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Dear students get fully solved assignments
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This document discusses various applications of linear programming (LP) in areas such as marketing, finance, and operations management. In marketing, LP can be used for media selection and marketing research problems. It provides examples of using LP to determine an optimal advertising plan that maximizes exposure quality within a budget. In finance, LP is used for portfolio selection and financial planning problems. Operations management applications covered include make-or-buy decisions, production scheduling, workforce assignment, and blending problems.
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This document presents an overview of linear programming, including:
- Linear programming involves choosing a course of action when the mathematical model contains only linear functions.
- The objective is to maximize or minimize some quantity subject to constraints. A feasible solution satisfies all constraints while an optimal solution results in the largest/smallest objective value.
- Problem formulation involves translating a verbal problem statement into mathematical terms by defining decision variables and writing the objective and constraints in terms of these variables.
- An example problem is presented to maximize profit by determining the optimal number of products A and B to manufacture, given constraints on money invested and labor hours. The objective and constraints are written mathematically to formulate the problem as a linear program.
This document summarizes an algorithm for outsourcing and hiring workers in an online labor market. The TFO (Team Formation with Outsourcing) model aims to balance workloads and compatibility between workers while minimizing labor costs. It considers hiring workers at a fixed salary with costs for hiring and firing, and outsourcing work at an extra premium. The algorithm uses an online primal-dual technique to determine whether to hire or outsource at each step, based on these various costs. It was shown to achieve good competitive ratios compared to alternative approaches through experiments.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists a mail ID and phone number to contact along with details about the subject code, semester, credits, and marks for an Operations Research assignment from the Winter 2013 semester. The assignment contains 6 questions and provides evaluation criteria. Students are instructed to answer all questions and note the word count requirement for longer questions.
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Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
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This document discusses various applications of linear programming (LP) in areas such as marketing, finance, and operations management. In marketing, LP can be used for media selection and marketing research problems. It provides examples of using LP to determine an optimal advertising plan that maximizes exposure quality within a budget. In finance, LP is used for portfolio selection and financial planning problems. Operations management applications covered include make-or-buy decisions, production scheduling, workforce assignment, and blending problems.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
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Call us at : 08263069601
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This document presents an overview of linear programming, including:
- Linear programming involves choosing a course of action when the mathematical model contains only linear functions.
- The objective is to maximize or minimize some quantity subject to constraints. A feasible solution satisfies all constraints while an optimal solution results in the largest/smallest objective value.
- Problem formulation involves translating a verbal problem statement into mathematical terms by defining decision variables and writing the objective and constraints in terms of these variables.
- An example problem is presented to maximize profit by determining the optimal number of products A and B to manufacture, given constraints on money invested and labor hours. The objective and constraints are written mathematically to formulate the problem as a linear program.
This document summarizes an algorithm for outsourcing and hiring workers in an online labor market. The TFO (Team Formation with Outsourcing) model aims to balance workloads and compatibility between workers while minimizing labor costs. It considers hiring workers at a fixed salary with costs for hiring and firing, and outsourcing work at an extra premium. The algorithm uses an online primal-dual technique to determine whether to hire or outsource at each step, based on these various costs. It was shown to achieve good competitive ratios compared to alternative approaches through experiments.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists a mail ID and phone number to contact along with details about the subject code, semester, credits, and marks for an Operations Research assignment from the Winter 2013 semester. The assignment contains 6 questions and provides evaluation criteria. Students are instructed to answer all questions and note the word count requirement for longer questions.
This document provides an overview of various quantitative techniques topics including linear programming, transportation problem, forecasting, assignment problem, queuing theory, decision theory, inventory management, simulation, and network analysis. It describes the basic concepts, steps, and methods for each topic at a high level. For example, it defines linear programming as a technique for optimal allocation of limited resources given a criterion, describes the steps and assumptions. It also provides examples to illustrate some concepts.
Linear Programming Module- A Conceptual FrameworkSasquatch S
This document provides an overview of linear programming and how to formulate and solve linear programming problems. Key points:
- Linear programming involves optimizing an objective function subject to constraints, where all relationships are linear. It can be used to solve problems like resource allocation.
- To formulate a problem, you identify decision variables, write the objective function and constraints in terms of the variables, and specify non-negativity.
- Graphical methods can solve small 2-variable problems by finding the optimal point in the feasible region bounded by the constraint lines. Larger problems use computer solutions like the simplex method.
- To solve in Excel, you set up the model with decision variables, objective function
This document discusses linear programming techniques for managerial decision making. Linear programming can determine the optimal allocation of scarce resources among competing demands. It consists of linear objectives and constraints where variables have a proportionate relationship. Essential elements of a linear programming model include limited resources, objectives to maximize or minimize, linear relationships between variables, homogeneity of products/resources, and divisibility of resources/products. The linear programming problem is formulated by defining variables and constraints, with the objective of optimizing a linear function subject to the constraints. It is then solved using graphical or simplex methods through an iterative process to find the optimal solution.
The document discusses linear programming and the simplex method for solving linear programming problems. It begins with definitions of linear programming and its history. It then provides an example production planning problem that can be formulated as a linear programming problem. The document goes on to describe the standard form of a linear programming problem and terminology used. It explains how the simplex method works through iterative improvements to find the optimal solution. This is illustrated both geometrically and through an algebraic example solved using the simplex method.
This document provides information about getting solved assignments for the MBA Semester 2 Operations Research subject. It includes 6 questions related to operations research concepts like linear programming, transportation problems, assignment problems, and simulation. Students can get assignments solved at Rs. 125 each by emailing or calling the provided contact information. The questions cover topics like the framework of operations research, graphical and algebraic methods for linear programming problems, important terms in transportation problems, the Hungarian method for assignment problems, Monte Carlo simulation, assumptions of game theory, characteristics of Markov chains, and job prioritization rules.
The reference of this book is from Dominick Salvatore's Managerial Economics. It is in chapter 8 with the following topic: Linear Programming, Production process, Feasible region, Optimal solution, Objective function, Inequality constraints, Nonnegativity constraints, Decision variables, Binding constraints, Slack variable, Simplex method, Primal problem, Dual problem, Shadow price, Duality theorem and Logistic management.
The document provides an introduction to linear programming for data scientists. It defines linear programming and some key terms. It presents an example problem involving maximizing profit from chocolate production given resource constraints. The document outlines the process for formulating a linear programming problem and solving it graphically or using R. It presents another example involving maximizing profits from toy production. The document concludes by describing how to solve linear programs using OpenSolver.
Application of linear programming technique for staff training of register se...Enamul Islam
This study aims to minimize training costs for staff at Patuakhali Science and Technology University using linear programming. It identifies two decision variables (permanent and non-permanent staff to be trained) and develops constraints based on time available and staff in different departments. The linear programming model is solved to find the optimal solution: 1 permanent staff should be sent for 5 days of training among departments to minimize costs. The research suggests this approach can help determine optimal staffing levels for future training programs.
Karmarkar's Algorithm For Linear Programming ProblemAjay Dhamija
The document discusses Karmarkar's algorithm, an interior point method for solving linear programming problems. It introduces key concepts of Karmarkar's algorithm such as projecting a vector onto the feasible region, Karmarkar's centering transformation, and Karmarkar's potential function. The original algorithm assumes the linear program is in canonical form and generates a sequence of interior points with decreasing objective function values using a projective transformation to move points to the center of the feasible region.
This document discusses resource optimization and linear programming. It defines optimization as finding the best solution to a problem given constraints. Linear programming is introduced as a mathematical technique to optimize allocation of scarce resources. The key components of a linear programming model are described as decision variables, an objective function, and constraints. Graphical and algebraic methods for solving linear programming problems are also summarized.
This document provides an overview of linear programming and the simplex method. It begins with introducing linear programming and its applications. Examples of linear programming problems are presented, including product mix, blending, production scheduling, transportation, and network flow problems. The steps for developing a linear programming model and graphical solution method are described. The document then focuses on explaining the simplex method, using a product mix problem as an example. It walks through applying the simplex method to find the optimal solution in multiple steps.
The document provides an outline of topics related to linear programming, including:
1) An introduction to linear programming models and examples of problems that can be solved using linear programming.
2) Developing linear programming models by determining objectives, constraints, and decision variables.
3) Graphical and simplex methods for solving linear programming problems.
4) Using a simplex tableau to iteratively solve a sample product mix problem to find the optimal solution.
Linear programming class 12 investigatory projectDivyans890
This document provides an introduction to linear programming, including its definition, characteristics, formulation, and uses. Linear programming is a technique for determining an optimal plan that maximizes or minimizes an objective function subject to constraints. It involves expressing a problem mathematically and using linear algebra to determine the optimal values for the decision variables. Common applications of linear programming include production planning, portfolio optimization, and transportation scheduling.
The document discusses the assignment problem and the Hungarian method for solving it. It provides definitions for key terms like balanced vs unbalanced assignment problems and dummy jobs/persons. It also outlines the mathematical formulation of assignment problems and lists some common application areas. The summary describes the Hungarian method as follows:
1) It is used to solve assignment problems by finding the minimum cost matching between people/objects and tasks.
2) The method works on a cost matrix representing all possible assignments.
3) It uses the principle that the optimal solution does not change if a constant is subtracted from rows/columns with a total cost of zero.
Hybrid Methods of Some Evolutionary Computations AndKalman Filter on Option P...IJMERJOURNAL
ABSTRACT: The search for a better option price continues within the financial institution. In pricing a put option, holders of the underlying stock always want to make the best decision by maximizing profit. We present an optimal hybrid model among the following combinations: Kalman Filter-Genetic Programming(KF-GP), Kalman Filter-Evolutionary Strategy(KF-ES) and Evolutionary Strategy -Genetic Programming(ES- GP). Our results indicate that the hybrid method involving Kalman Filter-Evolutionary Strategy(KF-ES) is the best model for any investor. Sensitivity analysis was conducted on the model parameters to ascertain the rigidity of the model.
The production possibilities frontier (PPF) indicates the maximum output combinations of two goods or services an economy can achieve with full employment of available resources. It assumes resources and technology are fixed. The opportunity cost refers to the next best alternative forgone in making a choice. It provides a basis for decision making, price determination, and efficient allocation of resources by analyzing the costs of all alternatives.
The document discusses various concepts related to assignment and transportation problems including:
1) The steps to solve an assignment problem using the Hungarian method.
2) Examples of assignment problems involving personnel assignment and swimmer selection.
3) The formulation of a transportation problem to minimize shipping costs involving plants, cities, supply, and demand.
4) Examples of transportation problems involving flight assignment and power plant shipping costs.
5) How to solve transshipment problems by converting them into transportation problems.
This document provides an overview of the topics covered in Unit V: Linear Programming. It begins with an introduction to operations research and some example problems that can be modeled as linear programs. It then discusses formulations of linear programs, including the standard and slack forms. The document outlines the simplex algorithm for solving linear programs and how to convert between standard and slack forms. It provides examples demonstrating these concepts. The key topics covered are linear programming models, formulations, and the simplex algorithm.
The document discusses strategic decisions in the DRAM industry in the late 1990s. Fujitsu closed its factory in Durham, UK due to long-run prices being below average costs, but continued production in Gresham, OR where costs were lower. Texas Instruments sold some plants to Micron, who believed it could achieve greater economies of scale. Micron did not buy all TI's plants as some had higher average costs. The document also covers short-run and long-run supply curve analysis.
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
This document provides an overview of various quantitative techniques topics including linear programming, transportation problem, forecasting, assignment problem, queuing theory, decision theory, inventory management, simulation, and network analysis. It describes the basic concepts, steps, and methods for each topic at a high level. For example, it defines linear programming as a technique for optimal allocation of limited resources given a criterion, describes the steps and assumptions. It also provides examples to illustrate some concepts.
Linear Programming Module- A Conceptual FrameworkSasquatch S
This document provides an overview of linear programming and how to formulate and solve linear programming problems. Key points:
- Linear programming involves optimizing an objective function subject to constraints, where all relationships are linear. It can be used to solve problems like resource allocation.
- To formulate a problem, you identify decision variables, write the objective function and constraints in terms of the variables, and specify non-negativity.
- Graphical methods can solve small 2-variable problems by finding the optimal point in the feasible region bounded by the constraint lines. Larger problems use computer solutions like the simplex method.
- To solve in Excel, you set up the model with decision variables, objective function
This document discusses linear programming techniques for managerial decision making. Linear programming can determine the optimal allocation of scarce resources among competing demands. It consists of linear objectives and constraints where variables have a proportionate relationship. Essential elements of a linear programming model include limited resources, objectives to maximize or minimize, linear relationships between variables, homogeneity of products/resources, and divisibility of resources/products. The linear programming problem is formulated by defining variables and constraints, with the objective of optimizing a linear function subject to the constraints. It is then solved using graphical or simplex methods through an iterative process to find the optimal solution.
The document discusses linear programming and the simplex method for solving linear programming problems. It begins with definitions of linear programming and its history. It then provides an example production planning problem that can be formulated as a linear programming problem. The document goes on to describe the standard form of a linear programming problem and terminology used. It explains how the simplex method works through iterative improvements to find the optimal solution. This is illustrated both geometrically and through an algebraic example solved using the simplex method.
This document provides information about getting solved assignments for the MBA Semester 2 Operations Research subject. It includes 6 questions related to operations research concepts like linear programming, transportation problems, assignment problems, and simulation. Students can get assignments solved at Rs. 125 each by emailing or calling the provided contact information. The questions cover topics like the framework of operations research, graphical and algebraic methods for linear programming problems, important terms in transportation problems, the Hungarian method for assignment problems, Monte Carlo simulation, assumptions of game theory, characteristics of Markov chains, and job prioritization rules.
The reference of this book is from Dominick Salvatore's Managerial Economics. It is in chapter 8 with the following topic: Linear Programming, Production process, Feasible region, Optimal solution, Objective function, Inequality constraints, Nonnegativity constraints, Decision variables, Binding constraints, Slack variable, Simplex method, Primal problem, Dual problem, Shadow price, Duality theorem and Logistic management.
The document provides an introduction to linear programming for data scientists. It defines linear programming and some key terms. It presents an example problem involving maximizing profit from chocolate production given resource constraints. The document outlines the process for formulating a linear programming problem and solving it graphically or using R. It presents another example involving maximizing profits from toy production. The document concludes by describing how to solve linear programs using OpenSolver.
Application of linear programming technique for staff training of register se...Enamul Islam
This study aims to minimize training costs for staff at Patuakhali Science and Technology University using linear programming. It identifies two decision variables (permanent and non-permanent staff to be trained) and develops constraints based on time available and staff in different departments. The linear programming model is solved to find the optimal solution: 1 permanent staff should be sent for 5 days of training among departments to minimize costs. The research suggests this approach can help determine optimal staffing levels for future training programs.
Karmarkar's Algorithm For Linear Programming ProblemAjay Dhamija
The document discusses Karmarkar's algorithm, an interior point method for solving linear programming problems. It introduces key concepts of Karmarkar's algorithm such as projecting a vector onto the feasible region, Karmarkar's centering transformation, and Karmarkar's potential function. The original algorithm assumes the linear program is in canonical form and generates a sequence of interior points with decreasing objective function values using a projective transformation to move points to the center of the feasible region.
This document discusses resource optimization and linear programming. It defines optimization as finding the best solution to a problem given constraints. Linear programming is introduced as a mathematical technique to optimize allocation of scarce resources. The key components of a linear programming model are described as decision variables, an objective function, and constraints. Graphical and algebraic methods for solving linear programming problems are also summarized.
This document provides an overview of linear programming and the simplex method. It begins with introducing linear programming and its applications. Examples of linear programming problems are presented, including product mix, blending, production scheduling, transportation, and network flow problems. The steps for developing a linear programming model and graphical solution method are described. The document then focuses on explaining the simplex method, using a product mix problem as an example. It walks through applying the simplex method to find the optimal solution in multiple steps.
The document provides an outline of topics related to linear programming, including:
1) An introduction to linear programming models and examples of problems that can be solved using linear programming.
2) Developing linear programming models by determining objectives, constraints, and decision variables.
3) Graphical and simplex methods for solving linear programming problems.
4) Using a simplex tableau to iteratively solve a sample product mix problem to find the optimal solution.
Linear programming class 12 investigatory projectDivyans890
This document provides an introduction to linear programming, including its definition, characteristics, formulation, and uses. Linear programming is a technique for determining an optimal plan that maximizes or minimizes an objective function subject to constraints. It involves expressing a problem mathematically and using linear algebra to determine the optimal values for the decision variables. Common applications of linear programming include production planning, portfolio optimization, and transportation scheduling.
The document discusses the assignment problem and the Hungarian method for solving it. It provides definitions for key terms like balanced vs unbalanced assignment problems and dummy jobs/persons. It also outlines the mathematical formulation of assignment problems and lists some common application areas. The summary describes the Hungarian method as follows:
1) It is used to solve assignment problems by finding the minimum cost matching between people/objects and tasks.
2) The method works on a cost matrix representing all possible assignments.
3) It uses the principle that the optimal solution does not change if a constant is subtracted from rows/columns with a total cost of zero.
Hybrid Methods of Some Evolutionary Computations AndKalman Filter on Option P...IJMERJOURNAL
ABSTRACT: The search for a better option price continues within the financial institution. In pricing a put option, holders of the underlying stock always want to make the best decision by maximizing profit. We present an optimal hybrid model among the following combinations: Kalman Filter-Genetic Programming(KF-GP), Kalman Filter-Evolutionary Strategy(KF-ES) and Evolutionary Strategy -Genetic Programming(ES- GP). Our results indicate that the hybrid method involving Kalman Filter-Evolutionary Strategy(KF-ES) is the best model for any investor. Sensitivity analysis was conducted on the model parameters to ascertain the rigidity of the model.
The production possibilities frontier (PPF) indicates the maximum output combinations of two goods or services an economy can achieve with full employment of available resources. It assumes resources and technology are fixed. The opportunity cost refers to the next best alternative forgone in making a choice. It provides a basis for decision making, price determination, and efficient allocation of resources by analyzing the costs of all alternatives.
The document discusses various concepts related to assignment and transportation problems including:
1) The steps to solve an assignment problem using the Hungarian method.
2) Examples of assignment problems involving personnel assignment and swimmer selection.
3) The formulation of a transportation problem to minimize shipping costs involving plants, cities, supply, and demand.
4) Examples of transportation problems involving flight assignment and power plant shipping costs.
5) How to solve transshipment problems by converting them into transportation problems.
This document provides an overview of the topics covered in Unit V: Linear Programming. It begins with an introduction to operations research and some example problems that can be modeled as linear programs. It then discusses formulations of linear programs, including the standard and slack forms. The document outlines the simplex algorithm for solving linear programs and how to convert between standard and slack forms. It provides examples demonstrating these concepts. The key topics covered are linear programming models, formulations, and the simplex algorithm.
The document discusses strategic decisions in the DRAM industry in the late 1990s. Fujitsu closed its factory in Durham, UK due to long-run prices being below average costs, but continued production in Gresham, OR where costs were lower. Texas Instruments sold some plants to Micron, who believed it could achieve greater economies of scale. Micron did not buy all TI's plants as some had higher average costs. The document also covers short-run and long-run supply curve analysis.
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 ”
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Call us at : 08263069601
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This document provides information about getting fully solved assignments from an assignment help service. It lists an email address and phone number to contact for assistance with assignments. It also provides details about the available programs, subjects, semesters, credits, and other assignment details like word count requirements. Students are advised to mail their request with details of their semester and specialization to get solved assignments. Calling is listed as an emergency option.
This document provides information about obtaining fully solved assignments for the MBA semester 2 Operations Research course. It includes 6 sample questions from the course along with evaluation criteria for each. Students can email their semester and specialization details to help.mbaassignments@gmail.com or call 08263069601 to receive solved assignments. The questions cover topics like the methodology of operations research, linear programming problem formulation, finding initial basic feasible solutions using different methods, queueing models, Monte Carlo simulation, and game theory concepts.
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This document provides information about getting fully solved assignments from an assignment help service. Students are instructed to send their semester and specialization name to the provided email address or call the given phone number to receive help with their assignments. Mailing is preferred over calling except in emergencies. The document then provides a sample assignment question related to operations research on the topics of linear programming, transportation problem, simulation, integer programming, PERT/CPM, and queuing systems.
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This document provides information about getting fully solved assignments. Students should send their semester and specialization name to the provided email address or call the given phone number to receive assignments. The assignments are for an Operations Research course and are due by April 30th, 2014. The document includes 5 sample questions and answers as examples of the type of assignments available.
Dear students get fully solved SMU MBA Fall 2014 assignments
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Dear students get fully solved SMU MBA Fall 2014 assignments
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Dear students get fully solved assignments
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Dear students get fully solved assignments
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Dear students get fully solved assignments
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Dear students get fully solved assignments
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Dear students get fully solved SMU MBA assignments
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The document describes a sample PMP exam question and answer regarding risk response selection. A project team is considering two risk responses for the risk register: one with two criteria costing $5,000, and one with four criteria costing $2,000. The least likely action for the team to take to select the best response is running a Monte Carlo simulation, as there are only two options to consider rather than the many iterations needed for such an analysis.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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1. Dear students get fully solved assignments
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DRIVE FALL 2017
PROGRAM MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER II
SUBJECT CODE & NAME MBA205 & OPERATIONS RESEARCH
CREDITS 2, 4 CREDITS
MARKS 30
Note: Answer all questions. Kindly note that answers for 10 marks questions should be
approximately of 400 words. Each question is followed by evaluation scheme.
Assignment Set -1
Question 1 Define the Linear programming problem in operation Research. Also, explain various
assumptions, advantages and limitations of linear programming problem.
A Linear programming problem in operation Research
Assumptions of linear programming problem
Advantages of linear programming problem
Limitations of linear programming problem
Answer: In organisations,managers are required to make judicioususe of scarce resources, such as
men,materials,machinesandcapital,tominimisecosts andmaximise profits.A techniquethatisused
to select the best alternative from a set
Question 2 a. Discuss the concept of Degeneracy in transportation problem
b. The ABC Tool Company has a sales force of 25 men who work out from Regional offices. The
company produces four basic products linesof hand tools. Mr. Jain, the salesmanager, feelsthat 6
salesmenare neededtodistribute productline 1, 10 salesmenare neededtodistribute productline
2, 4 salesmen to product line 3 and 5 salesmen to product line 4. The cost per day of assigning
salesmen from each of the offices for selling each of the product lines are as follows;
Regional office Product Lines
𝑷𝟏 𝑷𝟐 𝑷𝟑 𝑷𝟒
R1 20 21 16 18
R2 17 28 14 16
R3 29 23 19 20
Now, 10 salesmen are allowed to office R1, 9 salesmen to office , and 7 salesmen to office .
How many salesmen should be assigned from each office to selling each product line in order to
minimize costs?
Degeneracy in transportation problem
Optimum allocation.
2. Optimum transportation cost
Answer: a) Degeneracy in transportation problem
A basic solution to an m-origin, n destination transportation problem can have at the most m+n-1
positive basicvariables(non-zero),otherwisethe basicsolutiondegenerates.Itfollowsthatwhenever
the numberof basic cellsislessthan m + n – 1, the transportationproblemisa degenerate one.The
degeneracy can develop in two ways:
Case 1 - The degeneracy develops while
Question 3 a. Elaborate the meaning of Simulation.
b. What are different Practical applications of simulation
A Meaning of Simulation.
Practical applications of simulation
Answer: a) Simulationisa representationof real-life situations.Itis a methodinwhicha replicaof a
real-worldprocessorsystemisdevelopedovera periodof time.The simulatedmodelactsinthe same
mannerasthe selectedphysical or abstractprocessorsystembehavesinreality.Forexample,aircraft
pilotsare giventrainingthroughsimulationmodelsastrainingwithreal aircrafts canincur huge costs
as well as involve various risks. Similarly, in education sector, teachers are trained on the simulated
modelsof students(agroupof individualswhoimitateasstudents).Thisavoidstheriskof spoilingthe
future of students if the teacher is not able to
Assignment Set -2
Question 1 a. Define the meaning of assignment problem in operation Research.
b. A Departmental head has four subordinates and four task to be performed. The subordinates
differinefficiencyandthe tasksdifferintheir intrinsicdifficulty.Hisestimate ofthe timeseachman
would take to perform each task is given in the following matrix-
Tasks Subordinates
I II III IV
A 8 26 17 11
B 13 28 4 26
C 38 19 18 15
D 19 26 24 10
How should the tasks be allocated to subordinates to minimize the total man-hours?
A Description of assignment problem
Optimum allocation through Hungarian method
Answer: a) An assignment problem is a special type of transportation problem. In an assignment
problem,the same numberof facilities(sourcesof supply) needstobe allocatedtothe same number
of jobs(pointsof destinations) so thatthe transportationcostisminimisedorthe profitismaximised.
An assignment problem can occur while assigning:
Machines to factory orders
Salespeople to sales territories
Teachers to classes
Police vehicles to patrolling areas
3.
Question 2. Define following criteria’s used for decision making under Uncertainty
a. Optimism (maximax or minimin) criterion
b. Pessimism (maximin or minimax) criterion
c. Equal probabilities (Laplace) criterion
d. Coefficient of optimism (Hurwicz) criterion
e. Regret (salvage) criterion
Answer: a) Optimism (maximaxor minimin) criterion: Here,the decisionmakertriesto achieve the
largest possible profit (Maximax) or minimum possible cost (minimin).If the entries in the payoff
matrix are the one which the decisionmakerwantsas large as possible,forexample,profitsorsales
revenue,he/sheselectsthe alternative thatrepresentsthe maximumof the maximumpayoff.Incase
where the entriesof the payoff matrix are one which the decisionmakerwants as small as possible,
he/she goes for the minimum of the
Question3a. Explainthe importance andutilityofthe replacementmodel inbusinessorganizations.
b. The maintenance cost and re-sale value per year of a machine whose purchase price is Rs. 7000
is given below-
Year 1 2 3 4 5 6 7 8
Maintenance cost (Rs.) 900 1200 1600 2100 2800 3700 4700 5900
Resale value (Rs.) 4000 2000 1200 600 500 400 400 400
Importance and utility of the replacement model Replacement Year
Answer: a) In an organisation, replacement problems arise when fixed assets, such as machines,
equipment, and other tools, need to be replaced due to reduced efficiency, failure or breakdown.
Sometimes,replacementtakesplace when more efficientequipmentisavailable inthe marketorthe
maintenance of the existing equipment is incurring a huge cost on an organisation. However, an
organisation needs to decide when the
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