Using Grid Puzzle to Solve Constraint-Based Scheduling Problemcsandit
Constraint programming (CP) is one of the most effe
ctive techniques for solving practical
operational problems. The outstanding feature of th
e method is a set of constraints affecting a
solution of a problem can be imposed without a need
to explicitly defining a linear relation
among variables, i.e. an equation. Nevertheless, th
e challenge of paramount importance in
using this technique is how to present the operatio
nal problem in a solvable Constraint
Satisfaction Problem (CSP) model. The problem model
ling is problem independent and could be
an exhaustive task at the beginning stage of proble
m solving, particularly when the problem is a
real-world practical problem. This paper investigat
es the application of a simple grid puzzle
game when a player attempts to solve a practical sc
heduling problem. The examination
scheduling is presented as an operational game. The
game‘s rules are set up based on the
operational practice. CP is then applied to solve t
he defined puzzle and the results show the
success of the proposed method. The benefit of usin
g a grid puzzle as the model is that the
method can amplify the simplicity of CP in solving
practical problems.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Using Grid Puzzle to Solve Constraint-Based Scheduling Problemcsandit
Constraint programming (CP) is one of the most effe
ctive techniques for solving practical
operational problems. The outstanding feature of th
e method is a set of constraints affecting a
solution of a problem can be imposed without a need
to explicitly defining a linear relation
among variables, i.e. an equation. Nevertheless, th
e challenge of paramount importance in
using this technique is how to present the operatio
nal problem in a solvable Constraint
Satisfaction Problem (CSP) model. The problem model
ling is problem independent and could be
an exhaustive task at the beginning stage of proble
m solving, particularly when the problem is a
real-world practical problem. This paper investigat
es the application of a simple grid puzzle
game when a player attempts to solve a practical sc
heduling problem. The examination
scheduling is presented as an operational game. The
game‘s rules are set up based on the
operational practice. CP is then applied to solve t
he defined puzzle and the results show the
success of the proposed method. The benefit of usin
g a grid puzzle as the model is that the
method can amplify the simplicity of CP in solving
practical problems.
This Presentation discusses he following topics:
Introduction
Need for Problem formulation
Problem Solving Components
Definition of Problem
Problem Limitation
Goal or Solution
Solution Space
Operators
Examples of Problem Formulation
Well-defined Problems and Solution
Examples of Well-Defined Problems
Constraint satisfaction problems (CSPs)
Examples of constraint satisfaction problem
Decision problem
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed
method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem
transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will
optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency
of the suggested method, a lot of test problems have been solved using this method. Comparing the results
of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable
for solving the multi-objective problems.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
Adapted Branch-and-Bound Algorithm Using SVM With Model SelectionIJECEIAES
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
I am Marianna P. I am a Computer Science Exam Expert at programmingexamhelp.com. I hold a Bachelor of Information Technology from, California Institute of Technology, United States. I have been helping students with their exams for the past 12 years. You can hire me to take your exam in Computer Science.
Visit programmingexamhelp.com or email support@programmingexamhelp.com. You can also call on +1 678 648 4277 for any assistance with the Computer Science Exam.
A brief study on linear programming solving methodsMayurjyotiNeog
This small presentation includes a brief study on various linear programming solving methods. These methods (graphical & simplex) are used to solve industrial engineering related problems in practical use.
Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
Dear students get fully solved SMU MBA assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
help.mbaassignments@gmail.com
or
call us at : 08263069601
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
(Prefer mailing. Call in emergency )
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed
method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem
transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will
optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency
of the suggested method, a lot of test problems have been solved using this method. Comparing the results
of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable
for solving the multi-objective problems.
A HYBRID COA/ε-CONSTRAINT METHOD FOR SOLVING MULTI-OBJECTIVE PROBLEMSijfcstjournal
In this paper, a hybrid method for solving multi-objective problem has been provided. The proposed method is combining the ε-Constraint and the Cuckoo algorithm. First the multi objective problem transfers into a single-objective problem using ε-Constraint, then the Cuckoo optimization algorithm will optimize the problem in each task. At last the optimized Pareto frontier will be drawn. The advantage of
this method is the high accuracy and the dispersion of its Pareto frontier. In order to testing the efficiency of the suggested method, a lot of test problems have been solved using this method. Comparing the results of this method with the results of other similar methods shows that the Cuckoo algorithm is more suitable for solving the multi-objective problems.
Adapted Branch-and-Bound Algorithm Using SVM With Model SelectionIJECEIAES
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
I am Marianna P. I am a Computer Science Exam Expert at programmingexamhelp.com. I hold a Bachelor of Information Technology from, California Institute of Technology, United States. I have been helping students with their exams for the past 12 years. You can hire me to take your exam in Computer Science.
Visit programmingexamhelp.com or email support@programmingexamhelp.com. You can also call on +1 678 648 4277 for any assistance with the Computer Science Exam.
A brief study on linear programming solving methodsMayurjyotiNeog
This small presentation includes a brief study on various linear programming solving methods. These methods (graphical & simplex) are used to solve industrial engineering related problems in practical use.
Binary Class and Multi Class Strategies for Machine LearningPaxcel Technologies
This presentation discusses the following -
Possible strategies to follow when working on a new machine learning problem.
The common problems with classifiers (how to detect them and eliminate them).
Popular approaches on how to use binary classifiers to problems with multi class classification.
Dear students get fully solved SMU MBA assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
2. A constraint search does not refer to any specific search algorithm but to a
layer of complexity added to existing algorithms that limit the possible
solution set.
Heuristic and acquired knowledge can be combined to produce the desired
result a constraint satisfaction problem is a special kind of search problem in
which states are defined by the values of a set of variables and the goal state
specifies a set of constraints that the value must obey.
There are many problems in AI in which the goal state is not specified in the
problem and it requires to be discovered according to some specific
constraint.
Examples of some constraint satisfaction search include design problem,
labeling graphs, robot path planning and cryptarithmatic problem etc.
3. a constraint satisfaction problem (or CSP) is defined by a set of variables, X1;X2; :
: : ;Xn,
and a set of constraints, C1;C2; : : : ;Cm.
Each variable Xi has a nonempty domain Di of possible values.
Each constraint Ci involves some subset of t variables and specifies the allowable
combinations of values for that subset.
A state of the problem is defined by an assignment of values to some or all of the
variables, {Xi = vi;Xj = vj ; : : :}
An assignment that does not violate any constraints is called a consistent or legal
assignment.
A complete assignment is one in which every variable is mentioned, and a solution
to a CSP is a complete assignment that satisfies all the constraints. Some CSPs also
require a solution that maximizes an objectivenfunction.
4. Many problems in AI can be considered as problems of constraint satisfaction,
in which the goal state satisfies a given set of constraint. constraint
satisfaction problems can be solved by using any of the search strategies.
5. Outline
The general form of the constraint satisfaction procedure is as follows:
Until a complete solution is found or until all paths have led to lead ends, do
1. select an unexpanded node of the search graph.
2. Apply the constraint inference rules to the selected node to generate all
possible new constraints.
3. If the set of constraints contains a contradiction, then report that this path
is a dead end.
4. If the set of constraints describes a complete solution then report success.
5. If neither a constraint nor a complete solution has been found then apply
the rules to generate new partial solutions. Insert these partial solutions into
the search graph.
10. What is to be done?
Assign decimal digit to each of the letters in such a way that the answer to
the problem is correct to the same letter occurs more than once , it must be
assign the same digit each time . no two different letters may be assigned the
same digit.
11. CONSTRAINTS
1. no two digit can be assigned to same letter.
2. only single digit number can be assign to a letter.
3. Assumption can be made at various levels such that they do not contradict
each other.
4. The problem can be decomposed into secured constraints. A constraint
satisfaction approach may be used.
5. Any of search techniques may be used.
6. Backtracking may be performed as applicable us applied search techniques.
7. Rule of arithmetic may be followed.
12. Initial state of problem.
D=? E=? Y=? N=? R=? O=? S=? M=? C1=? C2=?
C1 ,C 2, C3 stands for the carry variables respectively.
Goal State: the digits to the letters must be assigned in such a manner so that
the sum is satisfied.
13. Solution
We are following the depth-first method to solve the problem.
1. initial guess m=1 because the sum of two single digits can generate at most
a carry '1'.
2. When n=1 o=0 or 1 because the largest single digit number added to m=1
can generate the sum of either 0 or 1 depend on the carry received from the
carry sum. By this we conclude that o=0 because m is already 1 hence we
cannot assign same digit another letter(rule no.)
3. We have m=1 and o=0 to get o=0 we have s=8 or 9, again depending on the
carry received from the earlier sum.
14. The same process can be repeated further. The problem has to be composed
into various constraints. And each constraints is to be satisfied by guessing the
possible digits that the letters can be assumed that the initial guess has been
already made . rest of the process is being shown in the form of a tree, using
depth-first search for the clear understandability of the solution process.