Simplex Method Flowchart/Algorithm
1 like1,493 views
The document describes the steps of the simplex method algorithm for solving linear programming problems. It begins by converting the linear programming model into standard form by adding slack, surplus, and/or artificial variables. It then sets up an initial simplex table to obtain the initial solution and computes various values. Based on whether the problem is a minimization or maximization type, it selects the key column with the most negative or positive value. The key row is then selected and the simplex table is updated through elementary row operations. The process is repeated until an optimal solution is found.
1 of 6
Ad
Recommended
Data structures trees - B Tree & B+Tree.pptx
Data structures trees - B Tree & B+Tree.pptxMalligaarjunanN B-Trees and B+ Trees are data structures used to store large amounts of data on disks when it cannot all fit in main memory. They allow for efficient multilevel indexing and reduce disk access times compared to other balanced trees like AVL trees by keeping the tree height low. B-Trees have multiple keys in each node and store data pointers in internal and leaf nodes, while B+ Trees only store data pointers in leaf nodes. B+ Trees provide faster searches and easier insertion/deletion compared to B-Trees. Both are commonly used in database systems and file systems to efficiently organize and retrieve large blocks of indexed data from secondary storage.
Applications of stack
Applications of stackeShikshak The document discusses applications of stacks, including reversing strings and lists, Polish notation for mathematical expressions, converting between infix, prefix and postfix notations, evaluating postfix and prefix expressions, recursion, and the Tower of Hanoi problem. Recursion involves defining a function in terms of itself, with a stopping condition. Stacks can be used to remove recursion by saving local variables at each step.
L20 Simplex Method
L20 Simplex MethodQuoc Bao Nguyen The document discusses iterative improvement algorithms and provides examples such as the simplex method for solving linear programming problems. It explains the standard form of a linear programming problem and gives an outline of the simplex method, which generates a sequence of feasible solutions with improving objective values until an optimal solution is found. Some notes on limitations of the simplex method and improvements like the ellipsoid and interior-point methods are also mentioned.
Big m method
Big m methodLuckshay Batra The Big M Method is a variant of the simplex method for solving linear programming problems. It introduces artificial variables and a large number M to convert inequalities into equalities. The transformed problem is then solved using the simplex method, eliminating artificial variables until an optimal solution is found. However, the method has drawbacks in determining a sufficiently large M value and not knowing feasibility until optimality is reached. It is inferior to the two-phase method and not used in commercial solvers.
Insertion sort
Insertion sortMYER301 The document discusses insertion sort, a simple sorting algorithm that builds a sorted output list from an input one element at a time. It is less efficient on large lists than more advanced algorithms. Insertion sort iterates through the input, at each step removing an element and inserting it into the correct position in the sorted output list. The best case for insertion sort is an already sorted array, while the worst is a reverse sorted array.
Traveling salesman problem
Traveling salesman problemJayesh Chauhan The document describes the traveling salesman problem (TSP) and how to solve it using a branch and bound approach. The TSP aims to find the shortest route for a salesman to visit each city once and return to the starting city. It can be represented as a weighted graph. The branch and bound method involves reducing the cost matrix by subtracting minimum row/column values, building a state space tree of paths, and choosing the path with the lowest cost at each step. An example demonstrates these steps to find the optimal solution of 24 for a 5 city TSP problem.
Operators in C/C++
Operators in C/C++Shobi P P Operators are symbols that perform specific tasks like mathematical or logical operations on operands or values. There are several types of operators in C/C++ including arithmetic, relational, logical, bitwise, assignment, conditional, and special operators. Arithmetic operators perform math operations like addition, subtraction, multiplication, and division. Relational operators check relationships between operands like equality, greater than, less than. Logical operators perform logical AND, OR, and NOT operations.
Lattice lecture Final DM Updated2.ppt
Lattice lecture Final DM Updated2.pptAkash588342 This document discusses lattices and partial orders. It defines a partial order as a relation that is reflexive, antisymmetric, and transitive. A lattice is a partially ordered set where every two elements have a unique least upper bound (join) and greatest lower bound (meet). The document provides examples of partial orders and discusses various types of lattices including bounded, complemented, distributive, and Boolean lattices. It also discusses concepts like sublattices, chains, extremal elements, and diagrams used to represent partial orders like Hasse diagrams.
Design and Analysis of Algorithms
Design and Analysis of AlgorithmsArvind Krishnaa This document summarizes a lecture on algorithms and graph traversal techniques. It discusses:
1) Breadth-first search (BFS) and depth-first search (DFS) algorithms for traversing graphs. BFS uses a queue while DFS uses a stack.
2) Applications of BFS and DFS, including finding connected components, minimum spanning trees, and bi-connected components.
3) Identifying articulation points to determine biconnected components in a graph.
4) The 0/1 knapsack problem and approaches for solving it using greedy algorithms, backtracking, and branch and bound search.
Quick sort algorithm using slide presentation , Learn selection sort example ...
Quick sort algorithm using slide presentation , Learn selection sort example ...University of Science and Technology Chitttagong This presentation was made for CSE student to understand easily quick sort algorithm to implement quick algorithm. So if u want to learn quick sort than watch it.
Simplex method concept,
Simplex method concept,Dronak Sahu The document discusses the Simplex method for solving linear programming problems involving profit maximization and cost minimization. It provides an overview of the concept and steps of the Simplex method, and gives an example of formulating and solving a farm linear programming model to maximize profits from two products. The document also discusses some complications that can arise in applying the Simplex method.
Rules of Karnaugh Map
Rules of Karnaugh MapTanjarul Islam Mishu A Karnaugh map is a pictorial method for minimizing Boolean expressions without using Boolean algebra. It groups adjacent ones in a truth table together according to certain rules: groups cannot include zeros, must be horizontal/vertical not diagonal, contain a power of 2 number of cells, be as large as possible, include every one, can overlap, and wrap around. The goal is to find the minimum number of groups to simplify the expression.
Two Phase Method- Linear Programming
Two Phase Method- Linear ProgrammingManas Lad This presentation deals with the two phase method which is used to handle constraints with equality and greater or equal to inequality.
Dynamic programming - fundamentals review
Dynamic programming - fundamentals reviewElifTech Dynamic Programming is an algorithmic paradigm that solves a given complex problem by breaking it into subproblems and stores the results of subproblems to avoid computing the same results again.
Algorithms and how to write an algorithms
Algorithms and how to write an algorithmsAhmed Nobi The document provides information about Ahmed Nobi and Abdelrahman Ashraf, including their contact details, skills, and experience. Ahmed Nobi has over one year of experience as an Android developer and three years of experience as a programming instructor. Abdelrahman Ashraf's skills and brief background are also mentioned. The document then provides an introduction to algorithms, including definitions, how to write an algorithm through obtaining a problem description, analyzing the problem, developing a high-level algorithm, refining with details, and reviewing. Examples of writing algorithms for moving a Jeroo and summing two numbers are included. Decision making with "if" statements and repeating tasks with loops are also covered.
Boolean Algebra DLD
Boolean Algebra DLDRokonuzzaman Rony George Boole first introduced Boolean algebra in 1854 as a way to systematically analyze logic circuits. Boolean algebra uses variables and operations like AND, OR and NOT to represent the behavior of digital logic gates. A key insight was Claude Shannon's 1938 application of Boolean algebra to the analysis and design of logic circuits. Boolean algebra provides a concise way to represent the operation of any logic circuit and determine its output for all combinations of inputs.
K map
K mapPranjali Rawat K map or karnaugh's map is a very important topic when studying boolean algebra.
Here is my powerpoint presentation to explain it in the easiest manner.Also I have added a question for your understanding.For the solution please write me up in the comment box.
Insertion sort
Insertion sortalmaqboli Insertion sort works by iterating through an array, inserting each element into its sorted position by shifting other elements over. It finds the location where each element should be inserted into the sorted portion using a linear search, moving larger elements out of the way to make room. This sorting algorithm is most effective for small data sets and can be implemented recursively or iteratively through comparisons and shifts.
13 Boolean Algebra
13 Boolean AlgebraPraveen M Jigajinni Boolean algebra is an algebra of logic developed by George Boole between 1815-1864 to represent logical statements as an algebra of true and false. It is used to perform logical operations in digital computers by representing true as 1 and false as 0. The fundamental logical operators are AND, OR, and NOT. Boolean algebra expressions can be represented in sum of products (SOP) form or product of sums (POS) form and minimized using algebraic rules or Karnaugh maps. Minterms and maxterms are used to derive Boolean functions from truth tables in canonical SOP or POS form.
Minterm and maxterm
Minterm and maxtermparsa.khan64 Minterms and maxterms are ways to represent Boolean functions. Minterms are AND terms with all variable combinations, while maxterms are OR terms. There are 2^n minterms and maxterms for n variables. Minterms and maxterms are related, with each minterm being the complement of a corresponding maxterm. Boolean functions can be expressed as the sum of minterms or product of maxterms. The minterms/maxterms used correspond to the 1s/0s in the function table.
5.1 greedy
5.1 greedyKrish_ver2 The document discusses various optimization problems that can be solved using the greedy method. It begins by explaining that the greedy method involves making locally optimal choices at each step that combine to produce a globally optimal solution. Several examples are then provided to illustrate problems that can and cannot be solved with the greedy method. These include shortest path problems, minimum spanning trees, activity-on-edge networks, and Huffman coding. Specific greedy algorithms like Kruskal's algorithm, Prim's algorithm, and Dijkstra's algorithm are also covered. The document concludes by noting that the greedy method can only be applied to solve a small number of optimization problems.
Big m method
Big m methodMamatha Upadhya The Big-M method, also known as the penalty method, is an algorithm used to solve linear programming problems with greater than or equal constraints. It works by introducing artificial variables to the constraints with greater than or equal signs and assigning a large penalty value M to the artificial variables in the objective function. The algorithm proceeds by solving the modified problem using the simplex method. The solution obtained will be optimal if no artificial variables remain in the basis, or infeasible if artificial variables remain at a positive level in the basis. The document provides examples demonstrating how to set up and solve LPP problems using the Big-M method.
Operators in c++
Operators in c++ABHIJITPATRA23 Operators are symbols that perform computations on operands and are essential to C++ programming. There are several types of operators including arithmetic, relational, logical, bitwise, and assignment. Arithmetic operators perform math operations, relational operators define relations between operands and return Boolean values, logical operators combine conditions and return Boolean values, bitwise operators perform bit-level operations, and assignment operators assign values to variables.
Java Notes
Java NotesAbhishek Khune This document provides an overview of Unit 1 and Unit 2 of the B.Tech II Yr II Semester course. Unit 1 covers object-oriented programming concepts like classes, inheritance, polymorphism over 5 lectures. The slides cover topics such as the need for OOP, classes and instances, method binding and exceptions. Unit 2 covers Java fundamentals like data types, variables, control statements and classes over 7 lectures across 85 slides, including the history of Java and its evolution from C and C++.
Activity selection problem
Activity selection problemQAU ISLAMABAD,PAKISTAN A greedy algorithm is a problem-solving technique that follows the problem-solving heuristic of making locally optimal choices at each step to find a global optimum. While this may find an optimal solution, it does not guarantee to do so as it does not consider the overall problem. The document discusses applying a greedy algorithm to solve the activity selection problem by always selecting the next activity that finishes earliest without conflicting with previously selected activities. It provides recursive and iterative implementations of the greedy algorithm to solve this problem in O(n log n) time by first sorting activities by finish time.
Knapsack Problem
Knapsack ProblemJenny Galino The document discusses the knapsack problem, which involves selecting a subset of items that fit within a knapsack of limited capacity to maximize the total value. There are two versions - the 0-1 knapsack problem where items can only be selected entirely or not at all, and the fractional knapsack problem where items can be partially selected. Solutions include brute force, greedy algorithms, and dynamic programming. Dynamic programming builds up the optimal solution by considering all sub-problems.
Simplex Method
Simplex Methodkzoe1996 The Simplex Method is an algorithm for solving linear programming problems. It involves setting up the problem in standard form, constructing an initial simplex tableau, and then iteratively selecting pivot columns and performing row operations until an optimal solution is found. The method terminates when all indicators in the tableau are positive or zero, at which point the basic and non-basic variables can be identified to read the optimal solution.
Merge Sort
Merge SortNikhil Sonkamble Merge sort is a divide and conquer algorithm that divides an array into halves, recursively sorts the halves, and then merges the sorted halves back together. The key steps are:
1. Divide the array into equal halves until reaching base cases of arrays with one element.
2. Recursively sort the left and right halves by repeating the divide step.
3. Merge the sorted halves back into a single sorted array by comparing elements pairwise and copying the smaller element into the output array.
Merge sort has several advantages including running in O(n log n) time in all cases, accessing data sequentially with low random access needs, and being suitable for external sorting of large data sets that do not fit in memory
Simplex method 
Simplex method DevyaneeDevyanee2007 This document discusses the simplex method for solving linear programming problems (LPP) with more than two variables. It begins with an introduction to the simplex method, which was developed by George Dantzig. The document then discusses putting an LPP into canonical form and the steps of the simplex method, including setting up an initial simplex table and checking for optimality. It provides examples of converting LPPs into canonical form.
Simplex Algorithm
Simplex AlgorithmMuhammad Kashif The document provides an overview of the simplex algorithm, which is used to solve linear programming problems. It defines key terms like standard form, slack variables, basic and non-basic variables, and pivoting. The simplex algorithm involves writing the problem in standard form, selecting a pivot column and row, and performing row operations to find an optimal solution. An example problem is worked through in multiple iterations to demonstrate how the algorithm progresses from an initial tableau to the optimal solution. Potential issues like cycling are also discussed, along with software tools to implement the simplex method.
Ad
More Related Content
What's hot (20)
Design and Analysis of Algorithms
Design and Analysis of AlgorithmsArvind Krishnaa This document summarizes a lecture on algorithms and graph traversal techniques. It discusses:
1) Breadth-first search (BFS) and depth-first search (DFS) algorithms for traversing graphs. BFS uses a queue while DFS uses a stack.
2) Applications of BFS and DFS, including finding connected components, minimum spanning trees, and bi-connected components.
3) Identifying articulation points to determine biconnected components in a graph.
4) The 0/1 knapsack problem and approaches for solving it using greedy algorithms, backtracking, and branch and bound search.
Quick sort algorithm using slide presentation , Learn selection sort example ...
Quick sort algorithm using slide presentation , Learn selection sort example ...University of Science and Technology Chitttagong This presentation was made for CSE student to understand easily quick sort algorithm to implement quick algorithm. So if u want to learn quick sort than watch it.
Simplex method concept,
Simplex method concept,Dronak Sahu The document discusses the Simplex method for solving linear programming problems involving profit maximization and cost minimization. It provides an overview of the concept and steps of the Simplex method, and gives an example of formulating and solving a farm linear programming model to maximize profits from two products. The document also discusses some complications that can arise in applying the Simplex method.
Rules of Karnaugh Map
Rules of Karnaugh MapTanjarul Islam Mishu A Karnaugh map is a pictorial method for minimizing Boolean expressions without using Boolean algebra. It groups adjacent ones in a truth table together according to certain rules: groups cannot include zeros, must be horizontal/vertical not diagonal, contain a power of 2 number of cells, be as large as possible, include every one, can overlap, and wrap around. The goal is to find the minimum number of groups to simplify the expression.
Two Phase Method- Linear Programming
Two Phase Method- Linear ProgrammingManas Lad This presentation deals with the two phase method which is used to handle constraints with equality and greater or equal to inequality.
Dynamic programming - fundamentals review
Dynamic programming - fundamentals reviewElifTech Dynamic Programming is an algorithmic paradigm that solves a given complex problem by breaking it into subproblems and stores the results of subproblems to avoid computing the same results again.
Algorithms and how to write an algorithms
Algorithms and how to write an algorithmsAhmed Nobi The document provides information about Ahmed Nobi and Abdelrahman Ashraf, including their contact details, skills, and experience. Ahmed Nobi has over one year of experience as an Android developer and three years of experience as a programming instructor. Abdelrahman Ashraf's skills and brief background are also mentioned. The document then provides an introduction to algorithms, including definitions, how to write an algorithm through obtaining a problem description, analyzing the problem, developing a high-level algorithm, refining with details, and reviewing. Examples of writing algorithms for moving a Jeroo and summing two numbers are included. Decision making with "if" statements and repeating tasks with loops are also covered.
Boolean Algebra DLD
Boolean Algebra DLDRokonuzzaman Rony George Boole first introduced Boolean algebra in 1854 as a way to systematically analyze logic circuits. Boolean algebra uses variables and operations like AND, OR and NOT to represent the behavior of digital logic gates. A key insight was Claude Shannon's 1938 application of Boolean algebra to the analysis and design of logic circuits. Boolean algebra provides a concise way to represent the operation of any logic circuit and determine its output for all combinations of inputs.
K map
K mapPranjali Rawat K map or karnaugh's map is a very important topic when studying boolean algebra.
Here is my powerpoint presentation to explain it in the easiest manner.Also I have added a question for your understanding.For the solution please write me up in the comment box.
Insertion sort
Insertion sortalmaqboli Insertion sort works by iterating through an array, inserting each element into its sorted position by shifting other elements over. It finds the location where each element should be inserted into the sorted portion using a linear search, moving larger elements out of the way to make room. This sorting algorithm is most effective for small data sets and can be implemented recursively or iteratively through comparisons and shifts.
13 Boolean Algebra
13 Boolean AlgebraPraveen M Jigajinni Boolean algebra is an algebra of logic developed by George Boole between 1815-1864 to represent logical statements as an algebra of true and false. It is used to perform logical operations in digital computers by representing true as 1 and false as 0. The fundamental logical operators are AND, OR, and NOT. Boolean algebra expressions can be represented in sum of products (SOP) form or product of sums (POS) form and minimized using algebraic rules or Karnaugh maps. Minterms and maxterms are used to derive Boolean functions from truth tables in canonical SOP or POS form.
Minterm and maxterm
Minterm and maxtermparsa.khan64 Minterms and maxterms are ways to represent Boolean functions. Minterms are AND terms with all variable combinations, while maxterms are OR terms. There are 2^n minterms and maxterms for n variables. Minterms and maxterms are related, with each minterm being the complement of a corresponding maxterm. Boolean functions can be expressed as the sum of minterms or product of maxterms. The minterms/maxterms used correspond to the 1s/0s in the function table.
5.1 greedy
5.1 greedyKrish_ver2 The document discusses various optimization problems that can be solved using the greedy method. It begins by explaining that the greedy method involves making locally optimal choices at each step that combine to produce a globally optimal solution. Several examples are then provided to illustrate problems that can and cannot be solved with the greedy method. These include shortest path problems, minimum spanning trees, activity-on-edge networks, and Huffman coding. Specific greedy algorithms like Kruskal's algorithm, Prim's algorithm, and Dijkstra's algorithm are also covered. The document concludes by noting that the greedy method can only be applied to solve a small number of optimization problems.
Big m method
Big m methodMamatha Upadhya The Big-M method, also known as the penalty method, is an algorithm used to solve linear programming problems with greater than or equal constraints. It works by introducing artificial variables to the constraints with greater than or equal signs and assigning a large penalty value M to the artificial variables in the objective function. The algorithm proceeds by solving the modified problem using the simplex method. The solution obtained will be optimal if no artificial variables remain in the basis, or infeasible if artificial variables remain at a positive level in the basis. The document provides examples demonstrating how to set up and solve LPP problems using the Big-M method.
Operators in c++
Operators in c++ABHIJITPATRA23 Operators are symbols that perform computations on operands and are essential to C++ programming. There are several types of operators including arithmetic, relational, logical, bitwise, and assignment. Arithmetic operators perform math operations, relational operators define relations between operands and return Boolean values, logical operators combine conditions and return Boolean values, bitwise operators perform bit-level operations, and assignment operators assign values to variables.
Java Notes
Java NotesAbhishek Khune This document provides an overview of Unit 1 and Unit 2 of the B.Tech II Yr II Semester course. Unit 1 covers object-oriented programming concepts like classes, inheritance, polymorphism over 5 lectures. The slides cover topics such as the need for OOP, classes and instances, method binding and exceptions. Unit 2 covers Java fundamentals like data types, variables, control statements and classes over 7 lectures across 85 slides, including the history of Java and its evolution from C and C++.
Activity selection problem
Activity selection problemQAU ISLAMABAD,PAKISTAN A greedy algorithm is a problem-solving technique that follows the problem-solving heuristic of making locally optimal choices at each step to find a global optimum. While this may find an optimal solution, it does not guarantee to do so as it does not consider the overall problem. The document discusses applying a greedy algorithm to solve the activity selection problem by always selecting the next activity that finishes earliest without conflicting with previously selected activities. It provides recursive and iterative implementations of the greedy algorithm to solve this problem in O(n log n) time by first sorting activities by finish time.
Knapsack Problem
Knapsack ProblemJenny Galino The document discusses the knapsack problem, which involves selecting a subset of items that fit within a knapsack of limited capacity to maximize the total value. There are two versions - the 0-1 knapsack problem where items can only be selected entirely or not at all, and the fractional knapsack problem where items can be partially selected. Solutions include brute force, greedy algorithms, and dynamic programming. Dynamic programming builds up the optimal solution by considering all sub-problems.
Simplex Method
Simplex Methodkzoe1996 The Simplex Method is an algorithm for solving linear programming problems. It involves setting up the problem in standard form, constructing an initial simplex tableau, and then iteratively selecting pivot columns and performing row operations until an optimal solution is found. The method terminates when all indicators in the tableau are positive or zero, at which point the basic and non-basic variables can be identified to read the optimal solution.
Merge Sort
Merge SortNikhil Sonkamble Merge sort is a divide and conquer algorithm that divides an array into halves, recursively sorts the halves, and then merges the sorted halves back together. The key steps are:
1. Divide the array into equal halves until reaching base cases of arrays with one element.
2. Recursively sort the left and right halves by repeating the divide step.
3. Merge the sorted halves back into a single sorted array by comparing elements pairwise and copying the smaller element into the output array.
Merge sort has several advantages including running in O(n log n) time in all cases, accessing data sequentially with low random access needs, and being suitable for external sorting of large data sets that do not fit in memory
Quick sort algorithm using slide presentation , Learn selection sort example ...
Quick sort algorithm using slide presentation , Learn selection sort example ...University of Science and Technology Chitttagong
Similar to Simplex Method Flowchart/Algorithm (20)
Simplex method
Simplex method DevyaneeDevyanee2007 This document discusses the simplex method for solving linear programming problems (LPP) with more than two variables. It begins with an introduction to the simplex method, which was developed by George Dantzig. The document then discusses putting an LPP into canonical form and the steps of the simplex method, including setting up an initial simplex table and checking for optimality. It provides examples of converting LPPs into canonical form.
Simplex Algorithm
Simplex AlgorithmMuhammad Kashif The document provides an overview of the simplex algorithm, which is used to solve linear programming problems. It defines key terms like standard form, slack variables, basic and non-basic variables, and pivoting. The simplex algorithm involves writing the problem in standard form, selecting a pivot column and row, and performing row operations to find an optimal solution. An example problem is worked through in multiple iterations to demonstrate how the algorithm progresses from an initial tableau to the optimal solution. Potential issues like cycling are also discussed, along with software tools to implement the simplex method.
Backtracking & branch and bound
Backtracking & branch and boundVipul Chauhan The document discusses backtracking and branch and bound algorithms. Backtracking incrementally builds candidates and abandons them (backtracks) when they cannot lead to a valid solution. Branch and bound systematically enumerates solutions and discards branches that cannot produce a better solution than the best found so far based on upper bounds. Examples provided are the N-Queens problem solved with backtracking and the knapsack problem solved with branch and bound. Pseudocode is given for both algorithms.
Linear Programing.pptx
Linear Programing.pptxAdnanHaleem The document defines linear programming and its key components. It explains that linear programming is a mathematical optimization technique used to allocate limited resources to achieve the best outcome, such as maximizing profit or minimizing costs. The document outlines the basic steps of the simplex method for solving linear programming problems and provides an example to illustrate determining the maximum value of a linear function given a set of constraints. It also discusses other applications of linear programming in fields like engineering, manufacturing, energy, and transportation for optimization.
Optimum Engineering Design - Day 4 - Clasical methods of optimization
Optimum Engineering Design - Day 4 - Clasical methods of optimizationSantiagoGarridoBulln The document provides information about optimization methods and integer programming problems. It discusses various optimization problem formulations including linear integer programming, binary integer programming, and mixed integer programming. It also describes methods for solving discrete optimization problems like the enumeration method, branch and bound method, and cutting plane method. Examples are provided to illustrate linear programming problems with integral coefficients and how to solve binary integer programming problems using implicit enumeration.
Chapter 3.Simplex Method hand out last.pdf
Chapter 3.Simplex Method hand out last.pdfTsegay Berhe This document provides material on solving linear programming problems using the simplex method. It begins with an introduction to the simplex method and how it can be used to solve linear programming problems analytically. It then presents the steps for solving a problem using the simplex method, including determining a starting basic feasible solution, selecting entering and leaving variables, and performing elementary row operations to arrive at the optimal solution. An example problem is also presented to illustrate how to set up and solve a linear programming problem using the simplex method.
Unit 2.pptx
Unit 2.pptxasthashukla33 For a good business plan creative thinking is important. A business plan is very important and strategic tool for entrepreneurs. A good business plan not only helps entrepreneurs focus on specific steps necessary for them to make business ideas succeed, but it also helps them to achieve short-term and long-term objectives. As an inspiring entrepreneur who is looking towards starting a business, one of the businesses you can successfully start without much stress is book servicing café.
Importance:
Nowadays, network plays an important role in people’s life. In the process of the improvement of the people’s living standard, people’s demand of the life’s quality and efficiency is more higher, the traditional bookstore’s inconvenience gradually emerge, and the online book store has gradually be used in public. The online book store system based on the principle of providing convenience and service to people.
With the online book servicing café, college student do not need to blindly go to various places to find their own books, but only in a computer connected to the internet log on online book servicing café in the search box, type u want to find of the book information retrieval, you can efficiently know whether a site has its own books, if you can online direct purchase, if not u can change the home book store to continue to search or provide advice to the seller in order to supply. This greatly facilitates every college student saving time.
The online book servicing café’s main users are divided into two categories, one is the front user, and one is the background user. The main business model for Book Servicing Café relies on college students providing textbooks, auctions, classifieds teacher evaluations available on website. Therefore, our focus will be on the marketing strategy to increase student traffic and usage. In turn, visitor volume and transactions will maintain the inventory of products and services offered.
Online bookstore system i.e. Book Servicing Café not only can easily find the information and purchase books, and the operating conditions are simple, user-friendly, to a large extent to solve real-life problems in the purchase of the books.
When you shop in online book servicing cafe, you have the chance of accessing and going through customers who have shopped at book servicing café and review about the book you intend to buy. This will give you beforehand information about that book.
While purchasing or selling books at the book servicing café, you save money, energy and time for your favorite book online. The book servicing café will offer discount coupons which help college students save money or make money on their purchases or selling. Shopping for books online is economical too because of the low shipping price.
Book servicing café tend to work with multiple suppliers, which allows them to offer a wider variety of books than a traditional retail store without accruing a large, costly inventory which will help colle
SIMPLEX METHOD.pptx
SIMPLEX METHOD.pptxTista3 The document provides an introduction to the simplex method for solving linear programming problems. It begins by explaining the limitations of graphical methods for problems with more than two decision variables or constraints. The simplex method, developed by George Dantzig, overcomes these limitations through an algebraic approach. The document then outlines the standard form and characteristics of a linear programming problem before explaining how to transform problems into standard form. Finally, it provides a high-level overview of the simplex algorithm, including setting up the initial tableau, pivoting to improve the objective function, and determining the entering and exiting variables at each step.
Linear Programming
Linear ProgrammingRashid Ansari The document discusses linear programming, which is a method for optimizing a linear objective function subject to linear equality and inequality constraints. It describes how to formulate a linear programming problem by defining the objective function and constraints in terms of decision variables. It also discusses graphical and algebraic solution methods, including identifying an optimal solution at an extreme point of the feasible region. Applications of linear programming are mentioned in areas like business, industry, and marketing.
Linear programming
Linear programmingPiyush Sharma Linear programming is a technique for choosing the best alternative from a set of feasible alternatives by expressing the objective function and constraints as linear mathematical functions. It requires a clear objective, quantitative terms, identifiable constraints, and feasible alternative choices. The objective functions and constraints define a feasible region, and the simplex method is an iterative algorithm that generates corner point solutions to find the optimal solution at an extreme point of the feasible region. It works by maintaining a basic feasible solution and performing elementary row operations at each iteration to improve the objective function value until reaching the optimal solution.
Calculator-Techniques for engineering.pptx
Calculator-Techniques for engineering.pptxSoleil50 This document provides instructions for using calculator techniques in a statistics course. It discusses using the shift solve and calc functions to find values and roots of expressions. It also covers using the equation solver, complex, and statistics modes to solve systems of equations, operate on complex numbers, and perform statistical calculations like regression. Examples are provided for each technique explained.
Simplex algorithm
Simplex algorithmKhwaja Bilal Hassan The document provides an overview of the simplex algorithm for solving linear programming problems. It begins with an introduction and defines the standard format for representing linear programs. It then describes the key steps of the simplex algorithm, including setting up the initial simplex tableau, choosing the pivot column and pivot row, and pivoting to move to the next basic feasible solution. It notes that the algorithm terminates when an optimal solution is reached where all entries in the objective row are non-negative. The document also briefly discusses variants like the ellipsoid method and cycling issues addressed by Bland's rule.
Investigación de Operaciones 023 Programación Lineal solución por el método s...
Investigación de Operaciones 023 Programación Lineal solución por el método s...Jorge Pablo Rivas This document discusses the simplex method for solving linear programming problems. It begins by providing background on the simplex method, describing it as an iterative analytical method for solving more complex problems than graphical methods. It then covers considerations and steps required before applying the simplex method, including converting inequalities to equations, defining slack and surplus variables, and establishing an initial basic feasible solution. The document concludes by outlining the basic algorithm and iterative process of the simplex method through tableau formatting.
final-ppts-daa-unit-iii-greedy-method.pdf
final-ppts-daa-unit-iii-greedy-method.pdfJasmineSayyed3 The document discusses greedy algorithms and their applications. Greedy algorithms make locally optimal choices at each step to arrive at a global solution. They are used to solve optimization problems by considering inputs in order based on a selection measure. Applications mentioned include knapsack problem, minimum spanning tree, job sequencing, Huffman coding, and shortest path. Specific details are provided on the knapsack problem and job sequencing problem algorithms.
Simplex Algorithm
Simplex AlgorithmAizaz Ahmad The document discusses the simplex algorithm for solving linear programming problems. It begins with an introduction and overview of the simplex algorithm. It then describes the key steps of the algorithm, which are: 1) converting the problem into slack format, 2) constructing the initial simplex tableau, 3) selecting the pivot column and calculating the theta ratio to determine the departing variable, 4) pivoting to create the next tableau. The document provides examples to illustrate these steps. It also briefly discusses cycling issues, software implementations, efficiency considerations and variants of the simplex algorithm.
daa-unit-3-greedy method
daa-unit-3-greedy methodhodcsencet The document discusses the greedy method algorithmic approach. It provides an overview of greedy algorithms including that they make locally optimal choices at each step to find a global optimal solution. The document also provides examples of problems that can be solved using greedy methods like job sequencing, the knapsack problem, finding minimum spanning trees, and single source shortest paths. It summarizes control flow and applications of greedy algorithms.
6260966
6260966Nidhi Divecha Valu The document discusses the simplex method for solving linear programming problems. It begins by introducing the simplex method and explaining that it is an iterative procedure that examines corner points to find an optimal solution. It then defines key terms like slack, surplus, and artificial variables. The majority of the document outlines the step-by-step procedures for using the simplex method to solve maximization and minimization problems. It also discusses how to handle special cases that may arise, such as degeneracy, unbounded problems, and infeasible problems.
Ad
More from Raja Adapa (20)
Ambassador and its journey in India.docx
Ambassador and its journey in India.docxRaja Adapa The Ambassador car has been an iconic vehicle in India since its production began in 1958. Over time, Hindustan Motors introduced several Mark and model revisions to the Ambassador, such as the Mark I through Mark IV, Nova, 1800 ISZ, Encore, and Avigo, bringing design upgrades and engine improvements. However, the Ambassador struggled to meet modern safety and emissions standards, leading to a decline in its market share as Hindustan Motors faced financial issues. In 2014, Ambassador production was temporarily halted, though there are discussions to potentially revive the historic brand under new ownership.
To create anaconda environment user defined path other than default path.docx
To create anaconda environment user defined path other than default path.docxRaja Adapa 1. To create an Anaconda environment in a custom path, open the Anaconda Navigator and go to Settings to edit the system environment variables.
2. In the environment variables window, click Edit to modify the PATH variable and set the custom path for new Anaconda environments.
3. New environments created in Anaconda Navigator will now be located in the custom path instead of the default path.
To create anaconda environment user defined path other than default path.docx
To create anaconda environment user defined path other than default path.docxRaja Adapa Python - creating a anaconda new environment with user defined path other than system defined default path
SCM PPT.pptx
SCM PPT.pptxRaja Adapa The document provides an overview of the supply chain management process. It defines a supply chain as a network of facilities and distribution options that procure materials, transform materials into products, and distribute finished products to customers. It then outlines the overall process flow, which includes inbound logistics to receive raw materials from suppliers, manufacturing to transform materials into products, and outbound logistics where distributors take finished goods from manufacturers for distribution to retailers. Metrics related to supplier compliance, inventory, production, transportation and more are presented for understanding the supply chain management process.
Methods of Capital Budgeting
Methods of Capital BudgetingRaja Adapa The document discusses various traditional and discounted cash flow methods for capital budgeting, including payback period, accounting rate of return, net present value, profitability index, and internal rate of return. It provides formulas and acceptance criteria for each method. The payback period method considers the period to recoup initial investment while accounting rate of return expresses returns as a percentage of capital. Net present value discounts future cash flows to determine if a project's present value exceeds costs. Profitability index measures the relationship between present values of inflows and outflows. Internal rate of return is the discount rate that yields a net present value of zero.
Outline the Methods of Performance Appraisal
Outline the Methods of Performance AppraisalRaja Adapa This document outlines various traditional and modern methods of performance appraisal. Traditional methods include straight ranking, paired comparison, grading, and graphic rating scales. Modern methods discussed include management by objectives, assessment centers, human asset accounting, behaviorally anchored rating scales, potential appraisal, and 360-degree feedback. 360-degree feedback involves collecting performance ratings from an employee's supervisor, peers, direct reports, and customers to provide a well-rounded perspective for development purposes. However, 360-degree feedback can be intimidating for employees and challenging for organizations to implement due to the complexities of selecting raters and analyzing multiple ratings.
Methods of Wage Fixation 3
Methods of Wage Fixation 3Raja Adapa This document describes two plans for paying workers - the Gantt task and bonus plan and Emerson's efficiency plan. Under the Gantt plan, workers are paid differently based on their output level: minimum wage for below standard output, time rate plus 20% bonus at standard output, and a high piece rate with no bonus for above standard output. Emerson's plan pays time rate up to 67% efficiency, time rate plus 20% bonus at 100% efficiency, and additional 1% bonus for each 1% over 100% efficiency. Examples are provided to demonstrate how earnings are calculated under each worker's output level for both plans.
Methods of Wage Fixation - 2
Methods of Wage Fixation - 2Raja Adapa The document summarizes three different incentive pay systems:
1) The Bedaux system calculates earnings based on the number of "B's" a worker achieves compared to the standard time allowed. A worker's earnings include their hourly rate plus a bonus for B's saved.
2) Taylor's differential piece rate system pays workers above standard at 120% of the piece rate and below standard at 80% of the piece rate.
3) Merrick's multiple piece rate system provides different bonus incentives based on production levels compared to the standard: less than 83% gets the normal rate, 83-100% gets a 10% bonus, and above 100% gets a 20% bonus. Worker earnings are calculated as
Methods of Wage Fixation
Methods of Wage FixationRaja Adapa This document discusses 5 different methods of wage fixation: 1) Time Rate System, where wages are calculated based on hours or days worked multiplied by an hourly or daily rate. 2) Piece Rate System, where wages are calculated by multiplying the number of units produced by a rate per unit. 3) Balance or Debt Method, where workers are paid the higher of their time rate wages or piece rate earnings each week, with deficits or surpluses carried over to future weeks. 4) Halsey System, where workers are paid their time rate for work completed, plus 50% of their hourly rate for any time saved. 5) Rowan Premium Plan, where a bonus is paid based on time saved multiplied by the ratio of actual
Loading Capability Limits of Transmission Lines
Loading Capability Limits of Transmission LinesRaja Adapa This document discusses the four main loading capability limits of transmission lines: thermal, voltage, dielectric, and stability limits. The thermal limit depends on ambient temperature, wind conditions, conductor size and is usually the main limiting factor. Voltage limits require the transmission voltage to be maintained within a specified range, like plus/minus 5% of nominal. The dielectric limit concerns insulation and allows for some increase in normal operating voltage. Stability limits involve ensuring the power system remains stable after the loss of a single element to prevent cascading outages. FACTS technology can help utilize more of the thermal limits and improve stability.
Some of the Basic Important Concepts in MBA
Some of the Basic Important Concepts in MBARaja Adapa This document provides definitions and explanations of various important concepts related to management, economics, marketing, operations, human resources, and other business topics. It covers foundational ideas like the four Ps of marketing, the BCG matrix, total quality management, and benchmarking. Key terms defined include product mix, job analysis, succession planning, and customer loyalty. Important management techniques like Kaizen, the McKinsey 7S model, and transactional analysis are also summarized.
Important Formulas of BEP, Ratio Analysis, Capital Budgeting
Important Formulas of BEP, Ratio Analysis, Capital BudgetingRaja Adapa This document provides formulas and explanations for various accounting and finance topics:
1) Break even analysis formulas to calculate variable costs, contribution, sales to reach profit targets, and break even points.
2) Rules for classifying accounts as personal, real, or nominal.
3) Leverage, liquidity, and profitability ratios for ratio analysis including debt to equity, current and quick ratios, gross and net profit ratios.
4) Capital budgeting methods including payback period, average rate of return, net present value, profitability index, and internal rate of return to evaluate investment projects.
Theoretical Framework for a Marketing Project
Theoretical Framework for a Marketing ProjectRaja Adapa Marketing management involves planning and executing the conception, pricing, promotion, and distribution of goods and services to create exchanges that satisfy organizational goals. It influences demand to help the organization achieve its objectives. The marketing concept holds that companies should focus on determining customers' needs and wants in order to develop the right products and services. Marketing involves the 4 P's: product, price, place, and promotion. Individuals' perceptions are based on their needs, experiences, and expectations, rather than objective reality, so marketers must understand customers' perceptions to influence their purchasing decisions.
Types of Market Structure
Types of Market StructureRaja Adapa This document defines different types of market structures:
- Perfect competition has many small firms, homogeneous products, free entry and exit, and firms that are price-takers.
- Monopoly has a single seller of a unique product without close substitutes that can influence price.
- Monopolistic competition features many small differentiated product sellers with some product differentiation and freedom of entry and exit.
- Oligopoly has a market dominated by a small number of interdependent firms that closely watch each other's actions.
Types of Scales and Scaling Techniques
Types of Scales and Scaling TechniquesRaja Adapa This document discusses different types of scales and scaling techniques used in research. There are four main types of measurement scales: nominal, ordinal, interval, and ratio scales. Nominal scales classify objects without quantitative values, ordinal scales rank objects, interval scales have equal differences between variables, and ratio scales have absolute values with zero as a meaningful point. Common scaling techniques include Guttman scales which determine attitude specificity, Likert scales which measure levels of agreement, and semantic differential scales which allow comparing concepts on a spectrum between two extremes.
Market Location Tactics
Market Location TacticsRaja Adapa There are four types of market location tactics: market leaders, market challengers, market followers, and market nichers. Market leaders have the largest market share and influence industry developments. They retain their position by expanding the market, defending their share, and enhancing effectiveness. Market challengers aim to challenge or follow the leader by setting objectives, choosing attack strategies like frontal or flank attacks, and usually aiming to increase their share. Market followers imitate leaders without disrupting the industry, using strategies like cloning or adapting. Market nichers carve out a small, unique niche by excelling in attributes, locations, or customization.
Porter's Five Force Model
Porter's Five Force ModelRaja Adapa The document discusses Porter's Five Forces model for analyzing competition within an industry. It describes the five competitive forces as: (1) threat of new entrants, (2) bargaining power of buyers, (3) bargaining power of suppliers, (4) intensity of rivalry among existing competitors, and (5) threat of substitute products. For each force, it provides examples to illustrate how the competitive intensity in an industry is determined by the collective strength of these five factors.
Outline of Types of Organizational Structures
Outline of Types of Organizational StructuresRaja Adapa There are several types of organizational structures that companies can adopt. A simple structure has an owner-manager who makes all decisions with informal relationships and communication. A line organization has a direct vertical flow of authority from top to bottom. A line and staff organization combines this direct authority with staff who are experts in specific areas. A functional structure groups employees into departments like marketing, production, and finance. A multi-divisional structure manages diversified products and markets across multiple business units. A matrix structure combines functional and project-based command with dual lines of authority. A network structure uses project groups and collaborations both within and outside the organization to operate in a flexible, non-hierarchical way.
Model paper 6 (UGC NET Paper-1)
Model paper 6 (UGC NET Paper-1)Raja Adapa This document contains a sample test with 37 multiple choice questions covering topics related to education, research methodology, communication, logic, and data interpretation. The questions are designed to assess knowledge of concepts like student-centered learning, research methods, data analysis, logical reasoning, and effective communication. Sample questions assess the ability to identify key aspects of passages, match concepts, calculate percentages and growth rates from tables, and recognize logical fallacies. The test covers a wide range of topics relevant to the UGC NET exam for paper 1.
Model paper 5 (UGC NET Paper-1)
Model paper 5 (UGC NET Paper-1)Raja Adapa The document contains a sample exam paper with multiple choice questions covering various topics related to research methodology and statistics. The questions test concepts like sampling techniques, data analysis methods, variables in research studies, and principles of communication. Some questions include data in the form of tables and passages and test the interpretation and analysis of the given information.
Ad
Recently uploaded (20)
Geography Sem II Unit 1C Correlation of Geography with other school subjects
Geography Sem II Unit 1C Correlation of Geography with other school subjectsProfDrShaikhImran The correlation of school subjects refers to the interconnectedness and mutual reinforcement between different academic disciplines. This concept highlights how knowledge and skills in one subject can support, enhance, or overlap with learning in another. Recognizing these correlations helps in creating a more holistic and meaningful educational experience.
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulse
pulse ppt.pptx Types of pulse , characteristics of pulse , Alteration of pulsesushreesangita003 what is pulse ?
Purpose
physiology and Regulation of pulse
Characteristics of pulse
factors affecting pulse
Sites of pulse
Alteration of pulse
for BSC Nursing 1st semester
for Gnm Nursing 1st year
Students .
vitalsign
Presentation of the MIPLM subject matter expert Erdem Kaya
Presentation of the MIPLM subject matter expert Erdem KayaMIPLM Presentation of the MIPLM subject matter expert Erdem Kaya
Link your Lead Opportunities into Spreadsheet using odoo CRM
Link your Lead Opportunities into Spreadsheet using odoo CRMCeline George In Odoo 17 CRM, linking leads and opportunities to a spreadsheet can be done by exporting data or using Odoo’s built-in spreadsheet integration. To export, navigate to the CRM app, filter and select the relevant records, and then export the data in formats like CSV or XLSX, which can be opened in external spreadsheet tools such as Excel or Google Sheets.
Herbs Used in Cosmetic Formulations .pptx
Herbs Used in Cosmetic Formulations .pptxRAJU THENGE The content is for the educational purpose for Pharmacy and Cosmetic students.
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC) A measles outbreak originating in West Texas has been linked to confirmed cases in New Mexico, with additional cases reported in Oklahoma and Kansas. The current case count is 817 from Texas, New Mexico, Oklahoma, and Kansas. 97 individuals have required hospitalization, and 3 deaths, 2 children in Texas and one adult in New Mexico. These fatalities mark the first measles-related deaths in the United States since 2015 and the first pediatric measles death since 2003.
The YSPH Virtual Medical Operations Center Briefs (VMOC) were created as a service-learning project by faculty and graduate students at the Yale School of Public Health in response to the 2010 Haiti Earthquake. Each year, the VMOC Briefs are produced by students enrolled in Environmental Health Science Course 581 - Public Health Emergencies: Disaster Planning and Response. These briefs compile diverse information sources – including status reports, maps, news articles, and web content– into a single, easily digestible document that can be widely shared and used interactively. Key features of this report include:
- Comprehensive Overview: Provides situation updates, maps, relevant news, and web resources.
- Accessibility: Designed for easy reading, wide distribution, and interactive use.
- Collaboration: The “unlocked" format enables other responders to share, copy, and adapt seamlessly. The students learn by doing, quickly discovering how and where to find critical information and presenting it in an easily understood manner.
CURRENT CASE COUNT: 817 (As of 05/3/2025)
• Texas: 688 (+20)(62% of these cases are in Gaines County).
• New Mexico: 67 (+1 )(92.4% of the cases are from Eddy County)
• Oklahoma: 16 (+1)
• Kansas: 46 (32% of the cases are from Gray County)
HOSPITALIZATIONS: 97 (+2)
• Texas: 89 (+2) - This is 13.02% of all TX cases.
• New Mexico: 7 - This is 10.6% of all NM cases.
• Kansas: 1 - This is 2.7% of all KS cases.
DEATHS: 3
• Texas: 2 – This is 0.31% of all cases
• New Mexico: 1 – This is 1.54% of all cases
US NATIONAL CASE COUNT: 967 (Confirmed and suspected):
INTERNATIONAL SPREAD (As of 4/2/2025)
• Mexico – 865 (+58)
‒Chihuahua, Mexico: 844 (+58) cases, 3 hospitalizations, 1 fatality
• Canada: 1531 (+270) (This reflects Ontario's Outbreak, which began 11/24)
‒Ontario, Canada – 1243 (+223) cases, 84 hospitalizations.
• Europe: 6,814
Introduction to Vibe Coding and Vibe Engineering
Introduction to Vibe Coding and Vibe EngineeringDamian T. Gordon Introduction to Vibe Coding and Vibe Engineering
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport This version of the lectures is provided free of charge to graduate students studying the Operations Management course at the MBA level.
Kenan Fellows Participants, Projects 2025-26 Cohort
Kenan Fellows Participants, Projects 2025-26 CohortEducationNC These are the educators participating in the Kenan Fellows Program for Teacher Leadership at NC State University.
Real GitHub Copilot Exam Dumps for Success
Real GitHub Copilot Exam Dumps for SuccessMark Soia Download updated GitHub Copilot exam dumps to boost your certification success. Get real exam questions and verified answers for guaranteed performance
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...Nguyen Thanh Tu Collection https://app.box.com/s/n8nl163m8v9ou7xil944pod44a5wi43h
"Basics of Heterocyclic Compounds and Their Naming Rules"
"Basics of Heterocyclic Compounds and Their Naming Rules"rupalinirmalbpharm This video is about heterocyclic compounds, which are chemical compounds with rings that include atoms like nitrogen, oxygen, or sulfur along with carbon. It covers:
Introduction – What heterocyclic compounds are.
Prefix for heteroatom – How to name the different non-carbon atoms in the ring.
Suffix for heterocyclic compounds – How to finish the name depending on the ring size and type.
Nomenclature rules – Simple rules for naming these compounds the right way.
Common rings – Examples of popular heterocyclic compounds used in real life.
How to Customize Your Financial Reports & Tax Reports With Odoo 17 Accounting
How to Customize Your Financial Reports & Tax Reports With Odoo 17 AccountingCeline George The Accounting module in Odoo 17 is a complete tool designed to manage all financial aspects of a business. Odoo offers a comprehensive set of tools for generating financial and tax reports, which are crucial for managing a company's finances and ensuring compliance with tax regulations.
How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...
How to track Cost and Revenue using Analytic Accounts in odoo Accounting, App...Celine George Analytic accounts are used to track and manage financial transactions related to specific projects, departments, or business units. They provide detailed insights into costs and revenues at a granular level, independent of the main accounting system. This helps to better understand profitability, performance, and resource allocation, making it easier to make informed financial decisions and strategic planning.
Odoo Inventory Rules and Routes v17 - Odoo Slides
Odoo Inventory Rules and Routes v17 - Odoo SlidesCeline George Odoo's inventory management system is highly flexible and powerful, allowing businesses to efficiently manage their stock operations through the use of Rules and Routes.
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptx
YSPH VMOC Special Report - Measles Outbreak Southwest US 5-3-2025.pptxYale School of Public Health - The Virtual Medical Operations Center (VMOC)
Operations Management (Dr. Abdulfatah Salem).pdf
Operations Management (Dr. Abdulfatah Salem).pdfArab Academy for Science, Technology and Maritime Transport
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...Nguyen Thanh Tu Collection
Simplex Method Flowchart/Algorithm
- 1. Subject: Operations Research Topic: Simplex Method Algorithm/Flowchart By A. Raja
- 2. Simplex Method Flowchart START •Convert LP model into standard form by adding either slack variables, surplus variables and/or artificial variables. •Decide coefficient of these variables in the objective function.
- 3. MINIMIZATION MAXIMIZATION Set up initial simplex table to obtain initial solution Compute Zj and Cj-Zj values Is LP PROBLEM OF MAX/MIN TYPE?
- 4. NO NO YES YES This solution is optimal Do Cj- Zj negativ e values exist? Do Cj- Zj positive values exist? Select key column with large negative Cj-Zj values Select key column with large positive Cj-Zj values
- 5. •Select key row with min XB/Xk >=0 •If all zero, then current (where xk>=0) Solution is unbounded &stop the procedure Identify key element at the intersection of key row and key column Update the entries in the simplex table by •First obtaining key row values and •Apply elementary row operations
- 6. THANK YOU Note: Because of space restriction, I unable to draw the flowchart in a single page. First you draw the complete flowchart in a single page, then you will get clear picture.