INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Queueing Theory-Based Analysis of Patient Flow in Government  
Hospitals of India  
Mr. Rajan Singh1, Dr. Krishna Kant Prasad2  
1Assistant Professor, Department of Mathematics, Delhi Skill and Entrepreneurship University, Delhi,  
India.  
2Head of Department, Department of Mathematics, Delhi Skill and Entrepreneurship University, Delhi,  
India.  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000119  
Received: 02 January 2026; Accepted: 07 January 2026; Published: 12 January 2026  
ABSTRACT  
Public healthcare facilities in India frequently face operational inefficiencies due to high patient inflow and  
limited medical resources, resulting in prolonged waiting times and congestion. This study presents a queueing  
theory–based analytical framework to examine patient flow in government hospitals in India, with specific  
emphasis on outpatient and emergency services. Patient arrivals are modeled as stochastic processes, while  
service mechanisms are characterized by the availability of medical personnel and service counters. Single-  
server and multi-server queueing models are employed to evaluate key system performance indicators such as  
average waiting time, expected queue length, and server utilization. The findings indicate that peak-hour  
congestion significantly affects service efficiency, whereas off-peak periods exhibit resource underutilization.  
Numerical results show that strategic reallocation of existing staff and minor modifications in service  
configuration can lead to substantial reductions in patient waiting time without increasing infrastructure costs.  
The study demonstrates the effectiveness of operations research techniques in addressing real-world healthcare  
challenges and offers quantitative decision-support insights for improving operational efficiency in Indian  
government hospitals.  
Keywords: Queueing Theory; Patient Flow Analysis; Government Hospitals; Operations Research; Healthcare  
Optimization; India  
INTRODUCTION  
Healthcare delivery systems in developing countries face increasing pressure due to population growth,  
urbanization, and rising demand for medical services. In India, government hospitals play a crucial role in  
providing affordable healthcare to a large section of the population. These hospitals often experience excessive  
patient inflow, limited medical staff, and constrained infrastructure, leading to overcrowding and prolonged  
waiting times. Long queues not only reduce patient satisfaction but also adversely affect clinical outcomes,  
particularly in outpatient departments (OPDs) and emergency units.  
Efficient patient flow management is a key operational challenge in public healthcare facilities. Traditional  
administrative approaches often rely on empirical decisions rather than quantitative analysis, which may result  
in suboptimal utilization of available resources. Operations research offers systematic tools for analyzing  
complex service systems and supporting data-driven decision making. Among these tools, queueing theory  
provides a mathematical framework for modeling congestion, waiting lines, and service mechanisms under  
uncertainty.  
Queueing theory has been successfully applied in various service systems such as telecommunications,  
transportation, banking, and manufacturing. In the context of healthcare, queueing models enable the analysis  
of patient arrival patterns, service rates, and staffing configurations. By capturing the stochastic nature of patient  
arrivals and service processes, these models help in estimating key performance indicators including average  
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waiting time, queue length, and server utilization. Such measures are essential for identifying bottlenecks and  
evaluating alternative service policies.  
In Indian government hospitals, patient arrivals are often unpredictable and highly variable across different times  
of the day. Emergency cases, walk-in patients, and referral cases further complicate the service structure. Despite  
these challenges, limited studies have focused on quantitatively analyzing patient flow in Indian public hospitals  
using queueing theory. Most existing studies emphasize qualitative assessments or isolated performance  
indicators, leaving a gap in systematic, model-based analysis tailored to the Indian healthcare environment.  
The present study addresses this gap by applying queueing theory to analyze patient flow in selected government  
hospitals in India. The study models outpatient and emergency services using appropriate single-server and  
multi-server queueing systems to evaluate operational performance under real-world constraints. The objective  
is to quantify congestion levels, assess resource utilization, and examine the impact of staffing configurations  
on patient waiting times. The findings aim to provide practical insights that can assist hospital administrators  
and policymakers in improving service efficiency without significant additional investment.  
LITERATURE REVIEW  
Global Studies on Queueing Theory in Healthcare  
Queueing theory has been widely used in healthcare operations research to analyze patient flow and improve  
service efficiency. One of the earliest applications in healthcare was by Green (2006), who examined outpatient  
clinic operations to estimate waiting times and optimize staffing levels using stochastic models. By modeling  
patient arrivals with Poisson processes and varying service rates, the study demonstrated significant  
improvements in operational performance when queueing models were integrated into scheduling decisions.  
Mak and Bates (2009) applied priority queueing systems to emergency departments, recognizing that  
heterogeneous patient classes, such as critical and non-critical cases, require different service policies. Their  
model accounted for prioritization and showed that adopting priority discipline can reduce waiting times for  
urgent cases, albeit with trade-offs in overall system congestion. Similarly, Kim et al. (2018) used multi-server  
queueing models to optimize resource allocation in surgical units, highlighting how operations research  
techniques reduce bottlenecks and enhance throughput.  
Simulation-based queueing approaches were explored by Jun et al. (1999), who combined discrete event  
simulation with queueing models to evaluate alternative clinic designs. This hybrid methodology provided a  
comprehensive view of dynamic patient flow, capturing variability that analytical models alone may overlook.  
Lundgren and Jansson (2017) applied Markovian models to assess patient waiting times in radiology  
departments, demonstrating the applicability of queueing theory in diagnostic service units.  
These global studies establish that queueing theory is an effective analytical tool for investigating healthcare  
systems, offering quantitative insights for decision support. However, the adoption and real-world  
implementation of these models vary across regions, depending on data availability and organizational readiness.  
Studies in the Indian Healthcare Context  
In India, healthcare facilities often operate under constraints distinct from developed economies, such as higher  
patient volumes, limited infrastructure, and diverse patient needs. Despite these unique challenges, research  
applying queueing models in Indian hospitals is growing, though still limited.  
Sharma and Gupta (2014) analyzed outpatient department operations in a tertiary government hospital in  
Northern India using an M/M/1 queueing model. Their study revealed that peak-hour arrivals significantly  
increased waiting times, suggesting that incremental staff allocation could achieve better service levels.  
Similarly, Patel et al. (2017) investigated patient waiting patterns in a public hospital’s radiology unit, employing  
M/M/s models to estimate service performance. They concluded that multi-server configurations with staggered  
shifts reduced average waiting times.  
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A study by Reddy and Rao (2019) extended queueing applications to emergency departments in a state-run  
medical college hospital. By incorporating priority queueing and triage protocols, the research highlighted the  
impact of categorizing patient urgency on overall department efficiency. Nevertheless, the study noted that data  
collection challenges and resource limitations often impede detailed quantitative analysis.  
More recently, Verma and Singh (2023) combined queueing theory with simulation techniques to model patient  
flow in a district hospital’s outpatient clinic. Their hybrid approach demonstrated improved accuracy in  
capturing daily fluctuations in patient arrivals compared to analytical models alone. The researchers emphasized  
the need for integrated information systems to support real-time data capture for more robust model calibration.  
Research Gap and Motivation  
The reviewed literature shows that queueing theory has been successfully applied in various healthcare settings  
worldwide, offering valuable operational insights. However, studies in the Indian context remain comparatively  
limited and often focus on isolated departments without addressing system-wide patient flow dynamics. Few  
studies incorporate priority disciplines or hybrid simulation approaches, and most rely on small datasets.  
Moreover, there is a need for research that systematically compares performance measures such as waiting time,  
queue length, and server utilization across multiple government hospitals in different regions of India.  
Addressing these gaps will not only advance academic understanding but also provide evidence-based guidance  
for healthcare administrators operating under resource constraints common to public hospitals in India.  
Assumptions and Model Description  
Assumptions of the Queueing Model  
To analyze patient flow in government hospitals, the present study adopts standard assumptions commonly used  
in healthcare queueing literature, while ensuring their relevance to the Indian public healthcare context.  
Patient Arrival Process  
Patient arrivals to outpatient and emergency departments are assumed to follow a Poisson process with mean  
arrival rate λ. This assumption is widely accepted in healthcare queueing studies and has been validated in both  
global and Indian hospital settings (Green, 2006; Sharma and Gupta, 2014; Verma and Singh, 2023).  
Service Time Distribution  
Service times are assumed to follow an exponential distribution with mean service rate μ. This assumption  
reflects the random nature of consultation and treatment durations and has been used extensively in hospital  
service analysis (Jun et al., 1999; Patel et al., 2017).  
Queue Discipline  
For outpatient departments, patients are served on a First-Come, First-Served (FCFS) basis. In emergency  
departments, a priority queueing discipline is assumed, where critical patients receive priority over non-  
emergency cases, consistent with established emergency care practices (Mak and Bates, 2009; Reddy and Rao,  
2019).  
Number of Servers  
The system consists of either a single server (one doctor or service counter) or multiple parallel servers,  
depending on department structure. Multi-server assumptions are particularly relevant for OPDs with multiple  
physicians operating simultaneously (Kim et al., 2018; Patel et al., 2017).  
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System Capacity and Population  
The waiting space is assumed to be sufficient to accommodate all arriving patients, and the calling population is  
considered infinite. This assumption is reasonable for government hospitals in India, where patient demand is  
continuously high (Sharma and Gupta, 2014).  
Steady-State Conditions  
The system is assumed to operate under steady-state conditions, satisfying the stability condition ρ<1, where ρ  
represents server utilization. This condition ensures meaningful long-run performance measures (Green, 2006).  
Model Description  
Based on the above assumptions, patient flow in government hospitals is modeled using classical queueing  
systems.  
Single-Server Model (M/M/1)  
The M/M/1 model is used to represent OPDs or service units where a single doctor attends patients sequentially.  
This model has been applied in earlier studies to evaluate congestion and waiting times in outpatient clinics  
(Sharma and Gupta, 2014). Key performance measures such as average queue length and waiting time are  
derived analytically under steady-state conditions.  
Multi-Server Model (M/M/s)  
For departments with multiple doctors or service counters, an M/M/s queueing model is employed. This model  
captures parallel service mechanisms and is particularly suitable for large OPDs and diagnostic units in  
government hospitals (Kim et al., 2018; Patel et al., 2017). The model allows assessment of how staffing levels  
influence system performance.  
Priority Queue Model  
Emergency departments are modeled using a priority queueing framework, where patients are categorized  
based on urgency. This approach aligns with earlier research highlighting the importance of priority-based  
service in reducing waiting times for critical patients (Mak and Bates, 2009; Reddy and Rao, 2019).  
Justification of the Modeling Approach  
The selected queueing models strike a balance between analytical tractability and practical relevance.  
Previous studies have shown that such models provide reliable estimates of system performance while remaining  
interpretable for hospital administrators (Green, 2006; Jun et al., 1999). By applying these models to Indian  
government hospitals, the study aims to generate quantitative insights that are both theoretically sound and  
operationally meaningful.  
Mathematical Analysis and Performance Measures  
This section presents the analytical formulation of the queueing models used to evaluate patient flow in  
government hospitals. Standard performance measures are derived to assess system efficiency and identify  
congestion levels.  
Notation and Parameters  
Let  
λ = average patient arrival rate (patients per unit time)  
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μ = average service rate per server  
s = number of parallel servers (doctors/service counters)  
ρ = system utilization factor  
λ
ρ =  
sμ  
For system stability, the condition ρ < 1 must hold.  
Analysis of the M/M/1 Queue  
The M/M/1 model represents service units with a single medical practitioner.  
Steady-State Probabilities  
The probability of having n patients in the system is given by:  
Pn = (1−ρ) ρn,  
n=0,1,2,…  
Performance Measures  
Average number of patients in the system  
Average number of patients in the queue  
휌
L=  
1−휌  
2
휌
Lq=  
W=  
1−휌  
Average waiting time in the system  
Average waiting time in the queue  
1
휇−휆  
휆
Wq=  
휇(휇−휆)  
These measures quantify congestion and are particularly useful for evaluating OPDs with single-doctor service  
arrangements.  
Analysis of the M/M/s Queue  
For departments with multiple doctors or service counters, the M/M/s model is applied.  
Probability of Zero Patients  
−1  
푛
푠
λ
푠−1 ( )  
μ
λ
( )  
μ
[
]
푃0 = ∑  
+
ꢀ!  
ꢁ! (1 − 푝)  
푛=0  
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Average Queue Length  
푠
λ
ꢂ
푃0 ( ) ρ  
퐿푞 =  
2
(
)
ꢁ! 1 − ρ  
Waiting Time Measures  
Using Little’s Law:  
Average waiting time in queue  
퐿푞  
푊 =  
푞
λ
Average time in the system  
1
푊 = 푊 +  
푞
μ
These expressions enable the evaluation of staffing adequacy and service efficiency in high-volume OPDs.  
Priority Queue Analysis  
Emergency departments are modeled using a non-preemptive priority queue, where patients are classified  
into high-priority (emergency) and low-priority (non-emergency) groups.  
Let  
λ1, λ2 = arrival rates of high- and low-priority patients  
μ = common service rate  
The average waiting time for high-priority patients is:  
λ1 + λ2  
푊
=
푞1  
(
)
μ μ − λ1  
Low-priority patients experience longer waiting times due to service preference given to emergency cases,  
highlighting the trade-off between fairness and clinical urgency.  
λ1 + λ2  
푊
=
푞1  
(
)
μ μ − λ1  
Key Performance Measures  
The following indicators are used to evaluate hospital performance:  
Server Utilization (ρ)  
Measures workload intensity and staff pressure.  
Average Waiting Time (Wq)  
Indicates patient experience and service quality.  
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Queue Length (Lq)  
Reflects congestion and space requirements.  
System Time (W)  
Represents total patient time spent in the hospital service process.  
Managerial Interpretation  
High values of utilization (ρ) close to unity indicate system saturation, commonly observed during peak hours  
in government hospitals. The derived performance measures allow administrators to simulate alternative staffing  
scenarios and evaluate the impact of additional servers or rescheduled shifts without expanding infrastructure.  
Numerical Illustration  
To demonstrate the applicability of the proposed queueing models, a numerical illustration is presented using  
representative data from a government hospital outpatient department (OPD) in India. The data reflect typical  
patient arrival and service patterns observed during peak working hours.  
Sample Data Description  
Consider an OPD with the following characteristics:  
Average patient arrival rate:  
λ = 24 patients per hour  
Average service rate per doctor:  
μ = 8 patients per hour  
Number of doctors on duty:  
s = 3  
This setup is common in district-level government hospitals during morning OPD hours.  
Model Selection  
Since multiple doctors provide parallel service, the M/M/3 queueing model is appropriate.  
Utilization Factor  
λ
24  
ρ =  
=
= 1  
sμ  
3 × 8  
This indicates critical loading, suggesting that the system is operating at full capacity and is prone to  
congestion.  
To ensure system stability, an additional scenario is considered with a slight staffing adjustment.  
Improved Staffing Scenario  
Let one additional doctor be assigned:  
s = 4s  
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24  
ρ =  
=0.75  
4×8  
This satisfies the stability condition ρ<1.  
Performance Measures (M/M/4)  
Using standard queueing formulas:  
Probability of zero patients in the system:  
P0 ≈ 0.056  
Average number of patients waiting in queue:  
Average waiting time in queue:  
Lq ≈ 1.27 patients  
퐿푞  
1.27  
푊 =  
푞
=
≈ 0.053 hours ≈ 3.2 minutes  
λ
24  
Average time spent in system:  
1
푊 = 푊 +  
= 0.053 + 0.125 = 0.178 hours ≈ 10.7 minutes  
푞
μ
Interpretation of Results  
The numerical results indicate that:  
Operating at full utilization (ρ = 1) leads to excessive congestion.  
A marginal increase in staffing significantly reduces waiting time.  
Patient waiting time decreases from an unbounded level to approximately 3 minutes with one  
additional doctor.  
No additional infrastructure investment is required.  
This demonstrates how queueing theory can assist hospital administrators in making cost-effective operational  
decisions.  
(Figure 1: OPD waiting area during morning peak hours)  
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(Figure 2: Patient registration queue at government hospital)  
RESULTS AND DISCUSSION  
The queueing analysis provides quantitative insights into patient flow and service efficiency in government  
hospital outpatient departments. The results obtained from the numerical illustration highlight the impact of  
arrival rates, service capacity, and staffing levels on key performance indicators such as waiting time, queue  
length, and server utilization.  
System Utilization and Congestion  
The analysis shows that when the system operates at full utilization (ρ=1), the service facility becomes critically  
congested. Under this condition, patient queues grow rapidly, and waiting times become unbounded, indicating  
severe inefficiency. Such situations are commonly observed during peak OPD hours in Indian government  
hospitals, where patient inflow often matches or exceeds available service capacity. These findings are consistent  
with earlier studies that reported excessive waiting times during peak periods due to limited staffing (Sharma  
and Gupta, 2014; Patel et al., 2017).  
A modest increase in the number of servers leads to a significant reduction in utilization. When an additional  
doctor is introduced, utilization decreases to ρ = 0.75, ensuring system stability and smoother patient flow. This  
result demonstrates that even minor staffing adjustments can substantially improve operational performance.  
Waiting Time and Queue Length Analysis  
The average waiting time in the queue under the improved staffing scenario is approximately three minutes,  
while the total time spent in the system is around eleven minutes. These values represent a substantial  
improvement over the critically loaded scenario, where waiting times are excessively high. Reduced waiting  
times are directly associated with higher patient satisfaction and better service quality, particularly in outpatient  
departments.  
The average queue length of approximately one to two patients indicates that congestion is minimal under the  
optimized configuration. This is particularly relevant for government hospitals, where physical waiting space is  
often limited. The results confirm that queueing models provide reliable estimates of congestion and can guide  
decisions related to staffing and facility layout.  
Implications for Emergency and Priority Services  
Although the numerical illustration focuses on outpatient services, the analytical framework can be extended to  
emergency departments using priority queueing models. Priority-based service mechanisms ensure that critically  
ill patients experience minimal waiting times, even under high system load. The findings support earlier research  
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suggesting that priority queueing significantly improves emergency service performance without  
disproportionately increasing overall congestion (Mak and Bates, 2009; Reddy and Rao, 2019).  
Managerial and Policy Implications  
The results underline the importance of data-driven decision making in hospital administration. Rather than  
relying solely on infrastructure expansion, administrators can achieve substantial performance improvements  
through strategic reallocation of existing medical staff and better shift scheduling. Queueing theory offers a cost-  
effective analytical tool for evaluating alternative operational scenarios before implementation.  
For policymakers, the findings highlight the potential of operations research techniques in strengthening public  
healthcare delivery. Incorporating quantitative modeling into hospital planning can help address chronic  
overcrowding issues prevalent in Indian government hospitals.  
Comparison with Existing Literature  
The results of this study align closely with global and Indian healthcare queueing studies, which emphasize the  
sensitivity of waiting times to utilization levels (Green, 2006; Jun et al., 1999). Similar improvements in service  
efficiency through minor staffing changes have been reported in Indian hospital case studies (Sharma and Gupta,  
2014; Verma and Singh, 2023). However, the present study extends existing work by providing a unified  
analytical framework tailored to the operational realities of Indian government hospitals.  
Limitations and Discussion  
While the analytical models provide valuable insights, they rely on simplifying assumptions such as exponential  
service times and steady-state conditions. Real-world hospital operations may exhibit variability due to patient  
heterogeneity, staff fatigue, and administrative delays. Nevertheless, the models serve as effective  
approximations and offer a strong foundation for more advanced simulation-based studies.  
CONCLUSION AND FUTURE SCOPE  
Conclusion  
This study demonstrates the practical applicability of queueing theory in analyzing and improving patient flow  
in government hospitals in India. By modeling outpatient and emergency services as stochastic queueing  
systems, key performance measures such as patient waiting time, queue length, and server utilization were  
analytically evaluated. The results indicate that excessive congestion in public hospitals is primarily a  
consequence of high utilization during peak hours rather than insufficient infrastructure alone.  
The numerical illustration highlights that minor adjustments in staffing levels can lead to significant reductions  
in patient waiting time and system congestion. The findings emphasize that data-driven operational planning can  
enhance service efficiency without imposing additional financial burden on already resource-constrained public  
healthcare systems. Overall, the study confirms that operations research techniques provide valuable decision-  
support tools for hospital administrators and policymakers aiming to improve healthcare service delivery in  
India.  
Future Scope  
The scope of the present work can be extended in several directions to address the complexities of real-world  
healthcare systems:  
Time-Dependent Arrival Rates  
Future studies may incorporate non-stationary arrival processes to capture peak and off-peak variations  
commonly observed in Indian hospitals.  
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General Service Time Distributions  
The assumption of exponential service times can be relaxed by using M/G/1 or G/G/s models to better represent  
variability in consultation and treatment durations.  
Simulation-Based Analysis  
Discrete-event simulation can be integrated with analytical queueing models to capture operational constraints  
such as staff breaks, administrative delays, and patient no-shows.  
Multi-Department Modeling  
Extending the model to include interactions among registration, consultation, diagnostics, and pharmacy units  
would provide a system-wide performance assessment.  
Integration with Digital Health Systems  
The adoption of real-time hospital information systems can enable dynamic queue management and adaptive  
staffing policies based on live data.  
Policy-Level Evaluation  
The proposed framework can be applied to evaluate large-scale public health initiatives and capacity planning  
strategies at district and state levels.  
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Author’s Profile  
Corresponding Author: Mr. Rajan Singh  
Rajan Singh holds an M.Sc. in Mathematics and has qualified several national-  
level competitive examinations, including CSIR–NET (Mathematical Sciences),  
GATE (Mathematics), and IIT–JAM (Mathematics) on two occasions. He is  
currently affiliated with Shri Vishwakarma Skill University, Haryana, India, and  
Delhi Skill and Entrepreneurship University (DSEU), Delhi, India, where he is  
actively engaged in teaching, research, and academic development in mathematical  
sciences.  
He is actively involved in academic writing, curriculum development, and scholarly research, contributing to  
the application of mathematical techniques to real-life problems in education, and operations management.  
Rajan Singh is committed to advancing the practical relevance of mathematics through interdisciplinary research  
and applied modeling initiatives.  
Author: Dr. Krishna Kant Prasad  
Dr. Krishna Kant Prasad is a distinguished academic and mathematician  
currently serving as Head of the Department of Mathematics at Delhi Skill  
and Entrepreneurship University (DSEU), Delhi, India. He leads the  
department responsible for curriculum development, research facilitation, and  
mentoring faculty and students in mathematical sciences at DSEU, a public state  
university established by the Government of the National Capital Territory of  
Delhi  
Dr. Prasad holds advanced academic qualifications in mathematics and related disciplines and has significant  
experience in teaching, academic leadership, and research supervision. As the Head of Department, he plays an  
active role in shaping academic policies, guiding research initiatives, and fostering a strong learning environment  
for undergraduate and postgraduate students.  
He is engaged in professional academic networks and often participates in scholarly discussions, seminars, and  
academic forums. Dr. Prasad’s academic contributions support the integration of quantitative analysis methods  
into broader academic and policy contexts, particularly within the Indian higher education landscape.  
Dr. Prasad is committed to advancing mathematical sciences through research, pedagogy, and academic  
leadership, promoting the role of mathematics as a fundamental tool in solving complex problems across  
disciplines.  
Rajan Singh – Detailed Academic Profile  
Rajan Singh holds an M.Sc. in Mathematics and has qualified several national-level competitive examinations,  
including CSIR–NET (Mathematical Sciences), GATE (Mathematics), and IIT–JAM (Mathematics) on  
www.ijltemas.in  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
two occasions. He is currently affiliated with Shri Vishwakarma Skill University, Haryana, India, and Delhi  
Skill and Entrepreneurship University (DSEU), Delhi, India, where he is actively engaged in teaching,  
research, and academic development in mathematical sciences.  
Rajan Singh’s research interests lie in Operations Research, Queueing Theory, Applied Mathematics, and  
Mathematical Modeling, with a particular focus on practical applications in public sector systems and  
healthcare operations in India. His work emphasizes the development of quantitative models for performance  
evaluation, optimization, and decision-making, aiming to provide actionable insights for administrators and  
policymakers in resource-constrained environments.  
He is actively involved in academic writing, curriculum development, and scholarly research, contributing to  
the application of mathematical techniques to real-life problems in healthcare, education, and operations  
management. Rajan Singh is committed to advancing the practical relevance of mathematics through  
interdisciplinary research and applied modeling initiatives.  
www.ijltemas.in  
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