1. A Progress Report
on
Ph.D. IInd
Semester
Under the Guidance of
Dr. Vinay Kumar
Assistant Professor
Department of Computer Science & Engineering
NATIONAL INSTITUTE OF TECHNOLOGY JAMSHEDPUR (INDIA)
By
Aditya Narayan Hati
(2019RSCS004)
Department of CSE
2. • Course Completed
• Faculty Development Program attended
• Research Paper study
• References
Contents of Report
3. • Advanced Data Structure (CS4101) of 4 credits
• Distributed Systems(CS4138) of 4 credits
• Secured 9.0 CGPA in 1st
Semester
Course Completed in 1st
semester
4. • Optimization Techniques (CS4203) of 4 credits
• Advanced Software Engineering (7CS4202) of 4 credits
• Results pending
Courses Taken in 2nd
Semester
5. Type Status Title Start date End date
Duration (in
days)
% Complete Note
Milestone Ended First semester 01-31-2019 01-31-2019 - 100
Milestone On track Second semester 07-31-2020 07-31-2020 - 100
Milestone Not started Third semester 12-30-2020 12-30-2020 - -
Task On track Research topic search 07-22-2019 10-30-2020 335 60
Task On track Research survey 04-01-2020 10-30-2020 153 15
Multiple studies are done.
Topics are yet to be finalized.
Overall research area will be
scheduling problem using
metaheuristic algorithms.
Task On track Research implementation 07-15-2020 03-31-2021 186 20
Estimated for 2 research
findings
Task Not started Analysis 11-02-2020 03-31-2021 108 0
Estimated for 1 survey and 2
research findings
Task Not started Journal writing and publication 11-02-2020 06-30-2021 173 0
Estimated for 1 survey and 2
research findings
Milestone Not started Research proposal finalization 11-16-2020 11-16-2020 - -
Milestone Not started Approval of research proposal 12-30-2020 12-30-2020 - -
Research plan and progress report
7. Reference Method Advantages Disadvantages
Ryerson [1] mathematical
modelling using
reliability physics
Applied to heterogeneous components.
Accurate failure rates for any specific
application may be predicted
Fast prediction will be possible with actual
stress
Field of failure data of components are
required
Requires strong mathematical background
Liu and Chen[2] mathematical
modelling with
Bayesian method
Uses agglomeration of inspection data
collected at multilevel for the dynamic
reliability assessment
Non-repairability assumption causes lower
estimation accuracy
Kumar and
Jackson [3]
MM with constant
hazard rate
non-constant failure rates of the components
are considered
Author used Markov chain which suffers
from state-space explosion problem
Kleyner and
Volovoi [4]
SPN modelling Combines various real-life factors (e.g. a
user's response time to the warning light,
duration of repair, estimated down time,
system age)
Authors claim that field data is not
required without which attrition function
cannot be developed, which is required for
analysis of warehouse shipping history for
product
Ramos et al. [5] GSPN modelling Disturbances in a probabilistic way;
however, they do not model the
stochastic response of the power
system
There should be detection technique
which takes a decision that system
will be shut down for repair or not
based on severity
Faraji and
Kiyono [6]
weighed SPN
(WSPN) modelling
Dynamically represents the relationship
between infrastructure elements
Getting life data for this method is
difficult for safety systems
RELATED WORK-RELIABILITY PREDICTION METHODS
8. Books:
1. Hamdy A. Taha, "Operations Research: An Introduction", Pearson Education Limited.
2. Amir Beck, "Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with MATLAB", SIAM.
3. Achintya Haldar, Sankaran Mahadevan, "Probability, Reliability, and Statistical Methods in Engineering Design",
Wiley.
Journals:
4. H. Garg, and S. P. Sharma, "Multi-objective reliability-redundancy allocation problem using particle swarm optimization",
Computers & Industrial Engineering, 2013, Volume 64, Issue 1, Pages 247 - 255.
5. H. Garg, "An efficient biogeography based optimization algorithm for solving reliability optimization problems", Swarm
and Evolutionary Computation, 2015, Volume 24, Pages 1-10.
6. P. K. Muhuri, and R. Nath,"A novel evolutionary algorithmic solution approach for bilevel reliability-redundancy allocation
problem", Reliability Engineering & System Safety, 2019, Volume 191.
7. P. T. H. Nguyen, and D. Sudholt, "Memetic algorithms outperform evolutionary algorithms in multimodal optimisation",
Artificial Intelligence, 2020, Volume 287.
8. T. Bäck, and H.-P. Schwefel, "An Overview of Evolutionary Algorithms for Parameter Optimization", Evolutionary
Computation, 1993, Volume 1, Issue 1, Pages 1-23.
9. Z. Michalewicz, and M. Schoenauer, "Evolutionary Algorithms for Constrained Parameter Optimization Problems",
Evolutionary Computation, 1996, Volume 4, Issue 1, Pages 1-32.
References
9. Journals (Contd.)
7. Q. Zhu, Q. Zhang, and Q. Lin, "A Constrained Multiobjective Evolutionary Algorithm with Detect-and-Escape
Strategy," IEEE Transactions on Evolutionary Computation, 2020.
8. Y. Yang, J. Liu, and S. Tan, "A constrained multi-objective evolutionary algorithm based on decomposition and
dynamic constraint-handling mechanism", Applied Soft Computing, 2020, Volume 89.
9. X. Wang,, and L. Tan, "A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers,
Computers & Operations Research", 2009, 36, 907–918.
10. C. C. Lin, W. Y. Liu, and Y. H. Chen, "Considering stockers in re-entrant hybrid flow shop scheduling with limited
buffer capacity", Computers & Industrial Engineering, 2020, Volume 139, ISSN 0360-8352.
11. M.R. Garey, "The Complexity of Flowshop and Jobshop Scheduling", Mathematics of Operations Research, 1976,
1(2): 117–129, doi:10.1287/moor.1.2.117.
12. D. H. Wolpert, and W. G. Macready, "No Free Lunch Theorems for Optimization", IEEE Transactions on
Evolutionary Computation, 1997, 1: 67.
13. L. Wang, L. Zhang, and D. Z. Zhenga, "An effective hybrid genetic algorithm for flow shop scheduling with limited
buffers", Computers & Operations Research, 2006, 33, 2960–2971.
14. Q. K.Pan, L. Gao, L. Wang, and W. D. Li, "An effective hybrid discrete differential evolution algorithm for the flow
shop scheduling with intermediate buffers", Information Sciences, 2011, 181, 668–685.
References (Contd.)
![Reference Method Advantages Disadvantages
Ryerson [1] mathematical
modelling using
reliability physics
Applied to heterogeneous components.
Accurate failure rates for any specific
application may be predicted
Fast prediction will be possible with actual
stress
Field of failure data of components are
required
Requires strong mathematical background
Liu and Chen[2] mathematical
modelling with
Bayesian method
Uses agglomeration of inspection data
collected at multilevel for the dynamic
reliability assessment
Non-repairability assumption causes lower
estimation accuracy
Kumar and
Jackson [3]
MM with constant
hazard rate
non-constant failure rates of the components
are considered
Author used Markov chain which suffers
from state-space explosion problem
Kleyner and
Volovoi [4]
SPN modelling Combines various real-life factors (e.g. a
user's response time to the warning light,
duration of repair, estimated down time,
system age)
Authors claim that field data is not
required without which attrition function
cannot be developed, which is required for
analysis of warehouse shipping history for
product
Ramos et al. [5] GSPN modelling Disturbances in a probabilistic way;
however, they do not model the
stochastic response of the power
system
There should be detection technique
which takes a decision that system
will be shut down for repair or not
based on severity
Faraji and
Kiyono [6]
weighed SPN
(WSPN) modelling
Dynamically represents the relationship
between infrastructure elements
Getting life data for this method is
difficult for safety systems
RELATED WORK-RELIABILITY PREDICTION METHODS](https://image.slidesharecdn.com/progreporthathi-250804074731-378985be/85/kvjhvhjvhjhjhjghjghjgjhgjhgjhgjhgjhgjhgjhgjh-7-320.jpg)
