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Greedy algorithms work by making locally optimal choices at each step to arrive at a global optimal solution. They require that the problem exhibits the greedy choice property and optimal substructure. Examples that can be solved with greedy algorithms include fractional knapsack problem, minimum spanning tree, and activity selection. The fractional knapsack problem is solved greedily by sorting items by value/weight ratio and filling the knapsack completely. The 0/1 knapsack problem differs in that items are indivisible.
This document contains questions and answers related to the key concepts in linear programming. It discusses topics like shadow prices, the role of topology in linear programming, goal programming for problems with multiple objectives, and the simplex method for solving linear programming problems. Several examples of linear programming problems are also provided relating to production planning, hospital management, and repellent distribution.
This document describes a linear programming problem (LPP) to minimize cost. The problem involves determining the optimal amounts of two fertilizer brands, SuperGro and CropQuick, to purchase to minimize total cost while meeting nitrogen and phosphate requirements. The LPP constructs decision variables for amounts of each brand, an objective function to minimize total cost, and constraints on nitrogen and phosphate levels. The optimal solution is to purchase 8 bags of CropQuick for a minimum total cost of 24.
This document provides an overview of linear programming, including:
- It describes the linear programming model which involves maximizing a linear objective function subject to linear constraints.
- It provides examples of linear programming problems like product mix, blending, transportation, and network flow problems.
- It explains how to develop a linear programming model by defining decision variables, the objective function, and constraints.
- It discusses solutions methods like the graphical and simplex methods. The simplex method involves iteratively moving to adjacent extreme points to maximize the objective function.
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.
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Greedy algorithms work by making locally optimal choices at each step to arrive at a global optimal solution. They require that the problem exhibits the greedy choice property and optimal substructure. Examples that can be solved with greedy algorithms include fractional knapsack problem, minimum spanning tree, and activity selection. The fractional knapsack problem is solved greedily by sorting items by value/weight ratio and filling the knapsack completely. The 0/1 knapsack problem differs in that items are indivisible.
This document contains questions and answers related to the key concepts in linear programming. It discusses topics like shadow prices, the role of topology in linear programming, goal programming for problems with multiple objectives, and the simplex method for solving linear programming problems. Several examples of linear programming problems are also provided relating to production planning, hospital management, and repellent distribution.
This document describes a linear programming problem (LPP) to minimize cost. The problem involves determining the optimal amounts of two fertilizer brands, SuperGro and CropQuick, to purchase to minimize total cost while meeting nitrogen and phosphate requirements. The LPP constructs decision variables for amounts of each brand, an objective function to minimize total cost, and constraints on nitrogen and phosphate levels. The optimal solution is to purchase 8 bags of CropQuick for a minimum total cost of 24.
This document provides an overview of linear programming, including:
- It describes the linear programming model which involves maximizing a linear objective function subject to linear constraints.
- It provides examples of linear programming problems like product mix, blending, transportation, and network flow problems.
- It explains how to develop a linear programming model by defining decision variables, the objective function, and constraints.
- It discusses solutions methods like the graphical and simplex methods. The simplex method involves iteratively moving to adjacent extreme points to maximize the objective function.
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 outlines course material for Operations Research. It covers linear programming models, including graphical and simplex methods for solving linear programs. Specific chapters outlined include linear programming introduction and formulation, the simplex method, duality, transportation and network models. Examples of linear programming problems are provided, such as production planning, diet formulation, and blending problems. The key concepts of decision variables, objectives, and constraints in linear programming are defined.
Linear programming is a technique that uses linear functions to depict complex relationships and find optimal solutions. It involves formulating a mathematical model with an objective function, decision variables, and constraints. A linear programming problem is presented involving a chocolate manufacturing company that wants to maximize profit by determining how many units of two types of chocolates to produce given constraints on available milk and cocoa. The problem is formulated as a linear program and solved graphically to find the optimal solution.
The document discusses linear programming problems and how to formulate them. It provides definitions of key terms like linear, programming, objective function, decision variables, and constraints. It then explains the steps to formulate a linear programming problem, including defining the objective, decision variables, mathematical objective function, and constraints. Several examples of formulated linear programming problems are provided to maximize profit or minimize costs subject to various constraints.
This document provides an overview of linear programming and the graphical method for solving two-variable linear programming problems. It defines linear programming as involving maximizing or minimizing a linear objective function subject to linear constraints. The graphical method is described as using a graph in the first quadrant to find the feasible region defined by the constraints and then determine the optimal solution by evaluating the objective function at the boundary points. An example problem is presented to demonstrate finding the feasible region and optimal solution graphically. Special cases like alternative optima and infeasible/unbounded problems are also mentioned.
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.
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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.
The document provides information about operations research (OR) including:
(1) OR uses mathematical models and statistics to aid decision-making, typically to optimize performance of complex systems.
(2) The basic OR process involves recognizing a problem, formulating it, constructing a model, finding a solution, defining the process, implementing it, and refining it.
(3) Linear programming applications discussed include marketing, finance, operations management, and blending problems. Graphical and algebraic methods are used to solve transportation problems.
Operations Management VTU BE Mechanical 2015 Solved paperSomashekar S.M
The document provides information about operations management concepts including scientific management, productivity, ABC analysis, economic order quantity, and materials requirements planning. It defines each concept and provides examples to illustrate how they are applied. Scientific management aims to improve efficiency through systematic analysis of work processes. Productivity is a measure of output per unit of input. ABC analysis categorizes inventory items based on their value and usage to determine appropriate control methods. Economic order quantity and ordering cycle determine optimal replenishment amounts and frequencies to minimize total inventory costs. Materials requirements planning is a technique to plan material needs at different production levels based on a product structure tree.
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This document contains questions and answers related to the key concepts in linear programming. It discusses topics like shadow prices, the role of topology in linear programming, goal programming for problems with multiple objectives, and the simplex method for solving linear programming problems. Several examples of linear programming problems are also provided relating to production planning, hospital management, and repellent distribution.
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Dear students get fully solved SMU MBA assignments
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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 outlines course material for Operations Research. It covers linear programming models, including graphical and simplex methods for solving linear programs. Specific chapters outlined include linear programming introduction and formulation, the simplex method, duality, transportation and network models. Examples of linear programming problems are provided, such as production planning, diet formulation, and blending problems. The key concepts of decision variables, objectives, and constraints in linear programming are defined.
Linear programming is a technique that uses linear functions to depict complex relationships and find optimal solutions. It involves formulating a mathematical model with an objective function, decision variables, and constraints. A linear programming problem is presented involving a chocolate manufacturing company that wants to maximize profit by determining how many units of two types of chocolates to produce given constraints on available milk and cocoa. The problem is formulated as a linear program and solved graphically to find the optimal solution.
The document discusses linear programming problems and how to formulate them. It provides definitions of key terms like linear, programming, objective function, decision variables, and constraints. It then explains the steps to formulate a linear programming problem, including defining the objective, decision variables, mathematical objective function, and constraints. Several examples of formulated linear programming problems are provided to maximize profit or minimize costs subject to various constraints.
This document provides an overview of linear programming and the graphical method for solving two-variable linear programming problems. It defines linear programming as involving maximizing or minimizing a linear objective function subject to linear constraints. The graphical method is described as using a graph in the first quadrant to find the feasible region defined by the constraints and then determine the optimal solution by evaluating the objective function at the boundary points. An example problem is presented to demonstrate finding the feasible region and optimal solution graphically. Special cases like alternative optima and infeasible/unbounded problems are also mentioned.
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.
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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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.
The document provides information about operations research (OR) including:
(1) OR uses mathematical models and statistics to aid decision-making, typically to optimize performance of complex systems.
(2) The basic OR process involves recognizing a problem, formulating it, constructing a model, finding a solution, defining the process, implementing it, and refining it.
(3) Linear programming applications discussed include marketing, finance, operations management, and blending problems. Graphical and algebraic methods are used to solve transportation problems.
Operations Management VTU BE Mechanical 2015 Solved paperSomashekar S.M
The document provides information about operations management concepts including scientific management, productivity, ABC analysis, economic order quantity, and materials requirements planning. It defines each concept and provides examples to illustrate how they are applied. Scientific management aims to improve efficiency through systematic analysis of work processes. Productivity is a measure of output per unit of input. ABC analysis categorizes inventory items based on their value and usage to determine appropriate control methods. Economic order quantity and ordering cycle determine optimal replenishment amounts and frequencies to minimize total inventory costs. Materials requirements planning is a technique to plan material needs at different production levels based on a product structure tree.
Dear students get fully solved assignments
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This document contains questions and answers related to the key concepts in linear programming. It discusses topics like shadow prices, the role of topology in linear programming, goal programming for problems with multiple objectives, and the simplex method for solving linear programming problems. Several examples of linear programming problems are also provided relating to production planning, hospital management, and repellent distribution.
Dear students get fully solved SMU MBA Fall 2014 assignments
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Dear students get fully solved SMU MBA assignments
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Dear students get fully solved assignments
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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 MBA assignments. It instructs students to send their semester and specialization name to the email address "help.mbaassignments@gmail.com" or call the provided phone number to receive solved assignments. The document then provides an example of an operations management assignment that covers topics like forecasting, production capacities, transportation costs, and equipment replacement models. It includes six questions with detailed answers on these subject areas.
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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.
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.)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
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 ..
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Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
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How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
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
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.
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.
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ASSIGNMENT
Note:Answerall questions.Kindlynote thatanswersfor10marks questionsshouldbe approximately
of 400 words. Each question is followed by evaluation scheme.
Question1. Explain the process of OR.
Answer: Quantitative basis for decision making is provided to managers by O.R. It enhances a
manager’sabilitytomake longrangeplansandtosolvetheroutine problemsofrunninganenterprise/
concern.O.R.is a systematicandlogical approach to provide a rational footing for taking decisions.
Operation research, like a scientific research is based on scientific methodology which involves
following steps.
Question2. a. Discuss any four applications of linear programming.
Answer 2. a. Four applications of linear programming are as follows:
1. Marketing applications:
Main application of linear programming in marketing is “media selection”.
Linear programming can be used to help marketing managers allocate a fixed
budget to various advertising media.
The main objective is to maximize frequency and quality of exposure.
DRIVE Spring 2017
PROGRAM MBA
SEMESTER II
SUBJECT CODE & NAME MBA205 OPERATIONS RESEARCH
BK ID B2068
CERDIT 4
MARKS 60
2. Restrictions on the allowable
b. An organisationproduces X1 and X2 units of products R and S, respectively.
In this case, the objective function and constraints are expressed as follows:
Maximise Z = 60X1 + 120X2
Subject to, 3X1 + 6X2 ≤ 240 Raw material constraint
2X1 + 4X2 ≤ 800 Labour hours constraint
X1, X2 ≥ 0 Non-negativity condition
Use graphical method to determine how many units of products R and S the
organisation should produce to maximise its profits.
Answer 2(b) - Here,
We have to maximise Z = 60X1 + 120X2
Subject to,
3X1 + 6X2 ≤ 240
2X1 + 4X2 ≤ 800
Question3.a. Explain the concept of Trans-shipment.
Answer3(a). Transhipment is the act of off-loading a container from one ship and loading it onto
anothership. In any service operatedbyanyline there are practical restrictionsintermsof coverage
of ports.. There is no shipping line that can cover all ports around the world on a single service and
therefore the services are segregated into trade lanes.
Let’ssaythere isa linerservice thatconnectsDurbantoFar East withSingapore,HongKong, andPort
Kelang being the ports of call on the voyage
Q3. b. Solve the following transportation problem using North-west corner
method & Matrix minimum method.
3. Answer 3. (b).
The transportation problem given is :
C1 C2 C3 C4 S
Question4.The processingtimeof four jobsand fivemachines(inhours, when
passing is not allowed) is given in following table
a. Find an optimal sequence for the above sequencing problem.
b. Calculate minimum elapsed time & idle time for machines A, B, C, D & E.
Answer4.
4. The Processing time for the new problem is given below:
The optimal sequence is
2 1 4 3
Total elapsed time = 61
Question 5. Define following criteria’s used for decision making under
Uncertainty
a. Optimism (maximax or minimin) criterion
Answer 5(a).
Maximax (Optimist)
The maximax looksatthe bestthatcouldhappenundereachactionandthenchoosesthe actionwith
b. Pessimism (maximin or minimax) criterion
Answer5(b).
Maximin (Pessimist)
The maximin person looks at the worst that could happen under each action and then choose the
c. Equal probabilities (Laplace) criterion
Answer5(c).
Laplace criterion is also called as law of equal probabilities criterion or criterion of rationality, since
probabilityof statesof nature are not knownitisassumedthatall statesof nature will occurin equal
d. Coefficient of optimism (Hurwicz) criterion
Answer5(d).
A compromise between the maximax and maximin criteria. The decision maker is neither totally
optimistic (as the maximax criterion assumes) nor totally pessimistic (as the maximin criterion
5. e. Regret (salvage) criterion
Answer5(e).
The minimizationof regretthatis highestwhenone decisionhasbeenmade insteadof another.Ina
situationinwhichadecisionhas been made that causes the expected payoff of an event to be less
Question 6. Explain the following:
a. Economic Order Quantity(EOQ)
Answer6(a).
Economicorder quantity(EOQ) isan equationforinventorythatdeterminesthe ideal orderquantity
a company should purchase for its inventory given a set cost of production, demand rate and other
variables. This is done to minimize variable inventory costs, and the formula takes into account
storage, or holding, costs, ordering costs and
b. PERT and CPM
Answer6(b).
PERT standsfor ProgramEvaluationReviewTechnique,amethodologydevelopedbythe U.S.Navyin
the 1950s to manage the Polaris
c. Applications of queuing models
Answer6(c).
QueueingTheoryhasa wide range of applications,andthissectionisdesignedtogive an illustration
of some of these. Ithasbeendividedinto3mainsections,TrafficFlow,SchedulingandFacilityDesign
and Employee
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