
rqa
sl






!)1(
!
!)1(
!
!)1(
!
)( (13)
where
  r
s rrC  !Cands)s)...(s)(2( 0   (14)
Time to Recruitment for a Single Grade Manpower System with…
27
Proceeding as in case(i), we get






 
111
)(
1 ab
p
b
p
ajq
TE
r
j
(15)
and



























   ab
p
b
p
ajqab
qabp
b
qbp
a
qa
jq
TV
r
j
r
j
111
)(
)2(
)(
)2(
)(
21
)(
1
)(
1
22222
2
1
2

 














 
22
1
2
111
ab
p
b
p
ajq
r
j
(16)
where abba ,, are given by (4).
Equations (9), (10) and (15), (16) give the mean and variance on the time to recruitment for Cases (i) and (ii)
respectively.
Remark:
Computation of )(TV for extended exponential and SCBZ Property possessing thresholds is similar
as their distribution will have just additional exponential terms.
Note:
(i)When p=0 and r =1, our results in Case (i) agree with the results in [8] for the manpower system
having only one threshold which is the mandatory threshold.
(ii)When p=0 and 1r our results in Cases (i) and (ii) agree with the results in [12] for the manpower
system having only one threshold which is the mandatory threshold.
(iii)When q=1and r =1, our results in Case (i) agree with the results in [6] for the manpower system
having certain instantaneous exits in the decision epochs.
(iv)When r =1, 1,0  qp our results in Case (i) agree with the results in [13] for the manpower
system having inter-decision times as an ordinary renewal process.
IV. NUMERICAL ILLUSTRATION
The mean and variance of time to recruitment is numerically illustrated by varying one parameter and
keeping other parameters fixed. The effect of the nodal parameters ,  , r on the performance measures is
shown in the following table. In the computations, it is assumed that
5.0,2.0,09.0,06.0 21  pq .
Table: Effect of nodal parameters on E(T) and V(T)
  r
Case(i) Case(ii)
E(T) V(T) E(T) V(T)
0.2 1 5 4.8795 19.5924 55.6992 2.4614x103
0.2 1 6 4.0662 13.6058 59.7491 2.8277x103
0.2 1 7 3.4853 9.9961 60.7982 2.9271x103
0.2 1 8 3.0497 7.6533 63.8479 3.2247x103
0.2 2 5 2.4397 4.8981 27.8496 615.3589
0.2 3 5 1.6265 2.1769 18.5664 273.4928
0.2 4 5 1.2199 1.2245 13.9248 153.8397
0.2 5 5 0.9759 0.7837 11.1398 98.4574
0.3 1 5 6.7738 37.1331 77.3230 4.7115x103
0.4 1 5 8.6656 60.2258 98.9184 7.6851x103
0.5 1 5 10.5564 88.8727 120.5011 1.1382x104
0.6 1 5 12.4465 123.0743 142.0773 1.5803x104
Time to Recruitment for a Single Grade Manpower System with…
28
V. FINDINGS
From the above tables, the following observations are presented which agree with reality.
1. When α increases and keeping all the other parameters fixed, the average loss of manpower increases.
Therefore the mean and variance of time to recruitment increase.
2. As λ increases, on the average, the inter-decision time decreases and consequently the mean and
variance of time to recruitment decrease when the other parameters are fixed.
3. When r increases and keeping all the other parameters fixed, the mean and variance of the time to
recruitment decrease in Case (i) and increase in Case (ii).
VI. CONCLUSION
The models discussed in this paper are found to be more realistic and new in the context of considering
(i) separate points (exit points) on the time axis for attrition, thereby removing a severe limitation on
instantaneous attrition at decision epochs (ii) associating a probability for any decision to have exit points and
(iii) provision of optional and mandatory thresholds. From the organization’s point of view, our models are
more suitable than the corresponding models with instantaneous attrition at decision epochs, as the provision of
exit points at which attrition actually takes place, postpone the time to recruitment.
REFERENCES
[1] D. J. Bartholomew, Stochastic model for social processes (New York, John Wiley and Sons, 1973).
[2] D. J. Bartholomew and F. Andrew Forbes, Statistical techniques for manpower planning (New York, John Wiley and Sons, 1979).
[3] R. C. Grinold and K.T. Marshall, Manpower planning models (New York, North-Holland, 1977).
[4] A. Muthaiyan, A study on stochastic models in manpower planning, doctoral diss., Bharathidasan University, Tiruchirappalli,
2010.
[5] K. P. Uma, A study on manpower models with univariate and bivariate policies of recruitment, doctoral diss., Avinashilingam
University for Women, Coimbatore, 2010.
[6] J. B. Esther Clara, Contributions to the study on some stochastic models in manpower planning, doctoral diss., Bharathidasan
University, Tiruchirappalli, 2012.
[7] J. Sridharan, A. Saivarajan and A. Srinivasan, Expected time to recruitment in a single graded manpower system with two
thresholds , Journal of Advances in Mathematics, 7(1), 2014, 1147 – 1157.
[8] A. Devi and A. Srinivasan, Variance of time to recruitment for single grade manpower system with different epochs for decisions
and exits, International Journal of Research in Mathematics and Computations, 2(1), 2014, 23 – 27.
[9] A. Devi and A. Srinivasan, Variance of time to recruitment for single grade manpower system with different epochs for decisions
and exits having correlated inter- decision times, Annals of Pure and Applied Mathematics, 6(2), 2014, 185 –190.
[10] J. Medhi, Stochastic processes (New Delhi, Wiley Eastern, second edition, 1994).
[11] A. Devi and A. Srinivasan, A stochastic model for time to recruitment in a single grade manpower system with different epochs for
decisions and exits having inter- decision times as geometric process, Second International Conference on Business Analytic and
Intelligence, ( ICBAI ), 2014.
[12] A. Devi and A. Srinivasan, Expected time to recruitment for a single grade manpower system with different epochs for decisions
and exits having inter- decision times as an order statistics, Mathematical Sciences International Research Journal, 3(2), 2014,
887 – 890.
[13] G. Ravichandran and A. Srinivasan, Variance of time to recruitment for a single grade manpower system with two thresholds
having different epochs for decisions and exits, Indian Journal of Applied Research, 5(1), 2015, 60 – 64.
[14] G. Ravichandran and A. Srinivasan, Time to recruitment for a single grade manpower system with two thresholds, different epochs
for exits and correlated inter-decisions, International Research Journal of Natural and Applied Sciences, 2(2), 2015, 129 – 138.
[15] G. Ravichandran and A. Srinivasan, Time to recruitment for a single grade manpower system with two thresholds, different epochs
for exits and geometric inter-decisions, IOSR Journal of Mathematics, 11(2), Ver. III, 2015, 29 – 32.

More Related Content

What's hot

On the Fractional Optimal Control Problems With Singular and Non–Singular ...
On  the Fractional Optimal Control Problems  With Singular and Non–Singular  ...On  the Fractional Optimal Control Problems  With Singular and Non–Singular  ...
On the Fractional Optimal Control Problems With Singular and Non–Singular ...
Scientific Review SR
 
A SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONS
A SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONSA SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONS
A SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONS
csandit
 
Hybrid model for forecasting space-time data with calendar variation effects
Hybrid model for forecasting space-time data with calendar variation effectsHybrid model for forecasting space-time data with calendar variation effects
Hybrid model for forecasting space-time data with calendar variation effects
TELKOMNIKA JOURNAL
 
A-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-Model
A-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-ModelA-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-Model
A-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-Model
Prof Dr S.M.Aqil Burney
 
Paper 1 (rajesh singh)
Paper 1 (rajesh singh)Paper 1 (rajesh singh)
Paper 1 (rajesh singh)
Nadeem Shafique Butt
 
Minimality and Equicontinuity of a Sequence of Maps in Iterative Way
Minimality and Equicontinuity of a Sequence of Maps in Iterative WayMinimality and Equicontinuity of a Sequence of Maps in Iterative Way
Minimality and Equicontinuity of a Sequence of Maps in Iterative Way
inventionjournals
 
Forecasting With An Adaptive Control Algorithm
Forecasting With An Adaptive Control AlgorithmForecasting With An Adaptive Control Algorithm
Forecasting With An Adaptive Control Algorithm
shwetakarsh
 
Granger Causality Test: A Useful Descriptive Tool for Time Series Data
Granger Causality Test: A Useful Descriptive Tool for Time  Series DataGranger Causality Test: A Useful Descriptive Tool for Time  Series Data
Granger Causality Test: A Useful Descriptive Tool for Time Series Data
IJMER
 
New directions in neuropsychological assessment: Augmenting neuropsychologica...
New directions in neuropsychological assessment: Augmenting neuropsychologica...New directions in neuropsychological assessment: Augmenting neuropsychologica...
New directions in neuropsychological assessment: Augmenting neuropsychologica...
Kevin McGrew
 
Pushing the edge of the contemporary cognitive (CHC) theory: New directions ...
Pushing the edge of the contemporary cognitive (CHC) theory:  New directions ...Pushing the edge of the contemporary cognitive (CHC) theory:  New directions ...
Pushing the edge of the contemporary cognitive (CHC) theory: New directions ...
Kevin McGrew
 

What's hot (10)

On the Fractional Optimal Control Problems With Singular and Non–Singular ...
On  the Fractional Optimal Control Problems  With Singular and Non–Singular  ...On  the Fractional Optimal Control Problems  With Singular and Non–Singular  ...
On the Fractional Optimal Control Problems With Singular and Non–Singular ...
 
A SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONS
A SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONSA SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONS
A SURVEY OF MARKOV CHAIN MODELS IN LINGUISTICS APPLICATIONS
 
Hybrid model for forecasting space-time data with calendar variation effects
Hybrid model for forecasting space-time data with calendar variation effectsHybrid model for forecasting space-time data with calendar variation effects
Hybrid model for forecasting space-time data with calendar variation effects
 
A-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-Model
A-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-ModelA-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-Model
A-New-Quantile-Based-Fuzzy-Time-Series-Forecasting-Model
 
Paper 1 (rajesh singh)
Paper 1 (rajesh singh)Paper 1 (rajesh singh)
Paper 1 (rajesh singh)
 
Minimality and Equicontinuity of a Sequence of Maps in Iterative Way
Minimality and Equicontinuity of a Sequence of Maps in Iterative WayMinimality and Equicontinuity of a Sequence of Maps in Iterative Way
Minimality and Equicontinuity of a Sequence of Maps in Iterative Way
 
Forecasting With An Adaptive Control Algorithm
Forecasting With An Adaptive Control AlgorithmForecasting With An Adaptive Control Algorithm
Forecasting With An Adaptive Control Algorithm
 
Granger Causality Test: A Useful Descriptive Tool for Time Series Data
Granger Causality Test: A Useful Descriptive Tool for Time  Series DataGranger Causality Test: A Useful Descriptive Tool for Time  Series Data
Granger Causality Test: A Useful Descriptive Tool for Time Series Data
 
New directions in neuropsychological assessment: Augmenting neuropsychologica...
New directions in neuropsychological assessment: Augmenting neuropsychologica...New directions in neuropsychological assessment: Augmenting neuropsychologica...
New directions in neuropsychological assessment: Augmenting neuropsychologica...
 
Pushing the edge of the contemporary cognitive (CHC) theory: New directions ...
Pushing the edge of the contemporary cognitive (CHC) theory:  New directions ...Pushing the edge of the contemporary cognitive (CHC) theory:  New directions ...
Pushing the edge of the contemporary cognitive (CHC) theory: New directions ...
 

Viewers also liked

Results on order statistics from heterogeneous population
Results on order statistics from heterogeneous populationResults on order statistics from heterogeneous population
Results on order statistics from heterogeneous population
Birla Institute of Technology & Science - K.K. Birla Goa Campus
 
Concepts in order statistics and bayesian estimation
Concepts in order statistics and bayesian estimationConcepts in order statistics and bayesian estimation
Concepts in order statistics and bayesian estimation
Alexander Decker
 
Medians and order statistics
Medians and order statisticsMedians and order statistics
Medians and order statistics
Rajendran
 
Multivariate normal proof
Multivariate normal proofMultivariate normal proof
Multivariate normal proof
Cole Arora
 
A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...
A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...
A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...
IJRES Journal
 
Set Operations - Union Find and Bloom Filters
Set Operations - Union Find and Bloom FiltersSet Operations - Union Find and Bloom Filters
Set Operations - Union Find and Bloom Filters
Amrinder Arora
 
Target audience for music magazine
Target audience for music magazineTarget audience for music magazine
Target audience for music magazine
mataytayy
 
Medians and Order Statistics
Medians and Order StatisticsMedians and Order Statistics
Medians and Order Statistics
Hoa Nguyen
 
07 Analysis of Algorithms: Order Statistics
07 Analysis of Algorithms: Order Statistics07 Analysis of Algorithms: Order Statistics
07 Analysis of Algorithms: Order Statistics
Andres Mendez-Vazquez
 
Bayseian decision theory
Bayseian decision theoryBayseian decision theory
Bayseian decision theory
sia16
 
Parametric test
Parametric testParametric test
Parametric test
Chinnannan Periasamy
 
Berd 5-6
Berd 5-6Berd 5-6
Berd 5-6
PSIT kanpur
 
Nonparametric tests
Nonparametric testsNonparametric tests
Nonparametric tests
Arun Kumar
 
Parametric tests seminar
Parametric tests seminarParametric tests seminar
Parametric tests seminar
drdeepika87
 
Parametric and non parametric test
Parametric and non parametric testParametric and non parametric test
Parametric and non parametric test
Ajay Malpani
 
Non-Parametric Tests
Non-Parametric TestsNon-Parametric Tests
Non-Parametric Tests
Pratik Bhadange
 
Parametric vs Nonparametric Tests: When to use which
Parametric vs Nonparametric Tests: When to use whichParametric vs Nonparametric Tests: When to use which
Parametric vs Nonparametric Tests: When to use which
Gönenç Dalgıç
 
Non parametric tests
Non parametric testsNon parametric tests
Non parametric tests
Raghavendra Huchchannavar
 
DIstinguish between Parametric vs nonparametric test
 DIstinguish between Parametric vs nonparametric test DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test
sai prakash
 

Viewers also liked (19)

Results on order statistics from heterogeneous population
Results on order statistics from heterogeneous populationResults on order statistics from heterogeneous population
Results on order statistics from heterogeneous population
 
Concepts in order statistics and bayesian estimation
Concepts in order statistics and bayesian estimationConcepts in order statistics and bayesian estimation
Concepts in order statistics and bayesian estimation
 
Medians and order statistics
Medians and order statisticsMedians and order statistics
Medians and order statistics
 
Multivariate normal proof
Multivariate normal proofMultivariate normal proof
Multivariate normal proof
 
A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...
A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...
A Mathematical Model for the Hormonal Responses During Neurally Mediated Sync...
 
Set Operations - Union Find and Bloom Filters
Set Operations - Union Find and Bloom FiltersSet Operations - Union Find and Bloom Filters
Set Operations - Union Find and Bloom Filters
 
Target audience for music magazine
Target audience for music magazineTarget audience for music magazine
Target audience for music magazine
 
Medians and Order Statistics
Medians and Order StatisticsMedians and Order Statistics
Medians and Order Statistics
 
07 Analysis of Algorithms: Order Statistics
07 Analysis of Algorithms: Order Statistics07 Analysis of Algorithms: Order Statistics
07 Analysis of Algorithms: Order Statistics
 
Bayseian decision theory
Bayseian decision theoryBayseian decision theory
Bayseian decision theory
 
Parametric test
Parametric testParametric test
Parametric test
 
Berd 5-6
Berd 5-6Berd 5-6
Berd 5-6
 
Nonparametric tests
Nonparametric testsNonparametric tests
Nonparametric tests
 
Parametric tests seminar
Parametric tests seminarParametric tests seminar
Parametric tests seminar
 
Parametric and non parametric test
Parametric and non parametric testParametric and non parametric test
Parametric and non parametric test
 
Non-Parametric Tests
Non-Parametric TestsNon-Parametric Tests
Non-Parametric Tests
 
Parametric vs Nonparametric Tests: When to use which
Parametric vs Nonparametric Tests: When to use whichParametric vs Nonparametric Tests: When to use which
Parametric vs Nonparametric Tests: When to use which
 
Non parametric tests
Non parametric testsNon parametric tests
Non parametric tests
 
DIstinguish between Parametric vs nonparametric test
 DIstinguish between Parametric vs nonparametric test DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test
 

Similar to Time to Recruitment for a Single Grade Manpower System with Two Thresholds, Different Epochs for Inter-Decisions as an Order Statistics and Exits

25893434.pdf
25893434.pdf25893434.pdf
25893434.pdf
VilasPATIL85
 
Financial Time Series Analysis Based On Normalized Mutual Information Functions
Financial Time Series Analysis Based On Normalized Mutual Information FunctionsFinancial Time Series Analysis Based On Normalized Mutual Information Functions
Financial Time Series Analysis Based On Normalized Mutual Information Functions
IJCI JOURNAL
 
Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...
Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...
Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...
IOSRjournaljce
 
Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...
Alexander Decker
 
Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...
Alexander Decker
 
Finding the Extreme Values with some Application of Derivatives
Finding the Extreme Values with some Application of DerivativesFinding the Extreme Values with some Application of Derivatives
Finding the Extreme Values with some Application of Derivatives
ijtsrd
 
Investigations of certain estimators for modeling panel data under violations...
Investigations of certain estimators for modeling panel data under violations...Investigations of certain estimators for modeling panel data under violations...
Investigations of certain estimators for modeling panel data under violations...
Alexander Decker
 
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...
IJERA Editor
 
E026028036
E026028036E026028036
E026028036
inventionjournals
 
Improving predictability and performance by relating the number of events and...
Improving predictability and performance by relating the number of events and...Improving predictability and performance by relating the number of events and...
Improving predictability and performance by relating the number of events and...
Asoka Korale
 
ppt0320defenseday
ppt0320defensedayppt0320defenseday
ppt0320defenseday
Xi (Shay) Zhang, PhD
 
1-s2.0-S0047259X16300689-main (1).pdf
1-s2.0-S0047259X16300689-main (1).pdf1-s2.0-S0047259X16300689-main (1).pdf
1-s2.0-S0047259X16300689-main (1).pdf
shampy kamboj
 
Contingency Table Test, M. Asad Hayat, UET Taxila
Contingency Table Test, M. Asad Hayat, UET TaxilaContingency Table Test, M. Asad Hayat, UET Taxila
Contingency Table Test, M. Asad Hayat, UET Taxila
Muhammad Warraich
 
E026028036
E026028036E026028036
E026028036
inventionjournals
 
MCS 2011
MCS 2011MCS 2011
MCS 2011
Tiansong Wang
 
Normality_assumption_for_the_log_re.pdf
Normality_assumption_for_the_log_re.pdfNormality_assumption_for_the_log_re.pdf
Normality_assumption_for_the_log_re.pdf
Vasudha Singh
 
A Method for the Analysis of BehaviouralUncertainty in Evacu.docx
A Method for the Analysis of BehaviouralUncertainty in Evacu.docxA Method for the Analysis of BehaviouralUncertainty in Evacu.docx
A Method for the Analysis of BehaviouralUncertainty in Evacu.docx
ransayo
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
ijceronline
 
Logistic regression vs. logistic classifier. History of the confusion and the...
Logistic regression vs. logistic classifier. History of the confusion and the...Logistic regression vs. logistic classifier. History of the confusion and the...
Logistic regression vs. logistic classifier. History of the confusion and the...
Adrian Olszewski
 
General Manpower – Hyper Exponential Machine System with Exponential Producti...
General Manpower – Hyper Exponential Machine System with Exponential Producti...General Manpower – Hyper Exponential Machine System with Exponential Producti...
General Manpower – Hyper Exponential Machine System with Exponential Producti...
inventionjournals
 

Similar to Time to Recruitment for a Single Grade Manpower System with Two Thresholds, Different Epochs for Inter-Decisions as an Order Statistics and Exits (20)

25893434.pdf
25893434.pdf25893434.pdf
25893434.pdf
 
Financial Time Series Analysis Based On Normalized Mutual Information Functions
Financial Time Series Analysis Based On Normalized Mutual Information FunctionsFinancial Time Series Analysis Based On Normalized Mutual Information Functions
Financial Time Series Analysis Based On Normalized Mutual Information Functions
 
Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...
Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...
Value Based Decision Control: Preferences Portfolio Allocation, Winer and Col...
 
Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...
 
Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...Application of panel data to the effect of five (5) world development indicat...
Application of panel data to the effect of five (5) world development indicat...
 
Finding the Extreme Values with some Application of Derivatives
Finding the Extreme Values with some Application of DerivativesFinding the Extreme Values with some Application of Derivatives
Finding the Extreme Values with some Application of Derivatives
 
Investigations of certain estimators for modeling panel data under violations...
Investigations of certain estimators for modeling panel data under violations...Investigations of certain estimators for modeling panel data under violations...
Investigations of certain estimators for modeling panel data under violations...
 
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...
Transportation Problem with Pentagonal Intuitionistic Fuzzy Numbers Solved Us...
 
E026028036
E026028036E026028036
E026028036
 
Improving predictability and performance by relating the number of events and...
Improving predictability and performance by relating the number of events and...Improving predictability and performance by relating the number of events and...
Improving predictability and performance by relating the number of events and...
 
ppt0320defenseday
ppt0320defensedayppt0320defenseday
ppt0320defenseday
 
1-s2.0-S0047259X16300689-main (1).pdf
1-s2.0-S0047259X16300689-main (1).pdf1-s2.0-S0047259X16300689-main (1).pdf
1-s2.0-S0047259X16300689-main (1).pdf
 
Contingency Table Test, M. Asad Hayat, UET Taxila
Contingency Table Test, M. Asad Hayat, UET TaxilaContingency Table Test, M. Asad Hayat, UET Taxila
Contingency Table Test, M. Asad Hayat, UET Taxila
 
E026028036
E026028036E026028036
E026028036
 
MCS 2011
MCS 2011MCS 2011
MCS 2011
 
Normality_assumption_for_the_log_re.pdf
Normality_assumption_for_the_log_re.pdfNormality_assumption_for_the_log_re.pdf
Normality_assumption_for_the_log_re.pdf
 
A Method for the Analysis of BehaviouralUncertainty in Evacu.docx
A Method for the Analysis of BehaviouralUncertainty in Evacu.docxA Method for the Analysis of BehaviouralUncertainty in Evacu.docx
A Method for the Analysis of BehaviouralUncertainty in Evacu.docx
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
 
Logistic regression vs. logistic classifier. History of the confusion and the...
Logistic regression vs. logistic classifier. History of the confusion and the...Logistic regression vs. logistic classifier. History of the confusion and the...
Logistic regression vs. logistic classifier. History of the confusion and the...
 
General Manpower – Hyper Exponential Machine System with Exponential Producti...
General Manpower – Hyper Exponential Machine System with Exponential Producti...General Manpower – Hyper Exponential Machine System with Exponential Producti...
General Manpower – Hyper Exponential Machine System with Exponential Producti...
 

More from inventy

Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...
Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...
Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...
inventy
 
Copper Strip Corrossion Test in Various Aviation Fuels
Copper Strip Corrossion Test in Various Aviation FuelsCopper Strip Corrossion Test in Various Aviation Fuels
Copper Strip Corrossion Test in Various Aviation Fuels
inventy
 
Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...
Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...
Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...
inventy
 
Comparative Study of the Quality of Life, Quality of Work Life and Organisati...
Comparative Study of the Quality of Life, Quality of Work Life and Organisati...Comparative Study of the Quality of Life, Quality of Work Life and Organisati...
Comparative Study of the Quality of Life, Quality of Work Life and Organisati...
inventy
 
A Study of Automated Decision Making Systems
A Study of Automated Decision Making SystemsA Study of Automated Decision Making Systems
A Study of Automated Decision Making Systems
inventy
 
Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...
Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...
Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...
inventy
 
Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...
Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...
Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...
inventy
 
Application of Kennelly’model of Running Performances to Elite Endurance Runn...
Application of Kennelly’model of Running Performances to Elite Endurance Runn...Application of Kennelly’model of Running Performances to Elite Endurance Runn...
Application of Kennelly’model of Running Performances to Elite Endurance Runn...
inventy
 
Development and Application of a Failure Monitoring System by Using the Vibra...
Development and Application of a Failure Monitoring System by Using the Vibra...Development and Application of a Failure Monitoring System by Using the Vibra...
Development and Application of a Failure Monitoring System by Using the Vibra...
inventy
 
The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...
The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...
The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...
inventy
 
Size distribution and biometric relationships of little tunny Euthynnus allet...
Size distribution and biometric relationships of little tunny Euthynnus allet...Size distribution and biometric relationships of little tunny Euthynnus allet...
Size distribution and biometric relationships of little tunny Euthynnus allet...
inventy
 
Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...
Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...
Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...
inventy
 
Effect of Various External and Internal Factors on the Carrier Mobility in n-...
Effect of Various External and Internal Factors on the Carrier Mobility in n-...Effect of Various External and Internal Factors on the Carrier Mobility in n-...
Effect of Various External and Internal Factors on the Carrier Mobility in n-...
inventy
 
Transient flow analysis for horizontal axial upper-wind turbine
Transient flow analysis for horizontal axial upper-wind turbineTransient flow analysis for horizontal axial upper-wind turbine
Transient flow analysis for horizontal axial upper-wind turbine
inventy
 
Choice of Numerical Integration Method for Wind Time History Analysis of Tall...
Choice of Numerical Integration Method for Wind Time History Analysis of Tall...Choice of Numerical Integration Method for Wind Time History Analysis of Tall...
Choice of Numerical Integration Method for Wind Time History Analysis of Tall...
inventy
 
Impacts of Demand Side Management on System Reliability Evaluation
Impacts of Demand Side Management on System Reliability EvaluationImpacts of Demand Side Management on System Reliability Evaluation
Impacts of Demand Side Management on System Reliability Evaluation
inventy
 
Reliability Evaluation of Riyadh System Incorporating Renewable Generation
Reliability Evaluation of Riyadh System Incorporating Renewable GenerationReliability Evaluation of Riyadh System Incorporating Renewable Generation
Reliability Evaluation of Riyadh System Incorporating Renewable Generation
inventy
 
The effect of reduced pressure acetylene plasma treatment on physical charact...
The effect of reduced pressure acetylene plasma treatment on physical charact...The effect of reduced pressure acetylene plasma treatment on physical charact...
The effect of reduced pressure acetylene plasma treatment on physical charact...
inventy
 
Experimental Investigation of Mini Cooler cum Freezer
Experimental Investigation of Mini Cooler cum FreezerExperimental Investigation of Mini Cooler cum Freezer
Experimental Investigation of Mini Cooler cum Freezer
inventy
 
Growth and Magnetic properties of MnGeP2 thin films
Growth and Magnetic properties of MnGeP2 thin filmsGrowth and Magnetic properties of MnGeP2 thin films
Growth and Magnetic properties of MnGeP2 thin films
inventy
 

More from inventy (20)

Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...
Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...
Experimental Investigation of a Household Refrigerator Using Evaporative-Cool...
 
Copper Strip Corrossion Test in Various Aviation Fuels
Copper Strip Corrossion Test in Various Aviation FuelsCopper Strip Corrossion Test in Various Aviation Fuels
Copper Strip Corrossion Test in Various Aviation Fuels
 
Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...
Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...
Additional Conservation Laws for Two-Velocity Hydrodynamics Equations with th...
 
Comparative Study of the Quality of Life, Quality of Work Life and Organisati...
Comparative Study of the Quality of Life, Quality of Work Life and Organisati...Comparative Study of the Quality of Life, Quality of Work Life and Organisati...
Comparative Study of the Quality of Life, Quality of Work Life and Organisati...
 
A Study of Automated Decision Making Systems
A Study of Automated Decision Making SystemsA Study of Automated Decision Making Systems
A Study of Automated Decision Making Systems
 
Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...
Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...
Crystallization of L-Glutamic Acid: Mechanism of Heterogeneous β -Form Nuclea...
 
Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...
Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...
Evaluation of Damage by the Reliability of the Traction Test on Polymer Test ...
 
Application of Kennelly’model of Running Performances to Elite Endurance Runn...
Application of Kennelly’model of Running Performances to Elite Endurance Runn...Application of Kennelly’model of Running Performances to Elite Endurance Runn...
Application of Kennelly’model of Running Performances to Elite Endurance Runn...
 
Development and Application of a Failure Monitoring System by Using the Vibra...
Development and Application of a Failure Monitoring System by Using the Vibra...Development and Application of a Failure Monitoring System by Using the Vibra...
Development and Application of a Failure Monitoring System by Using the Vibra...
 
The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...
The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...
The Management of Protected Areas in Serengeti Ecosystem: A Case Study of Iko...
 
Size distribution and biometric relationships of little tunny Euthynnus allet...
Size distribution and biometric relationships of little tunny Euthynnus allet...Size distribution and biometric relationships of little tunny Euthynnus allet...
Size distribution and biometric relationships of little tunny Euthynnus allet...
 
Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...
Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...
Removal of Chromium (VI) From Aqueous Solutions Using Discarded Solanum Tuber...
 
Effect of Various External and Internal Factors on the Carrier Mobility in n-...
Effect of Various External and Internal Factors on the Carrier Mobility in n-...Effect of Various External and Internal Factors on the Carrier Mobility in n-...
Effect of Various External and Internal Factors on the Carrier Mobility in n-...
 
Transient flow analysis for horizontal axial upper-wind turbine
Transient flow analysis for horizontal axial upper-wind turbineTransient flow analysis for horizontal axial upper-wind turbine
Transient flow analysis for horizontal axial upper-wind turbine
 
Choice of Numerical Integration Method for Wind Time History Analysis of Tall...
Choice of Numerical Integration Method for Wind Time History Analysis of Tall...Choice of Numerical Integration Method for Wind Time History Analysis of Tall...
Choice of Numerical Integration Method for Wind Time History Analysis of Tall...
 
Impacts of Demand Side Management on System Reliability Evaluation
Impacts of Demand Side Management on System Reliability EvaluationImpacts of Demand Side Management on System Reliability Evaluation
Impacts of Demand Side Management on System Reliability Evaluation
 
Reliability Evaluation of Riyadh System Incorporating Renewable Generation
Reliability Evaluation of Riyadh System Incorporating Renewable GenerationReliability Evaluation of Riyadh System Incorporating Renewable Generation
Reliability Evaluation of Riyadh System Incorporating Renewable Generation
 
The effect of reduced pressure acetylene plasma treatment on physical charact...
The effect of reduced pressure acetylene plasma treatment on physical charact...The effect of reduced pressure acetylene plasma treatment on physical charact...
The effect of reduced pressure acetylene plasma treatment on physical charact...
 
Experimental Investigation of Mini Cooler cum Freezer
Experimental Investigation of Mini Cooler cum FreezerExperimental Investigation of Mini Cooler cum Freezer
Experimental Investigation of Mini Cooler cum Freezer
 
Growth and Magnetic properties of MnGeP2 thin films
Growth and Magnetic properties of MnGeP2 thin filmsGrowth and Magnetic properties of MnGeP2 thin films
Growth and Magnetic properties of MnGeP2 thin films
 

Recently uploaded

HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
panagenda
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
Fwdays
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
Zilliz
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
Jakub Marek
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
saastr
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Zilliz
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
MichaelKnudsen27
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
DianaGray10
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
Tatiana Kojar
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Edge AI and Vision Alliance
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
saastr
 
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptxPrinciple of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
BibashShahi
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
Pablo Gómez Abajo
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Jason Yip
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
DanBrown980551
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
c5vrf27qcz
 
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation ParametersEssentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
Safe Software
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
ScyllaDB
 

Recently uploaded (20)

HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
 
"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota"Choosing proper type of scaling", Olena Syrota
"Choosing proper type of scaling", Olena Syrota
 
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and MilvusGenerating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
 
Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)Main news related to the CCS TSI 2023 (2023/1695)
Main news related to the CCS TSI 2023 (2023/1695)
 
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
 
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup SlidesProgramming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
 
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptxNordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
 
What is an RPA CoE? Session 1 – CoE Vision
What is an RPA CoE?  Session 1 – CoE VisionWhat is an RPA CoE?  Session 1 – CoE Vision
What is an RPA CoE? Session 1 – CoE Vision
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
Skybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoptionSkybuffer SAM4U tool for SAP license adoption
Skybuffer SAM4U tool for SAP license adoption
 
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
9 CEO's who hit $100m ARR Share Their Top Growth Tactics Nathan Latka, Founde...
 
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptxPrinciple of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
 
Mutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented ChatbotsMutation Testing for Task-Oriented Chatbots
Mutation Testing for Task-Oriented Chatbots
 
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
 
Y-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PPY-Combinator seed pitch deck template PP
Y-Combinator seed pitch deck template PP
 
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation ParametersEssentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
 
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyFreshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
 

Time to Recruitment for a Single Grade Manpower System with Two Thresholds, Different Epochs for Inter-Decisions as an Order Statistics and Exits

  • 1. Research Inventy: International Journal Of Engineering And Science Vol.5, Issue 6 (June 2015), PP 24-28 Issn (e): 2278-4721, Issn (p):2319-6483, www.researchinventy.com 24 Time to Recruitment for a Single Grade Manpower System with Two Thresholds, Different Epochs for Inter-Decisions as an Order Statistics and Exits G. Ravichandran1 ; A. Srinivasan2 1 Assistant Professor in Mathematics, TRP Engineering College (SRM GROUP), Irungalur, Trichy- 621 105, Tamil Nadu, India, 2 Professor Emeritus, PG & Research Department of Mathematics, Bishop Heber College, Trichy-620 017, Tamil Nadu, India, ABSTRACT: In this paper, the problem of time to recruitment is studied using a univariate policy of recruitment involving two thresholds for a single grade manpower system with attrition generated by its policy decisions. Assuming that the policy decisions and exits occur at different epochs, a stochastic model is constructed and the variance of the time to recruitment is obtained when the inter-policy decision times and inter- exit times form an order statistics and an ordinary renewal process respectively. The analytical results are numerically illustrated with relevant findings by assuming specific distributions. Keywords: Single grade manpower system; decision and exit epochs; order statistics; ordinary renewal process; univariate policy of recruitment with two thresholds and variance of the time to recruitment. I. INTRODUCTION Attrition is a common phenomenon in many organizations. This leads to the depletion of manpower. Recruitment on every occasion of depletion of manpower is not advisable since every recruitment involves cost. Hence the cumulative depletion of manpower is permitted till it reaches a level, called the threshold. If the total loss of manpower exceeds this threshold, the activities in the organization will be affected and hence recruitment becomes necessary. In [1, 2, 3] the authors have discussed the manpower planning models using Markovian and renewal theoretic methods. In [4] the author has studied the problem of time to recruitment for a single grade manpower system and obtained the variance of the time to recruitment when the loss of manpower forms a sequence of independent and identically distributed random variables, the inter- decision times form a geometric process and the mandatory breakdown threshold for the cumulative loss of manpower is an exponential random variable by using univariate cum policy of recruitment. In [5] the author has studied the work in [4] using univariate and bivariate policies of recruitment both for the exponential mandatory threshold and for the one whose distribution has the SCBZ property. In [6] the author has initiated the study of the problem of time to recruitment for a single grade manpower system by incorporating alertness in the event of cumulative loss of manpower due to attrition crossing the threshold, by considering optional and mandatory thresholds for the cumulative loss of manpower in this manpower system. In [7] the authors have studied this problem when inter- decision times form an order statistics. In all the above cited work, it is assumed that attrition takes place instantaneously at decision epochs. This assumption is not realistic as the actual attrition will take place only at exit points which may or may not coincide with decision points. This aspect is taken into account for the first time in [8] and the variance of time to recruitment is obtained when the inter-decision times and exit times are independent and identically distributed exponential random variables using univariate policy for recruitment and Laplace transform in the analysis. In [9, 11, 12] the authors have extended their work in [8] when the inter- decision times form a sequence of exchangeable and constantly correlated exponential random variables, geometric process and order statistics respectively. Recently, in [13, 14, 15] the authors have studied the work in [8, 9, 11] respectively by considering optional and mandatory thresholds which is a variation from the work of [6] in the context of considering non-instantaneous exits at decision epochs. In the present paper, for a single grade manpower system, a mathematical model is constructed in which attrition due to policy decisions takes place at exit points and there are optional and mandatory thresholds as control limits for the cumulative loss of manpower. A univariate policy of recruitment based on shock model approach is used to determine the variance of time to recruitment when the system has different epochs for policy decisions and exits and the inter- exit times form an ordinary renewal process. The present paper studies the research work in [15] when the inter- policy decision times form an order statistics.
  • 2. Time to Recruitment for a Single Grade Manpower System with… 25 II. MODEL DESCRIPTION Consider an organization taking decisions at random epochs in (0, ∞) and at every decision making epoch a random number of persons quit the organization. There is an associated loss of manpower if a person quits. It is assumed that the loss of manpower is linear and cumulative. Let 𝑋𝑖 be the continuous random variable representing the amount of depletion of manpower (loss of man hours) caused at the 𝑖 𝑡ℎ exit point and kS be the total loss of manpower up to the first k exit points. It is assumed that 𝑋𝑖’s are independent and identically distributed random variables with probability density function m(.), distribution function M(.) and mean )0( 1   . Let kU be the continuous random variable representing the time between the (k-1)th and kth policy decisions. It is assumed that sUk ' are independent and identically distributed random variables with distribution F(.) and probability density function (.)f .Let (.))( jaF and (.))( jaf be the distribution and the probability density function of the th j order statistic ( rj ,...,2,1 ) selected from the sample of size r from the population   1kkU . Let 𝑊𝑖 be the continuous random variable representing the time between the (i-1)th and ith exit points. It is assumed that iW ’s are independent and identically distributed random variables with probability density function g(.), probability distribution function G(.). Let )(tNe be the number of exit points lying in (0, t]. Let Y be the optional threshold level and Z the mandatory threshold level (Y< Z) for the cumulative depletion of manpower in the organization with probability density function h(.) and distribution function H(.). Let p be the probability that the organization is not going for recruitment when optional threshold is exceeded by the cumulative loss of manpower. Let q be the probability that every policy decision has exit of personnel. As q=0 corresponds to the case where exits are impossible, it is assumed that q  0. Let T be the random variable denoting the time to recruitment with probability distribution function L(.), density function l (.), mean E(T) and variance V(T). Let (.)a be the Laplace transform of a(.). The univariate CUM policy of recruitment employed in this paper is stated as follows: Recruitment is done whenever the cumulative loss of manpower in the organization exceeds the mandatory threshold. The organization may or may not go for recruitment if the cumulative loss of manpower exceeds the optional threshold. III. MAIN RESULT As in [15], we get )()(])([)(])([)( 00 ZSPYSPktNPpYSPktNPtTP kk k ek k e       (1) )()()()()( 1 1k 1 1k 1 1k                  k k k k k k abtGabbtGbpatGatL (2) and              )()1(1 )( )()1(1 )( )()1(1 )( )( sfqab sfqab sfqb sfqb p sfqa sfqa sl (3) where ababbbaa  1,1,1 (4) Let rUUU ,...,, 21 be arranged in increasing order of magnitude so that we have the sequence as )()2()1( ,...,, rUUU , here r...,,2,1j,...,2,1 r are called the order statistics and with )1(U as the first order statistic and )(rU the rth order statistic and the random variables )()2()1( ,...,, rUUU are not independent. The probability density function of the th j order statistic is given by
  • 3. Time to Recruitment for a Single Grade Manpower System with… 26 jrj ja tFtftF j r jtf         )](1[)()]([)( 1 )( , (5) where it is assumed that t etF   1)( . Case (i): )()( )1( tftf a In this case, from (3) and (5), we get               )()1(1 )( )()1(1 )( )()1(1 )( )( )1( )1( )1( )1( )1( )1( sfqab sfqab sfqb sfqb p sfqa sfqa sl a a a a a a , (6) where sr r sf a     )()1( (7) Substituting (7) in (6) and on simplification, we get                    rqabsr qrab rqbsr qrb p rqasr rqa sl )1()()1()()1()( )( (8)             qrabqrb p qra sl ds d TE s 111 )()( 0 (9) and     2 22222 2 02 2 1 )( 1 )()()( 1 )( 2 )()()(                 ab p b p aqrab p b p aqr TEsl ds d TV s  (10) where abba ,, are given by (4). Case (ii): )()( )( tftf ra . In this case, from (3) and (5), we get               )()1(1 )( )()1(1 )( )()1(1 )( )( )( )( )( )( )( )( sfqab sfqab sfqb sfqb p sfqa sfqa sl ra ra ra ra ra ra (11) where s)s)...(s)(2( ! )( r )(     r r sf ra (12) Substituting (12) in (11) and on simplification, we get              r r r r r r rqabC rqab rqbC rqb p rqaC rqa sl       !)1( ! !)1( ! !)1( ! )( (13) where   r s rrC  !Cands)s)...(s)(2( 0   (14)
  • 4. Time to Recruitment for a Single Grade Manpower System with… 27 Proceeding as in case(i), we get         111 )( 1 ab p b p ajq TE r j (15) and                               ab p b p ajqab qabp b qbp a qa jq TV r j r j 111 )( )2( )( )2( )( 21 )( 1 )( 1 22222 2 1 2                    22 1 2 111 ab p b p ajq r j (16) where abba ,, are given by (4). Equations (9), (10) and (15), (16) give the mean and variance on the time to recruitment for Cases (i) and (ii) respectively. Remark: Computation of )(TV for extended exponential and SCBZ Property possessing thresholds is similar as their distribution will have just additional exponential terms. Note: (i)When p=0 and r =1, our results in Case (i) agree with the results in [8] for the manpower system having only one threshold which is the mandatory threshold. (ii)When p=0 and 1r our results in Cases (i) and (ii) agree with the results in [12] for the manpower system having only one threshold which is the mandatory threshold. (iii)When q=1and r =1, our results in Case (i) agree with the results in [6] for the manpower system having certain instantaneous exits in the decision epochs. (iv)When r =1, 1,0  qp our results in Case (i) agree with the results in [13] for the manpower system having inter-decision times as an ordinary renewal process. IV. NUMERICAL ILLUSTRATION The mean and variance of time to recruitment is numerically illustrated by varying one parameter and keeping other parameters fixed. The effect of the nodal parameters ,  , r on the performance measures is shown in the following table. In the computations, it is assumed that 5.0,2.0,09.0,06.0 21  pq . Table: Effect of nodal parameters on E(T) and V(T)   r Case(i) Case(ii) E(T) V(T) E(T) V(T) 0.2 1 5 4.8795 19.5924 55.6992 2.4614x103 0.2 1 6 4.0662 13.6058 59.7491 2.8277x103 0.2 1 7 3.4853 9.9961 60.7982 2.9271x103 0.2 1 8 3.0497 7.6533 63.8479 3.2247x103 0.2 2 5 2.4397 4.8981 27.8496 615.3589 0.2 3 5 1.6265 2.1769 18.5664 273.4928 0.2 4 5 1.2199 1.2245 13.9248 153.8397 0.2 5 5 0.9759 0.7837 11.1398 98.4574 0.3 1 5 6.7738 37.1331 77.3230 4.7115x103 0.4 1 5 8.6656 60.2258 98.9184 7.6851x103 0.5 1 5 10.5564 88.8727 120.5011 1.1382x104 0.6 1 5 12.4465 123.0743 142.0773 1.5803x104
  • 5. Time to Recruitment for a Single Grade Manpower System with… 28 V. FINDINGS From the above tables, the following observations are presented which agree with reality. 1. When α increases and keeping all the other parameters fixed, the average loss of manpower increases. Therefore the mean and variance of time to recruitment increase. 2. As λ increases, on the average, the inter-decision time decreases and consequently the mean and variance of time to recruitment decrease when the other parameters are fixed. 3. When r increases and keeping all the other parameters fixed, the mean and variance of the time to recruitment decrease in Case (i) and increase in Case (ii). VI. CONCLUSION The models discussed in this paper are found to be more realistic and new in the context of considering (i) separate points (exit points) on the time axis for attrition, thereby removing a severe limitation on instantaneous attrition at decision epochs (ii) associating a probability for any decision to have exit points and (iii) provision of optional and mandatory thresholds. From the organization’s point of view, our models are more suitable than the corresponding models with instantaneous attrition at decision epochs, as the provision of exit points at which attrition actually takes place, postpone the time to recruitment. REFERENCES [1] D. J. Bartholomew, Stochastic model for social processes (New York, John Wiley and Sons, 1973). [2] D. J. Bartholomew and F. Andrew Forbes, Statistical techniques for manpower planning (New York, John Wiley and Sons, 1979). [3] R. C. Grinold and K.T. Marshall, Manpower planning models (New York, North-Holland, 1977). [4] A. Muthaiyan, A study on stochastic models in manpower planning, doctoral diss., Bharathidasan University, Tiruchirappalli, 2010. [5] K. P. Uma, A study on manpower models with univariate and bivariate policies of recruitment, doctoral diss., Avinashilingam University for Women, Coimbatore, 2010. [6] J. B. Esther Clara, Contributions to the study on some stochastic models in manpower planning, doctoral diss., Bharathidasan University, Tiruchirappalli, 2012. [7] J. Sridharan, A. Saivarajan and A. Srinivasan, Expected time to recruitment in a single graded manpower system with two thresholds , Journal of Advances in Mathematics, 7(1), 2014, 1147 – 1157. [8] A. Devi and A. Srinivasan, Variance of time to recruitment for single grade manpower system with different epochs for decisions and exits, International Journal of Research in Mathematics and Computations, 2(1), 2014, 23 – 27. [9] A. Devi and A. Srinivasan, Variance of time to recruitment for single grade manpower system with different epochs for decisions and exits having correlated inter- decision times, Annals of Pure and Applied Mathematics, 6(2), 2014, 185 –190. [10] J. Medhi, Stochastic processes (New Delhi, Wiley Eastern, second edition, 1994). [11] A. Devi and A. Srinivasan, A stochastic model for time to recruitment in a single grade manpower system with different epochs for decisions and exits having inter- decision times as geometric process, Second International Conference on Business Analytic and Intelligence, ( ICBAI ), 2014. [12] A. Devi and A. Srinivasan, Expected time to recruitment for a single grade manpower system with different epochs for decisions and exits having inter- decision times as an order statistics, Mathematical Sciences International Research Journal, 3(2), 2014, 887 – 890. [13] G. Ravichandran and A. Srinivasan, Variance of time to recruitment for a single grade manpower system with two thresholds having different epochs for decisions and exits, Indian Journal of Applied Research, 5(1), 2015, 60 – 64. [14] G. Ravichandran and A. Srinivasan, Time to recruitment for a single grade manpower system with two thresholds, different epochs for exits and correlated inter-decisions, International Research Journal of Natural and Applied Sciences, 2(2), 2015, 129 – 138. [15] G. Ravichandran and A. Srinivasan, Time to recruitment for a single grade manpower system with two thresholds, different epochs for exits and geometric inter-decisions, IOSR Journal of Mathematics, 11(2), Ver. III, 2015, 29 – 32.