2.6 initial feasible solution by vam
•Download as PPTX, PDF•
2 likes•202 views
This document discusses an initial feasible solution method called VAM for transportation and assignment problems. It is presented by Dr. V. M. Tidake and focuses on helping students understand and practice the VAM method. Students are provided an exercise to solve using VAM. The document encourages contacting Dr. Tidake for more details.
Report
Share
Report
Share
Recommended
2.11 case 1 optimal solution by modi method
This document is a presentation on finding the optimal solution using the MODI method for transportation and assignment problems. It is presented by Dr. V. M. Tidake and discusses how the MODI method can be used to find the optimal solution for a case study. Students are provided an exercise to practice applying the MODI method to find the optimal solution for a transportation or assignment problem.
2.8 theory optimal solution by modi method
This document discusses optimal solutions for transportation and assignment problems using the MODI method. It is presented by Dr. V. M. Tidake from Sanjivani College of Engineering, Kopargaon and covers optimal solutions by the MODI method. Students will learn the steps to solve transportation and assignment problems using the MODI method. The document provides contact information for Dr. Tidake and concludes by thanking the audience.
2.9 optimal solution by modi method
This document is a presentation on optimal solutions using the MODI method for transportation and assignment problems. It is presented by Dr. V. M. Tidake from Sanjivani College of Engineering, Kopargaon. The presentation explains the MODI method for finding the optimal solution and provides an exercise for students to practice solving problems using this method. It also briefly discusses finding an initial feasible solution using the least cost method.
2.4 initial feasible solution by nwcm
The document discusses an initial feasible solution method called the Northwest Corner Method (NWCM) for transportation and assignment problems. It presents a case study that students can practice solving using NWCM. The document was created by Dr. V. M. Tidake for a class on decision science and aims to help students understand how to find an initial solution for transportation and assignment problems using the NWCM approach.
2.12 case 2 optimal solution by modi method
The document discusses optimal solutions using the MODI method for transportation and assignment problems. It is presented by Dr. V. M. Tidake and discusses how students will learn to find optimal solutions using the MODI method. Students are provided an exercise to practice finding the optimal solution using the MODI method. Contact information is provided for Dr. Tidake for any additional details.
1.10 ds case 4 graphical solution to lpp
This document is a presentation on solving linear programming problems (LPP) using graphical methods. It discusses graphical solutions to LPP and provides an exercise for students to practice solving a case study using a graphical approach. The presentation was given by Dr. V. M. Tidake from Sanjivani College of Engineering to help students learn how to provide graphical solutions to linear programming problems.
1.7 ds case 1 graphical solution to lpp
This document discusses a case study on providing a graphical solution to a linear programming problem. It introduces linear programming and the session's objective to teach graphical solution methods. Students are instructed to solve a practice exercise case as graphical solution to a linear programming problem. Contact information is provided for further details.
2.14 case 4 case of maximization
This document is a presentation about transportation and assignment problems from Dr. V. M. Tidake of Sanjivani College of Engineering. It discusses finding the optimal solution in a case of maximization using the MODI method. Students will learn to apply the MODI method to find optimal solutions in maximization problems through a practice exercise. Contact information is provided for anyone seeking more details.
Recommended
2.11 case 1 optimal solution by modi method
This document is a presentation on finding the optimal solution using the MODI method for transportation and assignment problems. It is presented by Dr. V. M. Tidake and discusses how the MODI method can be used to find the optimal solution for a case study. Students are provided an exercise to practice applying the MODI method to find the optimal solution for a transportation or assignment problem.
2.8 theory optimal solution by modi method
This document discusses optimal solutions for transportation and assignment problems using the MODI method. It is presented by Dr. V. M. Tidake from Sanjivani College of Engineering, Kopargaon and covers optimal solutions by the MODI method. Students will learn the steps to solve transportation and assignment problems using the MODI method. The document provides contact information for Dr. Tidake and concludes by thanking the audience.
2.9 optimal solution by modi method
This document is a presentation on optimal solutions using the MODI method for transportation and assignment problems. It is presented by Dr. V. M. Tidake from Sanjivani College of Engineering, Kopargaon. The presentation explains the MODI method for finding the optimal solution and provides an exercise for students to practice solving problems using this method. It also briefly discusses finding an initial feasible solution using the least cost method.
2.4 initial feasible solution by nwcm
The document discusses an initial feasible solution method called the Northwest Corner Method (NWCM) for transportation and assignment problems. It presents a case study that students can practice solving using NWCM. The document was created by Dr. V. M. Tidake for a class on decision science and aims to help students understand how to find an initial solution for transportation and assignment problems using the NWCM approach.
2.12 case 2 optimal solution by modi method
The document discusses optimal solutions using the MODI method for transportation and assignment problems. It is presented by Dr. V. M. Tidake and discusses how students will learn to find optimal solutions using the MODI method. Students are provided an exercise to practice finding the optimal solution using the MODI method. Contact information is provided for Dr. Tidake for any additional details.
1.10 ds case 4 graphical solution to lpp
This document is a presentation on solving linear programming problems (LPP) using graphical methods. It discusses graphical solutions to LPP and provides an exercise for students to practice solving a case study using a graphical approach. The presentation was given by Dr. V. M. Tidake from Sanjivani College of Engineering to help students learn how to provide graphical solutions to linear programming problems.
1.7 ds case 1 graphical solution to lpp
This document discusses a case study on providing a graphical solution to a linear programming problem. It introduces linear programming and the session's objective to teach graphical solution methods. Students are instructed to solve a practice exercise case as graphical solution to a linear programming problem. Contact information is provided for further details.
2.14 case 4 case of maximization
This document is a presentation about transportation and assignment problems from Dr. V. M. Tidake of Sanjivani College of Engineering. It discusses finding the optimal solution in a case of maximization using the MODI method. Students will learn to apply the MODI method to find optimal solutions in maximization problems through a practice exercise. Contact information is provided for anyone seeking more details.
2.18 case 1 hungarian method in assignment
This document discusses the Hungarian method for solving assignment problems. It presents a case study where 4 jobs (A, B, C, D) need to be assigned to 4 machines (Q, S, P, R) with different processing costs for each job-machine combination. The Hungarian method is applied to find the optimal assignment that minimizes the total processing cost, which is assigning: A to Q for $25, B to S for $21, C to P for $19, and D to R for $34, for a total cost of $99. The document encourages practicing this method on explanatory cases.
2.13 case 3 case of degeneracy
The document discusses optimal solutions in cases of degeneracy for transportation and assignment problems. It presents a case study on degeneracy and describes the MODI method for finding the optimal solution when multiple optimal solutions exist. The document is a presentation on transportation and assignment models by Dr. V. M. Tidake for students of Sanjivani College of Engineering to learn how to identify and solve degenerate cases.
2.16 hungarian method in assignment
This document discusses the Hungarian method for solving assignment problems. It explains the steps of the Hungarian method which include subtracting the smallest elements from rows and columns to reduce the matrix, drawing lines to cover zeros, marking squares around uncovered zeros while cancelling others in the same row or column, and subtracting from uncovered elements and adding to intersections if the solution is not optimal. The goal is to obtain an optimal solution where the number of assignments equals the number of rows or columns. Real-life business situations where assignment problems could be applied are also discussed.
2.7 case 1 initial feasible solution by nwcm, clm, vam
This document is a presentation about obtaining initial feasible solutions for transportation and assignment problems using the Northwest Corner Method (NWCM), Least Cost Method (LCM), and Vogel's Approximation Method (VAM). It discusses each of these three methods and provides examples of how to apply them. At the end, students are given an exercise to practice solving a case using NWCM, LCM, and VAM. The presentation was given by Dr. V. M. Tidake from Sanjivani College of Engineering to help students understand how to find initial feasible solutions for transportation and assignment problems.
1.9 ds case 3 graphical solution to lpp
This document discusses a case study on the graphical solution to a linear programming problem. It presents the topic of linear programming and graphical solutions. Students will learn how to provide a graphical solution to a linear programming problem as an exercise. It provides contact information for Dr. V. M. Tidake for any additional details.
2.3 procedure to solve transportation problem
This document outlines the general procedure for solving a transportation problem which includes 8 steps: 1) Define the objective function to be minimized, 2) Set up the transportation table, 3) Add dummy rows or columns if needed, 4) Develop an initial feasible solution, 5) Examine the feasibility of the initial solution, 6) Test the solution for optimality using opportunity costs, 7) If not optimal, improve the solution using stepping stone or MODI methods, 8) Repeat steps 4-6 until an optimal solution is found. The document was presented by Dr. V. M. Tidake and discusses transportation and assignment problems.
2.21 special case of multiple solution in assignment
This document discusses special cases of multiple solutions in assignment problems. It presents an example of a Hungarian method for assignment that has 4 jobs and 4 machines, with costs for assigning each job to each machine. The method shows subtracting rows and columns to find optimal assignments with the minimum total cost. There are 3 optimal solutions found with the same minimum total cost of 18. The document instructs students to practice solving special cases of multiple solutions in assignment as an exercise.
2.15 introduction to assignment
The document discusses the concept of assignment problems in decision science. It explains that assignment aims to minimize costs by optimally assigning tasks. Assignment can be applied in areas like production, supply chain, logistics management, and services. The document provides an introduction to assignment and lists potential real-life business situations where assignment knowledge could be useful, such as optimally allocating tasks. It encourages contacting the author for more details.
2.20 special case of prohibited route in assignment
This document discusses a special case of prohibited routes in assignment problems. It presents a sample assignment problem with 4 clerks and 4 tasks, where Clerk 2 cannot be assigned to Task A and Clerk 3 cannot be assigned to Task B. It solves this problem using the Hungarian method, showing the steps of row and column subtraction, covering elements, and finding the optimal assignment with the minimum total hours of 18. The document encourages exercising this special case and provides contact information for the presenter.
2.10 special case prohibited route solution by modi method
This document discusses using the MODI method to find optimal solutions for transportation problems with prohibited routes. It explains that prohibited routes are handled by assigning very high costs or very low profits to prohibited cells. The document provides an example of using the MODI method to find the optimal solution for a transportation problem with prohibited routes. It concludes by giving an exercise for students to practice this method.
2.5 initial feasible solution by lcm
The document discusses an initial feasible solution method for transportation and assignment problems called the least cost method (LCM). It presents a transportation case study that students should solve as a practice exercise using the LCM. The document was created by Dr. V. M. Tidake for a class on decision science concepts taught at Sanjivani College of Engineering.
2.2 terminology of transportation problem
This document discusses terminology related to transportation problems. It defines key terms like balanced and unbalanced transportation problems, transportation tables, dummy sources and destinations, initial feasible solutions obtained through various methods like the northwest corner method, and optimal solutions found by stepping stone or modified distribution methods. The document is presented by Dr. V. M. Tidake and provides an overview of common concepts in transportation modeling.
1.5 ds case 3 lpp model formulation
The document presents a linear programming problem (LPP) case study about minimizing the daily costs of purchasing sand and gravel for a concrete company. The company produces 2000 tons of concrete per day using sand and gravel. Sand costs $6 per ton and gravel costs $8 per ton. The problem is formulated as an LPP model with the objective of minimizing costs and constraints for the total tons of sand and gravel purchased and limits on the percentage of each material used. Students are instructed to solve this case as a practice problem.
2.1 introduction to transportation
The document is a presentation about transportation and assignment models. It introduces transportation models as a type of linear programming problem used to establish the lowest cost routes for transporting goods from sources to destinations. Examples given include transporting goods from factories to warehouses. The presentation notes that transportation models have industry applications and can be used to solve real-life business situations involving logistics and routing optimization. Students are asked to list five such potential applications.
1.8 ds case 2 graphical solution to lpp
This document is a presentation on solving linear programming problems (LPP) using graphical methods. It discusses graphical solutions to LPP and provides an exercise for students to practice solving a case study using a graphical approach. The presentation was given by Dr. V. M. Tidake on LPP and how to represent the problem graphically to find the optimal solution within the feasible region.
2.19 special case of unbalanced problem in assignment
This document discusses solving a special case of an unbalanced assignment problem using the Hungarian method. An example is provided where a company wants to assign 3 employees (Mr. X, Y, and Z) to regional offices in Delhi, Mumbai, Kolkata, or a new opening in Chennai. The costs for assigning each employee to each office are given. The Hungarian method is applied, including adding a dummy row and column and performing row/column subtractions, to arrive at the optimal assignment which minimizes total cost: Mr. X to Mumbai for Rs. 2000, Mr. Y to Chennai for Rs. 1600, and Mr. Z to Delhi for Rs. 1000, for a total cost of Rs. 4
1.6 ds graphical solution linear programing problem
This document discusses the graphical solution method for linear programming problems (LPP). It defines a basic feasible solution as one that satisfies non-negativity conditions and constraints. An optimal feasible solution optimizes the objective function from among basic feasible solutions. The steps for the graphical method are given as: 1) Formulate the LPP, 2) Plot constraint lines on a graph ensuring constants are positive and inequalities are equalities, 3) Identify the feasible region, and 4) Find the optimal solution.
1.11 ds case 5 graphical solution to lpp
This document discusses a case study on the graphical solution to a linear programming problem. It presents the topic of linear programming and how to provide a graphical solution. Students will learn how to graphically solve linear programming problems as an exercise. It provides contact information for Dr. V. M. Tidake to get more details.
1.6 key strategies of financial management
This document discusses the key strategies of financial management. It is presented by Dr. V. M. Tidake and is part of a unit on financial management for students. The key strategies of financial management that will be explained include capital budgeting, working capital management, and cash flow management. Contact information is provided for Dr. Tidake to get more details.
1.1 ds introduction to linear programing problem
This document provides an introduction to linear programming problems (LPP). It defines key terms like linear, programming, objective function, constraints, and decision variables. It explains that an LPP aims to optimize a linear objective function subject to linear constraints. The document outlines the steps to formulate an LPP model, including defining the objective, decision variables, objective function, constraints, and non-negativity condition. It provides an example of formulating an LPP model and lists areas where LPP can be used for business decision making.
3.4 decision making under risk
This document is a presentation about decision making under risk given by Dr. V. M. Tidake. It discusses expected monetary value criterion, expected monetary value with perfect information, and value of perfect information as approaches to decision making under risk. The presentation aims to help students understand decision theories under risk and includes an exercise for students to state and explain various decision theories under risk. It concludes by thanking the audience and providing contact information for Dr. Tidake.
3.1.7 case 2 markov chain
This document discusses a Markov chain case study on brand market shares. It presents a brand switching matrix showing the percentage of customers of brands A, B, and C that switch to each brand each quarter. The initial market shares of brands A, B, and C are 50%, 30%, and 20% respectively. The document calculates the market shares after one quarter using the Markov chain formula of multiplying the initial shares by the brand switching matrix. The resulting market shares after one quarter are 35% for brand A, 40% for brand B, and 25% for brand C. Students are instructed to solve this case study as a practice exercise.
More Related Content
What's hot
2.18 case 1 hungarian method in assignment
This document discusses the Hungarian method for solving assignment problems. It presents a case study where 4 jobs (A, B, C, D) need to be assigned to 4 machines (Q, S, P, R) with different processing costs for each job-machine combination. The Hungarian method is applied to find the optimal assignment that minimizes the total processing cost, which is assigning: A to Q for $25, B to S for $21, C to P for $19, and D to R for $34, for a total cost of $99. The document encourages practicing this method on explanatory cases.
2.13 case 3 case of degeneracy
The document discusses optimal solutions in cases of degeneracy for transportation and assignment problems. It presents a case study on degeneracy and describes the MODI method for finding the optimal solution when multiple optimal solutions exist. The document is a presentation on transportation and assignment models by Dr. V. M. Tidake for students of Sanjivani College of Engineering to learn how to identify and solve degenerate cases.
2.16 hungarian method in assignment
This document discusses the Hungarian method for solving assignment problems. It explains the steps of the Hungarian method which include subtracting the smallest elements from rows and columns to reduce the matrix, drawing lines to cover zeros, marking squares around uncovered zeros while cancelling others in the same row or column, and subtracting from uncovered elements and adding to intersections if the solution is not optimal. The goal is to obtain an optimal solution where the number of assignments equals the number of rows or columns. Real-life business situations where assignment problems could be applied are also discussed.
2.7 case 1 initial feasible solution by nwcm, clm, vam
This document is a presentation about obtaining initial feasible solutions for transportation and assignment problems using the Northwest Corner Method (NWCM), Least Cost Method (LCM), and Vogel's Approximation Method (VAM). It discusses each of these three methods and provides examples of how to apply them. At the end, students are given an exercise to practice solving a case using NWCM, LCM, and VAM. The presentation was given by Dr. V. M. Tidake from Sanjivani College of Engineering to help students understand how to find initial feasible solutions for transportation and assignment problems.
1.9 ds case 3 graphical solution to lpp
This document discusses a case study on the graphical solution to a linear programming problem. It presents the topic of linear programming and graphical solutions. Students will learn how to provide a graphical solution to a linear programming problem as an exercise. It provides contact information for Dr. V. M. Tidake for any additional details.
2.3 procedure to solve transportation problem
This document outlines the general procedure for solving a transportation problem which includes 8 steps: 1) Define the objective function to be minimized, 2) Set up the transportation table, 3) Add dummy rows or columns if needed, 4) Develop an initial feasible solution, 5) Examine the feasibility of the initial solution, 6) Test the solution for optimality using opportunity costs, 7) If not optimal, improve the solution using stepping stone or MODI methods, 8) Repeat steps 4-6 until an optimal solution is found. The document was presented by Dr. V. M. Tidake and discusses transportation and assignment problems.
2.21 special case of multiple solution in assignment
This document discusses special cases of multiple solutions in assignment problems. It presents an example of a Hungarian method for assignment that has 4 jobs and 4 machines, with costs for assigning each job to each machine. The method shows subtracting rows and columns to find optimal assignments with the minimum total cost. There are 3 optimal solutions found with the same minimum total cost of 18. The document instructs students to practice solving special cases of multiple solutions in assignment as an exercise.
2.15 introduction to assignment
The document discusses the concept of assignment problems in decision science. It explains that assignment aims to minimize costs by optimally assigning tasks. Assignment can be applied in areas like production, supply chain, logistics management, and services. The document provides an introduction to assignment and lists potential real-life business situations where assignment knowledge could be useful, such as optimally allocating tasks. It encourages contacting the author for more details.
2.20 special case of prohibited route in assignment
This document discusses a special case of prohibited routes in assignment problems. It presents a sample assignment problem with 4 clerks and 4 tasks, where Clerk 2 cannot be assigned to Task A and Clerk 3 cannot be assigned to Task B. It solves this problem using the Hungarian method, showing the steps of row and column subtraction, covering elements, and finding the optimal assignment with the minimum total hours of 18. The document encourages exercising this special case and provides contact information for the presenter.
2.10 special case prohibited route solution by modi method
This document discusses using the MODI method to find optimal solutions for transportation problems with prohibited routes. It explains that prohibited routes are handled by assigning very high costs or very low profits to prohibited cells. The document provides an example of using the MODI method to find the optimal solution for a transportation problem with prohibited routes. It concludes by giving an exercise for students to practice this method.
2.5 initial feasible solution by lcm
The document discusses an initial feasible solution method for transportation and assignment problems called the least cost method (LCM). It presents a transportation case study that students should solve as a practice exercise using the LCM. The document was created by Dr. V. M. Tidake for a class on decision science concepts taught at Sanjivani College of Engineering.
2.2 terminology of transportation problem
This document discusses terminology related to transportation problems. It defines key terms like balanced and unbalanced transportation problems, transportation tables, dummy sources and destinations, initial feasible solutions obtained through various methods like the northwest corner method, and optimal solutions found by stepping stone or modified distribution methods. The document is presented by Dr. V. M. Tidake and provides an overview of common concepts in transportation modeling.
1.5 ds case 3 lpp model formulation
The document presents a linear programming problem (LPP) case study about minimizing the daily costs of purchasing sand and gravel for a concrete company. The company produces 2000 tons of concrete per day using sand and gravel. Sand costs $6 per ton and gravel costs $8 per ton. The problem is formulated as an LPP model with the objective of minimizing costs and constraints for the total tons of sand and gravel purchased and limits on the percentage of each material used. Students are instructed to solve this case as a practice problem.
2.1 introduction to transportation
The document is a presentation about transportation and assignment models. It introduces transportation models as a type of linear programming problem used to establish the lowest cost routes for transporting goods from sources to destinations. Examples given include transporting goods from factories to warehouses. The presentation notes that transportation models have industry applications and can be used to solve real-life business situations involving logistics and routing optimization. Students are asked to list five such potential applications.
1.8 ds case 2 graphical solution to lpp
This document is a presentation on solving linear programming problems (LPP) using graphical methods. It discusses graphical solutions to LPP and provides an exercise for students to practice solving a case study using a graphical approach. The presentation was given by Dr. V. M. Tidake on LPP and how to represent the problem graphically to find the optimal solution within the feasible region.
2.19 special case of unbalanced problem in assignment
This document discusses solving a special case of an unbalanced assignment problem using the Hungarian method. An example is provided where a company wants to assign 3 employees (Mr. X, Y, and Z) to regional offices in Delhi, Mumbai, Kolkata, or a new opening in Chennai. The costs for assigning each employee to each office are given. The Hungarian method is applied, including adding a dummy row and column and performing row/column subtractions, to arrive at the optimal assignment which minimizes total cost: Mr. X to Mumbai for Rs. 2000, Mr. Y to Chennai for Rs. 1600, and Mr. Z to Delhi for Rs. 1000, for a total cost of Rs. 4
1.6 ds graphical solution linear programing problem
This document discusses the graphical solution method for linear programming problems (LPP). It defines a basic feasible solution as one that satisfies non-negativity conditions and constraints. An optimal feasible solution optimizes the objective function from among basic feasible solutions. The steps for the graphical method are given as: 1) Formulate the LPP, 2) Plot constraint lines on a graph ensuring constants are positive and inequalities are equalities, 3) Identify the feasible region, and 4) Find the optimal solution.
1.11 ds case 5 graphical solution to lpp
This document discusses a case study on the graphical solution to a linear programming problem. It presents the topic of linear programming and how to provide a graphical solution. Students will learn how to graphically solve linear programming problems as an exercise. It provides contact information for Dr. V. M. Tidake to get more details.
1.6 key strategies of financial management
This document discusses the key strategies of financial management. It is presented by Dr. V. M. Tidake and is part of a unit on financial management for students. The key strategies of financial management that will be explained include capital budgeting, working capital management, and cash flow management. Contact information is provided for Dr. Tidake to get more details.
1.1 ds introduction to linear programing problem
This document provides an introduction to linear programming problems (LPP). It defines key terms like linear, programming, objective function, constraints, and decision variables. It explains that an LPP aims to optimize a linear objective function subject to linear constraints. The document outlines the steps to formulate an LPP model, including defining the objective, decision variables, objective function, constraints, and non-negativity condition. It provides an example of formulating an LPP model and lists areas where LPP can be used for business decision making.
What's hot (20)
2.7 case 1 initial feasible solution by nwcm, clm, vam
2.7 case 1 initial feasible solution by nwcm, clm, vam
2.21 special case of multiple solution in assignment
2.21 special case of multiple solution in assignment
2.20 special case of prohibited route in assignment
2.20 special case of prohibited route in assignment
2.10 special case prohibited route solution by modi method
2.10 special case prohibited route solution by modi method
2.19 special case of unbalanced problem in assignment
2.19 special case of unbalanced problem in assignment
1.6 ds graphical solution linear programing problem
1.6 ds graphical solution linear programing problem
Similar to 2.6 initial feasible solution by vam
3.4 decision making under risk
This document is a presentation about decision making under risk given by Dr. V. M. Tidake. It discusses expected monetary value criterion, expected monetary value with perfect information, and value of perfect information as approaches to decision making under risk. The presentation aims to help students understand decision theories under risk and includes an exercise for students to state and explain various decision theories under risk. It concludes by thanking the audience and providing contact information for Dr. Tidake.
3.1.7 case 2 markov chain
This document discusses a Markov chain case study on brand market shares. It presents a brand switching matrix showing the percentage of customers of brands A, B, and C that switch to each brand each quarter. The initial market shares of brands A, B, and C are 50%, 30%, and 20% respectively. The document calculates the market shares after one quarter using the Markov chain formula of multiplying the initial shares by the brand switching matrix. The resulting market shares after one quarter are 35% for brand A, 40% for brand B, and 25% for brand C. Students are instructed to solve this case study as a practice exercise.
5.3 theorems on probability
The document discusses theorems on probability presented by Dr. V. M. Tidake. It defines probability terms like union, intersection, and complement of events. It presents the addition theorem which states the probability of events A or B is equal to the probability of A plus the probability of B minus the probability of A and B together. It also presents the compound probability theorem which gives the probability of dependent events A and B as the probability of A times the conditional probability of B given A. The document concludes by asking students to list theorems on probability and their formulas.
3.7 case 2 decision theory
This document presents Case 2 on decision theory to find the optimal marketing strategy for an automobile company. It provides the conditional payoffs in crores of rupees for two car models, Model X and Model Y, based on likely sales figures of 1, 2, or 3 lac units. It analyzes the models using four decision making criteria: 1) Maximin gain criteria selects Model X; 2) Maximax gain criteria selects Model Y; 3) Laplace criteria selects Model Y; 4) Minimax regret criteria selects Model X. Students are asked to explain the case study on decision theory and the different criteria analyzed.
4.2 steps in network analysis
The document outlines the steps in network analysis for project planning and management. It discusses identifying activities and their interrelationships, determining resource requirements, scheduling activities, identifying float and slack, performing probability calculations, determining the critical path, analyzing resource needs, and allocating and readjusting project flow. The goal is for students to understand the key steps involved in network analysis using techniques like PERT and CPM.
5.2 terminologies in probability
The document discusses probability and its terminologies. It was presented by Dr. V. M. Tidake and discusses understanding various terms used in probability such as experiment, outcome, event, sample space, and probability. The session aims to explain these fundamental probability concepts and terminologies. It also provides contact information for Dr. Tidake for any additional details.
3.5 decion making under uncertainity
This document discusses decision making under uncertainty and presents various decision theories that can be used, including maximin, maximax, Hurwicz alpha criterion, Laplace criterion, and minimax regret criterion. It was presented by Dr. V. M. Tidake and aims to help students understand decision making under uncertainty by explaining these decision theories. The document provides an exercise for students to state and explain the various decision theories under uncertainty.
5.2.5 case 2 binomial probability distribution
This document discusses a binomial probability distribution case study presented by Dr. V. M. Tidake. It provides the probabilities of a variate taking specific values based on given mean and variance parameters of a binomial distribution. The number of trials is calculated to be 9, and the probabilities of the variate taking the value of exactly 2 or at most 2 are calculated to be 0.235 and 0.378 respectively using the binomial probability formula. At the end, students are asked to explain the case study on binomial probability distribution.
3.1 introduction decision theory
This document discusses an introduction to decision theory presented by Dr. V. M. Tidake. It defines decision theory as a mathematical approach for selecting the best alternatives from various options in business decision making. It outlines the steps in decision making as identifying the problem, defining objectives, collecting data, developing alternatives, evaluating alternatives, and selecting the best option. Students are asked to list where decision theory can be applied to solve real-life business problems.
3.1.8 comenting markov chain
This document discusses commenting on Markov chains and contains an example transition matrix for three brands: A, B, and C. It provides instructions for students to comment on the Markov chain based on the example transition matrix as a practice exercise. The document was presented by Dr. V. M. Tidake from Sanjivani College of Engineering, Kopargaon and discusses understanding how to comment on Markov chains.
4.1 introduction to network analysis
This document presents an introduction to network analysis by Dr. V. M. Tidake. It discusses network analysis techniques like PERT and CPM. PERT was developed for the US Navy's Polaris missile project, while CPM was used for planning chemical plant construction. The document states that after this session, students will understand the introduction to network analysis. It also provides an exercise for students to list real business situations where network analysis can be applied and includes contact information for Dr. Tidake.
5.1 introduction to probability
This document is a presentation on probability by Dr. V. M. Tidake for students of the MBA program at Sanjivani College of Engineering, Kopargaon. It introduces probability as the study of the possibility of outcomes occurring in experiments and defines probability p as the number of likely outcomes divided by the total number of outcomes. Examples of real business situations where probability can be applied are discussed.
4.3 difference between pert and cpm
This document discusses the differences between PERT and CPM network analysis techniques. It notes that PERT is probabilistic while CPM is deterministic, PERT handles non-repetitive activities while CPM repetitive activities, PERT is task/event oriented while CPM is activity oriented, and PERT focuses on time calculations while CPM considers time and cost tradeoffs. Examples provided of uses include R&D projects using PERT and construction projects using CPM. The document aims to help students understand the differences between PERT and CPM network analysis methods.
3.3 various decision models in decision theory
The document discusses various decision models in decision theory that are taught in a course on decision science. It lists the key decision models as decision making under certainty, risk, uncertainty, and conflict. The document was presented by Dr. V. M. Tidake and provides an overview of a lesson on decision theory models for students of the MBA program at Sanjivani College of Engineering, Kopargaon.
4.4 components of network analysis
The document discusses the components of network analysis, including events/nodes and activities. Events/nodes represent milestones in a project that occur at a specific point in time, such as the start or end of an activity, and are represented by circles. Activities are the basic tasks performed in a project that consume time and/or resources, such as digging or plastering, and are represented by arrows. The document provides examples of event and activity types and explains that activities have predecessor and successor relationships.
4.12 pert introduction
This document discusses Program Evaluation and Review Technique (PERT) as part of a lecture on network analysis. PERT is a technique that considers three time estimates (optimistic, most likely, pessimistic) for activities to account for uncertainty. The expected mean time for an activity is calculated as the average of the three estimates. For a project with critical activities, the project duration will follow a normal probability distribution with the mean being the sum of the individual activity means and variance being the sum of the individual variances. This allows calculating the probability of completing the project within a scheduled time.
4.6 fulkersons rule explanatory case
The document discusses Fulkerson's rule for network analysis. It presents an example network diagram with nodes labeled A through H. Fulkerson's rule is applied to number the nodes in the diagram, with the resulting numbers shown. The purpose is to teach students how to use Fulkerson's rule to assign event numbers in a network diagram.
5.2.9 case 1 probability distribution
This document discusses a case study on probability distributions. It presents the dividend amounts and probabilities of a company's share. It shows how to calculate the expected gain by taking the sum of each dividend amount multiplied by its probability. For a share with a face value of Rs. 100, the expected gain is calculated as Rs. 17. Since this 17% expected return is higher than the minimum expected return of 15%, the document concludes that the shares should be purchased.
3.1.4 Steady State Probability
This document discusses steady state probabilities in Markov chains. It explains that in a Markov chain, as the transitions between states continue indefinitely, the system will reach a steady state where the probabilities of being in each state remain constant. It provides the equations for determining the steady state probabilities - the steady state probability vector is equal to itself multiplied by the transition probability matrix, and the probabilities must sum to 1. An example is shown of setting up these equations to solve for the steady state probabilities. Finally, an exercise is provided to test understanding of steady state probabilities in Markov chains.
4.7 explanatory case on network analysis
This document discusses a case study on network analysis. It presents the activities involved in erecting a storage steel shed for an industrial unit, including erecting a site workshop, fencing the site, bending reinforcement, digging foundations, fabricating steel work, installing a concrete making plant, placing reinforcement, concreting the foundation, painting steel work, erecting the steel structure, and giving finishing touches. It rewrites the predecessor relationships between activities and draws the network diagram showing the sequence of the 11 activities and their dependencies.
Similar to 2.6 initial feasible solution by vam (20)
More from Vishal Tidake
Working Capital Management.pptx
WORKING CAPITAL MANAGEMENT IS A KEY TO SUCCESS FOR ORGANIZATION AS IT DEPICTS OPERATIONAL EFFICIENCY OF A BUSINESS VENTURE.
Cash Flow Statement.pptx
The cash flow statement discloses the movement of cash within a business over an accounting period. It shows how changes in balance sheet accounts affect cash and breaks the analysis down into three sections - operating, investing and financing activities. The cash flow statement provides important information about a company's liquidity, solvency and financial flexibility.
2.9 Fund Flow Statement.pptx
FUND FLOW STATEMENT IS A TECHNIQUE THAT DESCRIBES FLOW OF FUNDS IN AND OUT OF THE BUSINESS WITHIN PARTICULAR ACCOUNTING YEAR.
Ratio Analysis.pptx
The document discusses ratio analysis, which is used by businesses to analyze their financial position and performance. It defines ratios as divisions of two numbers and explains that ratio analysis allows businesses to discuss their profitability, liquidity, solvency, and turnover with stakeholders. The document then categorizes ratios into five types - liquidity ratios, turnover ratios, solvency ratios, profitability ratios, and miscellaneous ratios. It provides examples of common ratios within each category and discusses the importance and limitations of ratio analysis.
Chapter 1- Environment of Business Finance.pptx
The document discusses the classification of finance into public finance, institutional finance, private finance, and international finance. It also defines market capitalization as the number of outstanding shares of a company multiplied by the share price.
1.5 a's of financial management
This document discusses the A's of Financial Management which are the key aspects covered in a financial management course. It was presented by Dr. V. M. Tidake from Sanjivani College of Engineering and discusses understanding the A's of financial management as the objective of the session. Contact information is provided for Dr. Tidake for any additional details.
1.4 functions of financial management
This document discusses the functions of financial management and is presented by Dr. V. M. Tidake from Sanjivani College of Engineering. It lists understanding the functions of financial management as a learning objective and poses an exercise for students to state and explain the functions of financial management. It concludes by thanking the audience and providing contact information for Dr. Tidake.
1.3 approaches to finance
This document discusses approaches to financial management and is a presentation by Dr. V. M. Tidake on the topic. It outlines that students will understand the different approaches to financial management by the end of the session. It prompts an exercise question for students to state and explain the various approaches to finance and provides contact information for Dr. Tidake for any additional details.
1.2 introduction to financial management
This document is a presentation on an introduction to financial management given by Dr. V. M. Tidake at Sanjivani College of Engineering. The presentation covers the objectives of financial management which include maximizing shareholder wealth and firm value. It also contains sample problems and exercises for students to understand financial management concepts. Contact information is provided for Dr. Tidake for any additional questions.
1.1 introduction to finance
This document is a presentation about financial management that was given by Dr. V. M. Tidake. The presentation introduces finance and discusses its classification into public, institutional, private, and international finance. It aims to help students understand the introduction to finance and business finance in particular. Contact information is provided for Dr. Tidake to get more details.
Accounting standards notes Dr. V M Tidake
The document discusses Indian Accounting Standards (AS) as issued by the Institute of Chartered Accountants of India (ICAI). It provides definitions and explanations of key terms like accounting standards, accounting policies, revenue recognition, accounting for fixed assets, and depreciation accounting. Some of the main points covered include that accounting standards aim to standardize accounting policies and practices, the Accounting Standards Board of ICAI prepares the standards, and there are currently 32 accounting standards in India. The document also provides details on the objectives, disclosure requirements, and treatment of certain items under some major accounting standards like AS 1 on accounting policies, AS 6 on depreciation, AS 9 on revenue recognition, and AS 10 on fixed assets.
5.2.8 case 2 normal probability distribution
This document discusses solving probability problems using the normal distribution. It provides an example of test scores that are normally distributed with a mean of 14 and standard deviation of 2.5. It then calculates:
1) The number of students who scored between 12 and 15, which is 444.
2) The number of students who scored above 18, which is 55.
3) The number of students who scored below 8, which is 8.
The calculations are shown by converting the data values to standard normal variables and looking them up in a z-table.
5.2.7 case 1 normal probability distribution
This document discusses a case study on the normal probability distribution. It examines the weekly wages of 1000 workers which are normally distributed with a mean of Rs. 70 and standard deviation of Rs. 5. It estimates the number of workers whose weekly wages are: (i) Between Rs. 70 and Rs. 72 (155 workers) (ii) Between Rs. 69 and Rs. 72 (235 workers) (iii) More than Rs. 75 (159 workers) (iv) Less than Rs. 63 (81 workers). The document explains how to solve each case using the normal distribution and z-table. It concludes by asking students to explain the case study on normal probability distribution.
5.2.6 case 1 poisson probability distribution
This document discusses using the Poisson distribution to calculate probabilities related to defective electric bulbs. It provides an example where the probability of a bulb being defective is 5%. It then calculates: (1) the probability that none of the 100 bulbs in a box are defective is 0.007, (2) the probability that 3 bulbs are defective is 0.146, and (3) the probability that more than 3 bulbs are defective is 0.725. The document was presented by Dr. V. M. Tidake and discusses using the Poisson distribution to solve probability problems related to rare events.
5.2.4 case 1 binomial probability distribution
This document discusses the binomial probability distribution and provides examples of calculating probabilities of coin toss outcomes. Specifically, it addresses:
1) Calculating the probability of getting exactly 5 heads, at least 8 heads, not more than 3 heads, and at least one head when tossing 10 coins.
2) If the 10 coin toss is repeated 50 times, calculating the expected number of times exactly 5 heads will occur.
3) Explaining the case of the binomial probability distribution and providing contact information for the presenter.
5.10 case 7 probability
This document discusses a probability case study presented by Dr. V. M. Tidake. The case study asks the reader to calculate the probability that: (1) both a man and his wife will be alive 25 years in the future, given the man has a 30% probability of being alive and the wife a 40% probability; (2) only the man will be alive; (3) only the woman will be alive; and (4) at least one of them will be alive. The document provides the calculations to find each of these probabilities.
5.9 case 6 probability
This document presents a probability case study involving two friends, A and B, applying for two vacancies with different probabilities of selection. The probabilities of A and B being selected are 1/4 and 1/5, respectively. The document calculates the probabilities of: (1) one of them being selected; (2) both being selected; (3) neither being selected; and (4) at least one being selected. It concludes by asking students to solve the case study on probability.
5.8 case 5 probability
This document contains a presentation on probability by Dr. V. M. Tidake for students of the MBA program at Sanjivani College of Engineering, Kopargaon. It provides a case study on calculating the probability that the daily production at a factory will be less than the average production for the week, given production numbers for 7 days. The average production for the week is calculated to be 59 units. The probability is calculated as 3/7, as the production was less than average on 3 of the 7 days. Students are instructed to solve this case study on probability.
5.7 case 4 probability
This document discusses a probability case study presented by Dr. V. M. Tidake. The case study asks what is the probability of drawing a red queen from a standard 52-card deck given that the card drawn is a face card (jack, queen, or king). The document calculates this probability as 1/6 by defining the events, determining the probabilities of each event, and using the formula for conditional probability. It was presented to help students understand case 4 on probability.
5.6 case 3 probability
This document discusses a probability case study presented by Dr. V. M. Tidake. It examines the probability that a randomly selected leap year will have 53 Mondays. It explains that a leap year has 366 days, with 52 full weeks of 7 days each accounting for 364 days. The remaining 2 days could fall on 7 possible combinations of days, but only 2 will result in a Monday. Therefore, the probability that a leap year will have 53 Mondays is 2/7. Students are instructed to solve this case study on probability. The document provides contact information for Dr. Tidake for any additional details.
More from Vishal Tidake (20)
Recently uploaded
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
原版制作(swinburne毕业证书)斯威本科技大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(swinburne毕业证书)斯威本科技大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UniSA毕业证书)南澳大学毕业证如何办理
原版定制【微信:41543339】【(UniSA毕业证书)南澳大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
原版制作(Deakin毕业证书)迪肯大学毕业证学位证一模一样
学校原件一模一样【微信：741003700 】《(Deakin毕业证书)迪肯大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Natural Language Processing (NLP), RAG and its applications .pptx
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
Recently uploaded (20)
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
The Building Blocks of QuestDB, a Time Series Database
The Building Blocks of QuestDB, a Time Series Database
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
Natural Language Processing (NLP), RAG and its applications .pptx
Natural Language Processing (NLP), RAG and its applications .pptx
2.6 initial feasible solution by vam
- 1. www.sanjivanimba.org.in Unit No.2. DECISION SCIENCE Presented By: Dr. V. M. Tidake Ph. D (Financial Management), MBA(FM), MBA(HRM) BE(Chem) Dean, EDP & Associate Professor MBA 1 Sanjivani College of Engineering, Kopargaon Department of MBA www.sanjivanimba.org.in
- 2. www.sanjivanimba.org.in 302-DECISION SCIENCE Unit No.2. Transportation & Assignment 2.6 Initial Feasible Solution by VAM Presented By: Dr. V. M. Tidake Ph. D (Financial Management), MBA(FM), MBA(HRM) BE(Chem) Dean EDP & Associate Professor MBA 2 Sanjivani College of Engineering, Kopargaon Department of MBA www.sanjivanimba.org.in
- 3. www.sanjivanimba.org.in TRANSPORTATION & ASSIGNMENT At the End of the Session Student will be able to understand- A. Initial Feasible Solution by VAM Method.
- 9. www.sanjivanimba.org.in EXERCISE Solve the Above Case using VAM as a practice Exercise.
- 10. www.sanjivanimba.org.in For More Details Contact Dr. V M Tidake tidkevishal@gmail.com tidkevishalmba@sanjivani.org.in