1.7 ds case 1 graphical solution to lpp
•Download as PPT, PDF•
2 likes•211 views
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.
Report
Share
Report
Share
Recommended
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.
2.6 initial feasible solution by vam
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.
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.
2.4 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.
2.3 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.
2.12 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.
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.
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.
Recommended
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.
2.6 initial feasible solution by vam
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.
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.
2.4 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.
2.3 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.
2.12 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.
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.
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.
1.9 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.
2.9 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.
2.14 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.
2.13 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.
2.18 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.
2.7 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.
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.
2.10 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.
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.
2.15 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.
2.2 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.
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.
1.11 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 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.
1.6 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.
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
1.1 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.
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.
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.
More Related Content
What's hot
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.
2.9 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.
2.14 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.
2.13 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.
2.18 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.
2.7 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.
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.
2.10 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.
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.
2.15 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.
2.2 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.
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.
1.11 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 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.
1.6 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.
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
1.1 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
2.10 special case prohibited route solution by modi method
2.10 special case prohibited route solution by modi method
2.20 special case of prohibited route in assignment
2.20 special case of prohibited route in assignment
1.6 ds graphical solution linear programing problem
1.6 ds graphical solution linear programing problem
2.19 special case of unbalanced problem in assignment
2.19 special case of unbalanced problem in assignment
Similar to 1.7 ds case 1 graphical solution to lpp
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.
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.
4.8 case 1 on network analysis
This document presents a case study on network analysis. It describes a project with 14 activities labeled A to N. The precedence relationships between the activities are listed, with A being the first activity and N the last. A network diagram is drawn showing the activities as nodes and precedence relationships as arrows. The network diagram is then numbered from 1 to 11. Finally, an exercise is provided to draw a network diagram based on given information.
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.
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.
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.
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.
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.
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.
5.2.6 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.
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.
4.2 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.
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.
3.1 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.
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.
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.
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.
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 1.7 ds case 1 graphical solution to lpp (18)
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.
1.1 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.
5.2.4 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.
5.7 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.
5.5 case 2 probability
This document discusses probability concepts through examples involving dice rolls. It presents three probability questions: 1) finding the probability of rolling a sum greater than 9 on two dice, 2) finding the probability of rolling a sum that is a multiple of 3, and 3) finding the probability of rolling a sum that is divisible by 3 or 4. It defines the sample space of all possible dice roll outcomes. It then calculates the probabilities for each question by determining the number of favorable outcomes and dividing by the total number of outcomes. The probabilities calculated are 1/6, 1/3, and 5/9 respectively. Students are instructed to solve similar probability problems involving dice rolls.
5.4 case 1 probability
This document is a presentation on probability that was given by Dr. V. M. Tidake. It contains examples of calculating the probability of drawing certain cards from a standard 52-card deck. Specifically, it calculates the probability of drawing a non-spade card as 3/4 and the probability of drawing a face card as 3/13. The document provides the total number of possible outcomes, the number of favorable outcomes, and uses the formula for probability to calculate the results.
4.15 case 1 on network analysis
This document presents a case study on network analysis for a project. It includes the activities, their durations, and precedence relationships. The network diagram is drawn showing the critical path. The critical path is identified which has a duration of 10 weeks. The effect of changing the duration of activities D and F on the critical path duration is determined. If D is changed to 6 weeks, the critical path duration increases to 15 weeks. If F is changed to 8 weeks, the critical path duration increases to 14 weeks.
4.14 case 1 pert
The document discusses Program Evaluation and Review Technique (PERT) for a project with 8 activities. It shows the optimistic, most likely, and pessimistic times for each activity. It then calculates the expected time for each activity using PERT formulas. A network diagram is drawn showing the dependencies between activities. The critical path through the network is identified as activities 1-2-4-6-7-8, with an earliest completion time of 32.67 weeks.
More from Vishal Tidake (19)
Recently uploaded
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
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
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
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
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【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(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
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."
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
毕业原版【微信:41543339】【(Glasgow毕业证书)格拉斯哥大学毕业证】【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！
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【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！
Recently uploaded (20)
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
Udemy_2024_Global_Learning_Skills_Trends_Report (1).pdf
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
1.7 ds case 1 graphical solution to lpp
- 1. www.sanjivanimba.org.in 302-DECISION SCIENCE Unit No.1. LPP 1.7 Case 1: Graphical Solution to LPPPresented 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 1.7 CASE 1: GRAPHCIAL SOLUTION TO LPP LINEAR PROGRAMMING PROBLEM
- 3. www.sanjivanimba.org.in LINEAR PROGRAMING PROBLEM At the End of the Session Student will be able to provide- A. Graphical Solution to LPP
- 5. www.sanjivanimba.org.in EXERCISE Solve the Case 1: Graphical Solution to LPP as a Practice Exercise
- 6. www.sanjivanimba.org.in For More Details Contact Dr. V M Tidake tidkevishal@gmail.com tidkevishalmba@sanjivani.org.in