PROFIT ANALYSIS OF A CABLE MANUFACTURING PLANT PORTRAYING THE WINTER OPERATING STRATEGY

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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 9, Issue 11, November 2018, pp. 370–381, Article ID: IJMET_09_11_037
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=11
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
PROFIT ANALYSIS OF A CABLE
MANUFACTURING PLANT PORTRAYING THE
WINTER OPERATING STRATEGY
Taj SZ and Rizwan SM
Department of Mathematics and Statistics, National University of Science and Technology,
Sultanate of Oman
Alkali BM and Harrison DK
Department of Mechanical Engineering, Glasgow Caledonian University, Scotland, UK
Taneja G
Department of Mathematics, Maharshi Dayanand University, India
ABSTRACT
This paper presents the profit analysis of a plant, manufacturing electrical cables.
The reliability model portrays specific season based operational strategy adopted to
address demand based production of the cables. During the winter season, the plant
operates for 16 hours followed by 8 hours rest period for the machines. Real maintenance
data of the plant are used for estimating optimized reliability indices such as mean time
to plant failure, availability of the plant, expected number of repairs, expected busy period
of the repairman and overall profit of the plant. Semi-Markov processes and regenerative
point techniques are used to carry out the analysis. Simulated results are shown to
demonstrate the effect of varying failure rate on the overall profit with respect to revenue
per unit up time.
Keywords: Cable Plant, Regenerative Processes, Reliability, Semi-Markov Processes.
Cite this Article Taj SZ,Rizwan SM, Alkali BM, Harrison DK and Taneja G, Profit
Analysis of A Cable Manufacturing Plant Portraying the Winter Operating Strategy,
International Journal of Mechanical Engineering and Technology, 9(11), 2018, pp. 370–
381.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=11
1. INTRODUCTION
Detailed analysis of various complex industrial systems operating under different conditions and
assumptions has been widely discussed by a number of researchers. Tuteja et al. [1-3] performed
cost-benefit analysis of two-unit system with different types of standby, failures and repairman.
Rizwan et al. [4-9] carried out reliability analysis of single-unit PLC system with hot standby;
waste water treatment plant with inspection; and two-unit system with various categories of
repairman. Mathew et al. [10-16] estimated important reliability indices of single-unit and two-

Taj SZ,Rizwan SM, Alkali BM, Harrison DK and Taneja G
unit CC plant wherein different installation capacities and maintenance policies were considered.
Padmavathi et al. [17-22] extensively analysed desalination plant focussing on major and minor
failure, emergency shutdown, online repair, and priority to repair over maintenance. Rizwan et
al. [23-24] extended the work for reliability analysis of desalination plant with season based
shutdown and repair/maintenance on FCFS basis. Taneja et al. [25-26] discussed profit analysis
of system with varying demand. Yaqoob Al Rahbi et al. [27-29] worked on the reliability and
maintainability of three different systems in the aluminium industry. Taj et al. [30-34] studied the
performance of single-unit, two-unit and three-unit subsystems of a cable plant considering
various maintenance categories and priority to repair over preventive maintenance. Recently, Taj
et al. [35] presented performance and cost-benefit analysis of a cable plant portraying the summer
operating strategy based on operating the machines for 24 hours without rest. There is a potential
scope of analysing the winter operating strategy of the cable plant under consideration.
Thus, this paper presents the profit analysis of a cable plant based on 16 hours of operation
followed by 8 hours rest period for the machines, during the winter season. Real maintenance
data of the plant are used for estimating optimized reliability indices such as mean time to plant
failure, availability of the plant, expected number of repairs, expected busy period of the
repairman and overall profit of the plant. Semi-Markov processes [36] and regenerative point
techniques [37] are used to carry out the analysis. Simulated results are shown to demonstrate the
effect of varying failure rate on the overall profit with respect to revenue per unit up time.
2. NOTATIONS
Si state i
pdf probability density function
cdf cumulative distribution function
A estimated failure rate for subsystem-A
B estimated failure rate for subsystem-B
C estimated failure rate for subsystem-C
D estimated failure rate for subsystem-D
E estimated failure rate for subsystem-E
estimated rate of transition from operating state to rest state
estimated rate of transition from rest state to operating state
gA(t) pdf of repair times for subsystem-A
gB(t) pdf of repair times for subsystem-B
gC(t) pdf of repair times for subsystem-C
gD(t) pdf of repair times for subsystem-D
gE(t) pdf of repair times for subsystem-E
A estimated repair rate for subsystem-A
B estimated repair rate for subsystem-B
C estimated repair rate for subsystem-C
D estimated repair rate for subsystem-D
E estimated repair rate for subsystem-E
Qij cdf from Si to Sj
qij pdf from Si to Sj
© Laplace convolution
Laplace Stieltje’s convolution

Profit Analysis of A Cable Manufacturing Plant Portraying the Winter Operating Strategy
* Laplace transform
** Laplace Stieltje’s transform
MTPF mean time to plant failure
A availability of the plant
R expected number of repairs
B expected busy period of the repairman
3. MODEL DESCRIPTION AND ASSUMPTIONS
Following operating conditions and assumptions are considered:
• The plant consists of five subsystems (A, B, C, D and E) operating in series.
• During winter season, the plant operates for 16 hours followed by 8 hours rest period
for the machines.
• If a particular subsystem fails, the succeeding subsystems enter into the down state
whereas the preceding subsystems continue to operate.
• The entire plant enters into the failed state once the first subsystem of the plant,
arranged in series, fails.
• Repair is carried out upon failure.
• Repair work is completed before the entire plant enters into the rest state.
• Repair rates are taken as arbitrary.
• Failure rates are taken as exponential.
Transition states of the plant are described below:
State 0 (S0): all subsystems are operational
State 1 (S1): A is under repair; B, C, D and E are down
State 2 (S2): A is operational; B is under repair; C, D and E are down
State 3 (S3): A, B are operational; C is under repair; D and E are down
State 4 (S4): A, B and C are operational; D is under repair; E is down
State 5 (S5): A, B, C and D are operational; E is under repair
State 6 (S6): A is waiting for repair; B is under repair; C, D and E are down
State 7 (S7): A is waiting for repair; B and C are down; D is under repair; E is down
State 8 (S8): A is operational; B is waiting for repair; C is down; D is under repair; E is down
State 9 (S9): A is operational; B is waiting for repair; C and D are down; E is under repair
State 10 (S10): A, B and C are operational; D is waiting for repair; E is under repair
State 11 (S11): entire plant is at rest
S0, S1, S2, S3, S4, S5 and S11 are regenerative states. S6, S7, S8, S9 and S10 are non-regenerative
states. S1, S6 and S7 are failed states.
Rates of transition from Si to Sj are given in Table-1. Note that, 0 stands for no transition to the
mentioned state.

Table 1 Rates of transition
Sj
Si
S0 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11
S0 0 A B C D E 0 0 0 0 0
S1 gA(t) 0 0 0 0 0 0 0 0 0 0 0
S2 gB(t) 0 0 0 0 0 A 0 0 0 0 0
S3 gC(t) 0 0 0 0 0 0 0 0 0 0 0
S4 gD(t) 0 0 0 0 0 0 A B 0 0 0
S5 gE(t) 0 0 0 0 0 0 0 0 B D 0
S6 0 gB(t) 0 0 0 0 0 0 0 0 0 0
S7 0 gD(t) 0 0 0 0 0 0 0 0 0 0
S8 0 0 gD(t) 0 0 0 0 0 0 0 0 0
S9 0 0 gE(t) 0 0 0 0 0 0 0 0 0
S10 0 0 0 0 gE(t) 0 0 0 0 0 0 0
S11 0 0 0 0 0 0 0 0 0 0 0
For non-regenerative states (S6 , S7 , S8 , S9 and S10)
S2 to S1 via S6 dQ t = λ e ©1 g t dt
S4 to S1 via S7 dQ t = λ e ! " ©1 g# t dt
S4 to S2 via S8 dQ$
t = λ e ! " ©1 g# t dt
S5 to S2 via S9 dQ%
&
t = λ e "! ' ©1 g( t dt
S5 to S4 via S10 dQ% t = λ#e "! ' ©1 g( t dt
Estimated values of various rates of transition are given in Table-2.
Table 2 Estimated values of rates of transition
S. No. Rate (/hr) Estimated value (/hr)
1 A, failure rate for subsystem-A 0.0054
2 B, failure rate for subsystem-B 0.0076
3 C, failure rate for subsystem-C 0.0034
4 D, failure rate for subsystem-D 0.0060
5 E, failure rate for subsystem-E 0.0054
6
, rate of transition from
operating state to rest state
0.0625
7
, rate of transition from rest
state to operating state
0.1250
8 A, repair rate for subsystem-A 0.1890
9 B, repair rate for subsystem-B 0.1696
10 C, repair rate for subsystem-C 0.1890
11 D, repair rate for subsystem-D 0.1500
12 E, repair rate for subsystem-E 0.1936

4. TRANSITION PROBABILITIES AND MEAN SOJOURN TIMES
Using the definition [15] of transition probabilities q*+, we get:
dQ t = λ e dt (1)
dQ t = λ e dt (2)
dQ , t = λ-e dt (3)
dQ t = λ#e dt (4)
dQ % t = λ(e dt (5)
dQ , t = γe dt (6)
dQ t = g t dt (7)
dQ t = e g t dt (8)
dQ t = λ e G1111 t dt (9)
dQ t = λ e ©1 g t dt (10)
dQ, t = g- t dt (11)
dQ t = e ! " g# t dt (12)
dQ t = λ e ! " G#
1111 t dt (13)
dQ t = λ e ! " ©1 g# t dt (14)
dQ$
t = λ e ! " ©1 g# t dt (15)
dQ% t = e "! ' g( t dt (16)
dQ%
&
t = λ e "! ' ©1 g( t dt (17)
dQ% t = λ#e "! ' ©1 g( t dt (18)
dQ , t = σe 3
dt (19)
here, λ = λ + λ + λ- + λ# + λ( + γ
Using the definition [15] of non zero elements p*+, we get:
p = (20)
p = "
(21)
p , = 6
(22)
p = '
(23)
p % = 7
(24)
p , =
8
(25)
p = g ∗
0 (26)
p = g ∗
λ (27)
p = 1 − g ∗
λ (28)
p = 1 − g ∗
λ (29
p, = g-
∗
0 (30)
p = g#
∗
λ + λ (31)
p = ! "
<1 − g#
∗
λ + λ = (32)

p =
! "
<1 − g#
∗
λ + λ = (33)
p$
= "
! "
<1 − g#
∗
λ + λ = (34)
p% = g(
∗
λ + λ# (35)
p%
&
= "
"! '
<1 − g(
∗
λ + λ# = (36)
p% = '
"! '
<1 − g(
∗
λ + λ# = (37)
p , = 1 (38)
Following relations can easily be verified:
p + p + p , + p + p % + p , = 1 (39)
p = 1 (40)
p + p = 1 (41)
p + p = 1 (42)
p, = 1 (43)
p + p + p$
= 1 (44)
p + p + p$
= 1 (45)
p% + p%
&
+ p% = 1 (46)
p , = 1 (47)
Using the definition [15] of mean sojourn time μ*, we get:
μ = (48)
μ = ? G1111 t dt
@
(49)
μ = ? e G1111 t dt
@
(50)
μ, = ? G-
1111 t dt
@
(51)
μ = ? e ! " G#
1111 t dt
@
(52)
μ% = ? e "! ' G(
1111 t dt
@
(53)
μ = 3
(54)
Using the definition [15] of unconditional mean time m*+, following relations can easily be
verified:
m + m + m , + m + m % + m , = μ (55)
m = μ (56)
m + m = μ (57)
m + m = k (say) (58)
m, = μ, (59)
m + m + m$
= k (say) (60)
m + m + m$
= k, (say) (61)
m% + m%
&
+ m% = k (say) (62)
m , = μ (63)

5. MEAN TIME TO PLANT FAILURE
Using the definition [15] of ϕ* t and applying simple probabilistic arguments, we get:
ϕ t = Q t +Q t ϕ t +Q , t ϕ, t +Q t ϕ t
+Q % t ϕ% t +Q , t ϕ t (64)
ϕ t = Q t ϕ t +Q t (65)
ϕ, t = Q, t ϕ t (66)
ϕ t = Q t ϕ t +Q t +Q$
t ϕ t (67)
ϕ% t = Q% t ϕ t +Q%
&
t ϕ t +Q% t ϕ t (68)
ϕ t = Q , t ϕ t (69)
Taking Laplace Stieltjes transform of above relations and solving for ϕ ∗∗
s , we get:
ϕ ∗∗
s =
E F
# F
(70)
MTPF when the plant started at the beginning of state 0 is given as:
MTPF = lim
F→
JK
∗∗
F
F
=
E
#
(71)
where,
N = μ + μ <p + p %p%
&
+ p$
p + p %p% = + p ,μ,
+p , μ + k p + p %p% + p %k
D = 1 − p , − p , − p + p p$
p + p %p%
−p p + p %p%
&
− p %p%
6. AVAILABILITY OF THE PLANT
Using the definition [15] of A* t and applying simple probabilistic arguments, we get:
A t = M t +q t ©A t +q t ©A t +q , t ©A, t
+q t ©A t +q % t ©A% t +q , t ©A t (72)
A t = q t ©A t (73)
A t = q t ©A t +q t ©A t (74)
A, t = q, t ©A t (75)
A t = q t ©A t +q t ©A t +q$
t ©A t (76)
A% t = q% t ©A t +q%
&
t ©A t +q% t ©A t (77)
A t = q , t ©A t (78)
here, M t = e
Taking Laplace transform of above equations and solving for A ∗
s , we get:
A ∗
s =
EO F
#O F
(79)
In steady state, availability of the plant is given as:
A = lim
F→
s A ∗
s =
EO
#O
(80)
where,
N =
1
λ
D = μ + μ <p + p + p %p% p + p p$
+ p p + p %p%
& =
+p ,μ, + p , μ + k, p + p %p% + p %k

+[k <p$
p + p %p% + p + p %p%
& =]
7. EXPECTED NUMBER OF REPAIRS
Using the definition [15] of R* t and applying simple probabilistic arguments, we get:
R t = Q t <R t + 1=+Q t <R t + 1=+Q , t <R, t + 1=
+Q t <R t + 1=+Q % t <R% t + 1=+Q , t R t (81)
R t = Q t R t (82)
R t = Q t R t +Q t <R t + 1= (83)
R, t = Q, R t (84)
R t = Q t R t +Q t <R t + 1=+Q$
t <R t + 1= (85)
R% t = Q% t R t +Q%
&
t <R t + 1=+Q% t <R t + 1= (86)
R t = Q , R t (87)
Taking Laplace Stieltjes transform of above equations and solving for R ∗∗
s , we get:
R ∗∗
s =
ER F
#O F
(88)
In steady state, expected number of repairs per unit time is given by
R = lim
F→
s R ∗∗
s =
ER
#O
(89)
where,
N, = p + p %p% <p$
1 + p + 1 + p =
+ 1 + p p + p %p%
&
+ p + p , + p %
D is already specified
8. EXPECTED BUSY PERIOD OF THE REPAIRMAN
Using the definition [15] of B* t and applying simple probabilistic arguments, we get:
B t = q t ©B t +q t ©B t +q , t ©B, t
+q t ©B t +q % t ©B% t +q , t ©B t (90)
B t = W t +q t ©B t (91)
B t = W t +q t ©B t +q t ©B t (92)
B, t = W, t +q, t ©B t (93)
B t = W t +q t ©B t +q t ©B t +q$
t ©B t (94)
B% t = W% t +q% t ©B t +q%
&
t ©B t +q% t ©B t (95)
B t = q , t ©B t (96)
here, W t = G1111 t , W t = G1111 t , W, t = G-
1111 t , W t = G#
1111 t , W% t = G(
1111 t
Taking Laplace transform of above equations and solving for B ∗
s , we obtain:
B ∗
s =
ET F
#O F
(97)
In steady state, expected busy period of the repairman is given by
B = lim
F→
s B ∗
s =
ET
#O
(98)
where,
N = p + p %p% UG1111∗
0 p + p p$
+ G#
1111∗
0 + p$
G1111∗
0 V
+p G1111∗
0 p + p %p%
&
+ p G1111∗
0

+p G1111∗
0 + p ,G-
1111∗
0 + p %G(
1111∗
0
D is already specified
9. PARTICULAR CASE
For this particular case, the following are considered:
g t = η e X
(99)
g t = η e X" (100)
g- t = η-e X6 (101)
g# t = η#e X' (102)
g( t = η(e X7 (103)
Using the values given in Table-2 and the reliability expressions obtained in equations (71), (80),
(89) and (98), following reliability indices are obtained:
Mean time to plant failure = 271 hours
Availability of the plant = 0.6004
Expected number of repairs = 0.0170
Expected busy period of the repairman = 0.0990
10. PROFIT ANALYSIS
Overall profit incurred to the plant is given as:
P = C A − C B − C R (104)
where,
C = revenue per unit up time
C = cost per unit time for which the repairman is busy
C = cost per unit repair
11. SIMULATED RESULTS
Table-3 shows the behaviour of profit (P) with respect to revenue per unit up time (C ) for
different values of failure rate for subsystem-A (λ ). For simulation purpose, the values of C and
C are assumed to be 280₤ and 2815₤ respectively.
Table 3 Behaviour of P w.r.t. C for different values of λ
[ = ]. _ [ = ]. ]_ [ = ]. ]]_ [ = ]. ]]]_
P > or = or <
accordingly as
C > or = or <
1429₤
P > or = or <
accordingly as
C > or = or <
240₤
P > or = or <
accordingly as
C > or = or <
116₤
P > or = or <
accordingly as
C > or = or <
103₤
So, for the model to be beneficial at λ = 0.3, C should be greater than 1429₤. Similarly, for
the model to be beneficial at λ = 0.03, λ = 0.003 and λ = 0.0003; C should be greater than
240₤, 116₤ and 103₤ respectively.
Therefore, the cut off points of the profit are determined in order to get atleast the minimum
revenue, and the company does not run in loss.

REFERENCES
[1] Tuteja, R.K., & Taneja, G. (1992). Cost-benefit analysis of a two server, two unit, warm
standby system with different types of failure. Microelectron. Reliab., 32(10), 1353-1359.
[2] Tuteja, R.K., Rizwan, S.M., & Taneja, G. (1999). A Two Server System with Regular
Repairman Who Is Not Always Available. Paper presented at the National Symposium on
Management Science and Statistics - Applications to Trade and Industry (pp. 231-237). Guru
Nanak Dev University, Amritsar, India.
[3] Tuteja, R.K., Rizwan, S.M., & Taneja, G. (2000). Profit evaluation of a two unit cold standby
system with tiredness and two types of repairman. Journal of Indian Society of Statistics and
Operation Research, 21(1-4), 1-10.
[4] Rizwan, S. M., Khurana, V., & Taneja, G. (2005). Reliability Modelling of a Hot Standby
PLC System. Paper presented at the International Conference on Communication, Computer
and Power (pp. 486-489). Sultan Qaboos University, Sultanate of Oman.
[5] Rizwan, S.M., Khurana, V., & Taneja, G. (2007). Modelling and optimization of a single-unit
PLC system. International Journal of Modelling and Simulation, 27(4), 361-368.
[6] Rizwan, S.M., & Thanikal, J.V. (2014). Reliability analysis of a waste water treatment plant
with inspection. i-manager’s Journal on Mathematics, 3(2), 21-26.
[7] Rizwan, S.M., Thanikal, J.V., & Torrijos, M. (2014). A general model for reliability analysis
of a domestic waste water treatment plant. International Journal of Condition Monitoring and
Diagnostic Engineering Management, 17(3), 3-6.
[8] Rizwan, S.M., Taneja, G., & Tuteja, R.K. (2000). Comparative study between the profits of
two models for a two unit system with rest period of repairman. Journal of Decision and
Mathematical Sciences, 5(1-3), 27-44.
[9] Rizwan, S. M. (2007). Reliability analysis of a two unit system with two repairmen.
Caledonian Journal of Engineering, 3(2), 1-5.
[10] Mathew, A. G., Rizwan, S. M., Majumder, M. C., & Ramachandran, K. P. (2009). MTSF and
Availability of a Two Unit CC Plant. Paper presented at the International Conference on
Modelling, Simulation, and Applied Optimization (pp. 1-5). American University of Sharjah,
Sharjah, UAE: IEEE.
[11] Mathew, A.G., Rizwan, S.M., Majumder, M.C, Ramachandran, K.P., & Taneja, G. (2009).
Profit evaluation of a single unit CC plant with scheduled maintenance. Caledonian Journal
of Engineering, 5(1), 25-33.
[12] Mathew, A.G., Rizwan, S.M., Majumder, M.C., Ramachandran, K.P., & Taneja, G. (2009).
Optimization of a Single Unit CC Plant with Scheduled Maintenance Policy. Paper presented
at the International Conference on Recent Advances in Material Processing Technology (pp.
609-613). India.
[13] Mathew, A. G., Rizwan, S. M., Majumder, M. C., Ramachandran, K. P., & Taneja, G. (2010,
October). Comparative Analysis between Profits of the Two Models of a CC Plant. Paper
presented at the International Conference on Modelling, Optimization and Computing (pp.
226-231). National Institute of Technology, Durgapur, India: AIP.
Reliability modelling and analysis of a two unit parallel CC plant with different
installed capacities. Journal of Manufacturing Engineering, 5(3), 197-204.
[15] Mathew, A. G., Rizwan, S. M., Majumder, M. C., & Ramachandran, K. P. (2011). Reliability
modelling and analysis of a two unit continuous casting plant. Journal of the Franklin
Institute, 348(7), 1488-1505.
Reliability analysis of identical two-unit parallel CC plant system operative with full installed
capacity. International Journal of Performability Engineering, 7(2), 179-185.

[17] Padmavathi, N., Rizwan, S.M., Pal, A., & Taneja, G. (2012). Reliability analysis of an
evaporator of a desalination plant with online repair and emergency shutdowns. Aryabhatta
Journal of Mathematics and Informatics, 4(1), 1-12.
[18] Padmavathi, N., Rizwan, S.M., Pal, A., & Taneja, G. (2013). Comparative Analysis of the
Two Models of an Evaporator of a Desalination Plant. Paper presented at the International
Conference on Information and Mathematical Science (pp. 418-422). Punjab, India
[19] Padmavathi, N., Rizwan, S.M., Pal, A., & Taneja, G. (2013). Probabilistic analysis of an
evaporator of a desalination plant with priority for repair over maintenance. International
Journal of Scientific and Statistical Computing, 4(1), 1-8.
[20] Padmavathi, N., Rizwan, S.M., Pal, A., & Taneja, G. (2014). Probabilistic analysis of a
desalination plant with major and minor failures and shutdown during winter season.
International Journal of Scientific and Statistical Computing, 5(1), 15-23.
[21] Padmavathi, N., Rizwan, S.M., Pal, A., & Taneja, G. (2014). Probabilistic analysis of a seven
unit desalination plant with minor/major failures and priority given to repair over
maintenance. Aryabhatta Journal of Mathematics and Informatics, 6(1), 219-230.
[22] Padmavathi, N., Rizwan, S.M., & Senguttuvan, A. (2015). Comparative analysis between the
reliability models portraying two operating conditions of a desalination plant. International
Journal of Core Engineering and Management, 1(12), 1-10.
[23] Rizwan, S.M., Padmavathi, N., Pal, A., & Taneja, G. (2013). Reliability analysis of a seven
unit desalination plant with shutdown during winter season and repair/maintenance on FCFS
basis. International Journal of Performability Engineering, 9(5), 523-528.
[24] Rizwan, S.M., Padmavathi, N., & Taneja, G. (2015). Performance analysis of a
desalination plant as a single unit with mandatory shutdown during winter. Aryabhatta
Journal of Mathematics and Informatics, 7(1), 195-202.
[25] Taneja, G., & Malhotra, R. (2013). Cost-benefit analysis of a single unit system with
scheduled maintenance and variation in demand. Journal of Mathematics and Statistics, 9(3),
155-160.
[26] Taneja, G., Malhotra, R., & Chitkara, A.K. (2016). Comparative profit analysis of two
reliability models with varying demand. Aryabhatta Journal of Mathematics and Informatics,
8(2), 305-314.
[27] Yaqoob Al Rahbi, Rizwan, S.M., Alkali, B.M., Andrew Cowell, & Taneja, G. (2017).
Reliability analysis of rodding anode plant in aluminium industry. International Journal of
Applied Engineering Research, 12(16), 5616-5623.
[28] Yaqoob Al Rahbi, Rizwan, S.M., Alkali, B.M., Andrew Cowell, & Taneja, G. (2017,
September 20-22). Reliability Analysis of a Subsystem in Aluminium Plant. Paper presented
at the 6th
IEEE International Conference on Reliability, Infocom Technologies and
Optimization. doi:10.1109/ICRITO.2017.8342424
[29] Yaqoob Al Rahbi, Rizwan, S.M., Alkali, B.M., Andrew Cowell, & Taneja, G. (2018).
Maintenance analysis of a butt thimble removal station in aluminium plant. International
Journal of Mechanical Engineering and Technology, 9(4), 695-703.
[30] Taj, S.Z., Rizwan, S.M., Alkali, B.M., Harrison, D.K., & Taneja, G. (2017, April 4-6).
Reliability Modelling and Analysis of a Single Machine Subsystem of a Cable Plant. Paper
presented at the 7th
International Conference on Modelling, Simulation, and Applied
Optimization. doi:10.1109/ICMSAO.2017.7934917
[31] Taj, S.Z., Rizwan, S.M., Alkali, B.M., Harrison, D.K., & Taneja, G.L. (2017). Reliability
analysis of a single machine subsystem of a cable plant with six maintenance categories.
International Journal of Applied Engineering Research, 1(8), 2017, 1752-1757.
[32] Taj, S.Z., Rizwan, S.M., Alkali, B.M., Harrison, D.K., & Taneja, G. (2017). Probabilistic
modelling and analysis of a cable plant subsystem with priority to repair over preventive
maintenance. i-manager’s Journal on Mathematics, 6(3), 12-21.

[33] Taj, S.Z., Rizwan, S.M., Alkali, B.M., Harrison, D.K., & Taneja, G. (2018). Performance
analysis of a rod breakdown system. International Journal of Engineering and Technology,
7(3.4), 243-248.
[34] Taj, S.Z., Rizwan, S.M., Alkali, B.M., Harrison, D.K., & Taneja, G. (2018). Reliability
analysis of a 3-unit subsystem of a cable plant. Advances and Applications in Statistics, 52(6),
413-429.
[35] Taj, S.Z., Rizwan, S.M., Alkali, B.M., Harrison, D.K., & Taneja, G. (2018). Performance and
cost benefit analysis of a cable plant with storage of surplus produce. International Journal
of Mechanical Engineering and Technology, 9(8), 814-826.
[36] Ibe, O. (2008). Markov processes for stochastic modelling. London: Academic Press.
[37] Smith, W.L. (1955). Regenerative stochastic processes. Proc R Soc Lond A, 232(1188), 6-31.

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