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Saroj Kumar Patel
India
Приєднався 3 гру 2011
Professor, Mechanical Engineering Department, National Institute of Technology, Rourkela, Odisha State, India
Lec 29: Generalized Reduced Gradient Method
It explains the algorithm of Generalized Reduced Gradient Method for solving a constrained non-linear optimization problem illustrated with a solved numerical problem.
(Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
(Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
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Відео
Lec 23B: Job Shop Scheduling by Shifting Bottleneck Heuristic (Part 2)
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This is the concluding part of two lecture videos that explains optimal solution of a Generalized Job Shop Scheduling with n jobs and m machines for minimum makespan by Shifting Bottleneck Heuristic with the help of a solved numerical example.
Lec 23A: Job Shop Scheduling by Shifting Bottleneck Heuristic (Part 1)
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This is the 1st part of two lecture videos that explains optimal solution of a Generalized Job Shop Scheduling with n jobs and m machines for minimum makespan by Shifting Bottleneck Heuristic with the help of a solved numerical example. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre...
Simulated Annealing: A Solved Example
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It explains principle of Simulated Annealing and solves a numerical example using this algorithm. (Video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
Lec 1: Introduction to Decision Modelling/Operations Research
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This is the first lecture as an introduction to the course Decision Modelling which is also popularly known as Operations Research. It tells about the course objective, course content, list of reference books. (Lecture delivered Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela to its undergraduate students on the subject ME431/4237: Decision Modelling and...
Lec 21E: Flow Shop Scheduling by NEH heuristic with solved example
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This lecture explains Flow Shop Scheduling with n jobs and m machines by NEH (Nawaz, Enscore & Ham) heuristic with a solved numerical example. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
Lec 21D: Flow Shop Scheduling by CDS heuristic with solved example
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This lecture explains Flow Shop Scheduling with n jobs and m machines by CDS (Campbell, Dudek & Smith) heuristic with a solved numerical example. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
Lec 21C: Flow Shop Scheduling by Palmer heuristic with solved example
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This lecture explains Palmer heuristic for Flow Shop Scheduling with n jobs and m machines by Palmer heuristic with a solved numerical example. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
Lec 21B: Scheduling (Flow Shop, Branch and Bound algorithm, solved example)
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This video lecture illustrates Flow Shop Scheduling for minimum makespan using Branch and Bound algorithm with a solved numerical example. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
Lec 21A: Scheduling (Flow Shop, Permutation/Non-permutation type)
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(Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
Lec 14D: Network Problem (Minimal Spanning Tree by Kruskal Algorithm)
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With a solved numerical example, it illustrates Kruskal algorithm to find a minimal spanning tree in a network problem. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla Centre for Technology Enabled Learning (ANKCTEL), NIT Rourkela)
Lec 14C: Network Problem(Floyd algorithm for all pairs shortest route problem with computer program)
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With a solved numerical example, it illustrates Floyd algorithm to find shortest routes between all possible pairs of nodes in a network problem. It is based on dynamic programming approach. It also gives a computer program for this algorithm. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela and video made with support of A.N. Khosla...
Lec 6: Linear Programming (degenerate, multiple optima, unbound, infeasible solutions)
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With solved examples, this video explains the relationship between graphical and simplex methods of solving linear programming problems. Then it explains the features of linear programming problems resulting in four special types of solutions namely 1) Degenerate solution 2) Multiple optimal solution 3) Unbound solution 4) Infeasible solution. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel,...
Lec 5: Linear Programming (Big M method)
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With a solved example it explains Big M method for solving Linear Programming problem containing at least one equality or greater than equal type of inequality constraints. (Lecture delivered by Prof.(Dr.) Saroj Kumar Patel, Professor, Mechanical Engineering Department, NIT Rourkela to its undergraduate students on the subject ME431/4237: Decision Modelling and video made with support of A.N. K...
Lec 7: Linear Programming (Duality-Part 1)
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It starts with review of previous lectures on Big M method of solving a linear programming problem, linear programming problems resulting in four special types of solutions namely degenerate solution, multiple solution, unbound solution and infeasible solution. Then it explains features of duality, difference between primal and dual problems, dual theorems and procedure to find dual from primal...
Lec 8: Linear Programming (Duality-Part 2)
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Lec 8: Linear Programming (Duality-Part 2)
Lec 10: Linear Programming (Sensitivity Analysis-Part 2, MATLAB)
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Lec 10: Linear Programming (Sensitivity Analysis-Part 2, MATLAB)
Lec 9: Linear Programming (Sensitivity Analysis-Part 1)
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Lec 9: Linear Programming (Sensitivity Analysis-Part 1)
Lec 12: Transportation Problem (Vogel Approximation, u-v, MODI)
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Lec 12: Transportation Problem (Vogel Approximation, u-v, MODI)
Lec 13: Transportation problem (MODI, profit, unbalanced, transhipment) & Assignment Problem
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Lec 13: Transportation problem (MODI, profit, unbalanced, transhipment) & Assignment Problem
Lec 14: Network Problem (shortest route by systematic method, minimal spanning tree by Prim method)
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Lec 14: Network Problem (shortest route by systematic method, minimal spanning tree by Prim method)
Lec 16: CPM:Total/free/independent float-Part 2, Project Scheduling, PERT-Part 1
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Lec 16: CPM:Total/free/independent float-Part 2, Project Scheduling, PERT-Part 1
Lec 17: PERT(Part 2), project network crashing (Part 1)
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Lec 17: PERT(Part 2), project network crashing (Part 1)
Lec 18: CPM: Project network crashing (Part 2), CPM as LPP
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Lec 18: CPM: Project network crashing (Part 2), CPM as LPP
Lec 19: Scheduling (Introduction, assumption, types, single processor-shortest processing time)
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Lec 19: Scheduling (Introduction, assumption, types, single processor-shortest processing time)
Lec 20: Scheduling (Single Processor: dispatching rules like SPP, EDD, Moore; Gantt chart)
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Lec 20: Scheduling (Single Processor: dispatching rules like SPP, EDD, Moore; Gantt chart)
Lec 22: Scheduling (Job Shop: EDD, SPT, FCFS, LWR)
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Lec 22: Scheduling (Job Shop: EDD, SPT, FCFS, LWR)
Lec 23: Scheduling (Job Shop: LSM, Aker) and Decision Theory (Introduction)
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Lec 23: Scheduling (Job Shop: LSM, Aker) and Decision Theory (Introduction)
Lec 25: Decision Theory (Decision making under Uncertainty, under Risk using EMV and EOL)
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Lec 25: Decision Theory (Decision making under Uncertainty, under Risk using EMV and EOL)
Lec 26: Decision Theory (Decision Tree and Analytic Hierarchy Process)
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Lec 26: Decision Theory (Decision Tree and Analytic Hierarchy Process)
how we get that delta 1 and delta 2
delta 1 & delta 2 are obtained using random number generator. For more details on random number generation, please watch Lecture-36 on Simulated Annealing (Part 2) available in this playlist of mine.
Thank you
Nice Lec Sir
Thanks and welcome
Sir, the discussion is best.
do you know how we get that delta 1 and delta 2
Sir ? How to solve GRG manually like LPP using pen and notebook
LPP is an analytical method where we get result with 100% accuracy, but GRG gives approximate solution as it is a numerical method
@@SarojKumarPatel Thank you Sir. Can I solve GRG manually without using computer?
Excuse me Sir, are you have a video for flow shop heuristic Gupta Method? Or book? I need it to finish my college task. Im very interested that u can explain about Nawaz glow shop scheduling but i need one Method again that is Gupta Heuristic🙏
No, sorry.
The book name u follow
Deb, Kalyanmoy (2012) Optimization for Engineering Design : Algorithms and Examples, PHI, 2nd ed, New Delhi, ISBN: 81-203-0943-X
Not all heroes wear capes, this video is an example
Thanks 😊 sir
GODLEVEL TEACHING
How to tune the SA if i have any constraints in the problem?
The constrained problem need to be transformed into non-constrained problem using penalty function
Thank you so much. Now I will try to implement it in C++ Builder.
Most welcome
tq sir
Hope, you found it worth watching
all this stuff is used in pattern recognition and machine learning.
great lecture
Glad you liked it
thanks for this lectures
Dear Professor, thank you very much for the great lecture over GRGM with clear examples. I would like to ask which variant of GRGM is the one you explain on this video?
The version of GRGM explained in this video is same as that in the text book "Deb, Kalyanmoy (2012) Optimization for Engineering Design : Algorithms and Examples, PHI, 2nd ed, New Delhi, ISBN: 81-203-0943-X"
thanks sir you help me for solve my problem, thanks again from Indonesian Engineering Student
Thank you
Thank you sir for understanding step wise😊
Sir can you give me your channel plz
My channel is publicly available to all
So can you give your channel I'd and password because I want to start a new community and want to become a big UA-camr please🙏
Thank you for the lecture sir
Thank you for this presentation sir. My question is if after eliminating the first tardy job and eliminating the job with longest processing time and there are still tardy jobs. To continue with the process, do I have to identify and eliminate first tardy job first or to eliminate the job with longest processing time sir?
Thank you for asking your doubt. After eliminating job with longest processing time from among the jobs up to 1st tardy job in the sequence, recalculate the tardiness for the remaining 3 jobs and go to Step 2 to repeat the process. If there are still tardy jobs, then again identify the 1st tardy job among these 3 jobs and then eliminate the job with longest processing time and so on. Hope, it's clear now.
@@SarojKumarPatel Yes, thank you sir. I sent the question from Nigeria sir once again thank you for your quick response sir.
Thank you so much, Prof, for the clear explanation. I sincerely appreciate it. Please sir, what are the termination criteria for newton's method?
Sir can you suggest if the jobs can be completed on single machine itself i.e., it need not to go to different machine. And we have N jobs and M machines, objective is to assign all jobs to their compatible machines in such a way that total run time of each machine is minimum. Then which approach has to be followed?
These are two different types of problems. 1) Single Processor Scheduling 2) Multi Processor Scheduling. Both types of problems exist. Depending on type of problem, the approach for finding solution varies.
Sir Can You solve below Problem Please: Consider a state space having 3 states: s1, s2 and s3. The value of each state is V(s1) = 0, V(s2) = 4, V(s3) = 2. There can be transitions from s1 to s2, s2 to s1 and s3, and s3 to s2. Starting at s1, what is the probability that we end up back at s1 after 2 steps of simulated annealing? Assume that we follow a temperature schedule of [10, 5, 1].Next state is chosen uniformly at random whenever there are multiple possibilities. Round answer to 3 digits after decimal point.
How to open optimisation tool?
Thanks for your query. Type "optimtool" at MATLAB command prompt and then press ENTER key to open Optimization Toolbox. Another way to open is press the keys APPS and then OPTIMIZATION. For more details, you may watch my video "Lec 37: Optimization Toolbox of MATLAB-1"
Sir at 4 step at 14:26 € is still < L (v-u) why did u terminate it
Yes you are right. It should have been continued few more iterations till width of interval w=(v-u)< epsilon (i.e., desired accuracy). The procedure would be same. In this video, hand calculations have been explained for initial few iterations only. The answer given here is actually within 3 iterations i.e. minimum point lies in the range (1.563, 2.188). Thank you for raising this query.
what a grt explmation sir
Thank you
Extremely nice explanation of GA. Highly recommended and thank you Professor!
Thank you
Dear Sir, your SA lectures are insightful. I have one query though. Suppose we have 3 variable optimisation problem. So we will be required to find z3 and hence x3 also. In such a case, how to find out z3. Box-Meuller transformation will give us only z1 and z2.
Just like z1&z2, find z3&z4 and use z3&z4 may be used as z1 for next iteration, z5&z6 may be used as z1&z2 in next iteration, and so on
In-depth explanation by Professor. Thank you!
Thank you
Thank you sir... Having started with the 1st lecture, I finally completed the GA today... It took over 1 month... It was fun learning from you...
Excellent. All the best.
sir plese uolod ppt
All PPTs are included in the video lecture
Thanks, sir for this wonderful opening lecture... Hope this start ends in the completion of the lectures and the subsequent completion of my research.
Most welcome
Thank you Sir for the lectures
Most welcome
thank you very much sir, I follw you from algeria. I need a help about hybrid protocol FA and SA algorithms.
Most welcome
Thanks, sir
Most welcome.
Sir is there pdf available for this series
Sorry, pdf is not available. For any doubt, you may feel free to ask me.
I think there is a mistake at the completion time table, 32+6=36? Not 38?
Yes, you are right. 36 should be 38. I am sorry for this mistake and thank you for pointing out the mistake.
@@SarojKumarPatel Sir actually 36 is correct, it should be 32+ 4 instead of 32 + 6 ( because j1's processing time on the M3 İS 4 not 6) .
Thanks for your explanation sir, this is really useful for me. Please share the Matlab code for this method.
Thank you. But sorry, I do not have it's MATLAB code
Sir discuas quadratic interpolation method also
Ok. Thank you for the suggestion
A part of my headache is over. UA-camrs explaining better than my professors is still a better story than twilight
Thank you
thanks sir
All the best
Dear Prof Saroj Kumar Patel! I am M Nawaz of the author of the paper that proposed the heuristic now simply called NEH algorithm. How can I contact you?
So nice of you. It would be my pleasure to contact you.
Sir, any example of Matrix under crossover types?
Please elaborate your question. It's not clear.
Thanks for the explanation.
Thank you
If we fixed job 1 and job 3 has the earliest due date but it has a release date bigger than completion time of the first fixed job do we fix job 2 (because it is available ) for processing until we reach the release date of job 3 or we just fix job 3 because it has the earliest due date
Thank you for asking your doubt. Please tell me the concerned slide serial number so as to better understand your doubt
thanks for the explanation.
You are most welcome. You may feel free to ask me if you still have any doubt.
@@SarojKumarPatel Sir, Thanks for your kind reply.
thanks for the explanation.
You are most welcome