The document describes several applications of linear programming (LP) models in business contexts such as marketing, manufacturing, and scheduling. It provides examples to illustrate LP formulations for media mix optimization, production planning, and survey sampling cost minimization. Screenshots of LP solutions in Excel and other software are presented. The goal of the chapter is to help students understand how to model and solve real-world LP problems.
The document describes several examples of using linear programming (LP) to solve optimization problems in marketing, manufacturing, and finance. It provides details of LP models for selecting an optimal media mix in advertising, determining the best product mix for a tie manufacturer, and identifying an ideal investment portfolio to maximize returns while meeting risk constraints. Excel solutions are presented for each case study.
This document provides an overview of integer programming, goal programming, and nonlinear programming. It begins with learning objectives and an outline of topics to be covered, which include integer programming, modeling with binary variables, goal programming, and nonlinear programming. Several examples are provided to illustrate integer programming problems and how they can be formulated and solved. Mixed-integer and binary variable modeling are explained. Goal programming and how it differs from linear programming in addressing multiple objectives is introduced.
Bba 3274 qm week 10 integer programmingStephen Ong
This document discusses integer programming and various types of integer programming problems that commonly arise in business. It provides an example of a pure integer programming problem involving production planning at a company that makes chandeliers and ceiling fans. The document also discusses mixed-integer programming problems and modeling problems using 0-1 variables, providing examples for capital budgeting and facility location problems. Various software packages are demonstrated for solving integer programming problems.
The document provides an outline of topics related to linear programming, including:
1) An introduction to linear programming models and examples of problems that can be solved using linear programming.
2) Developing linear programming models by determining objectives, constraints, and decision variables.
3) Graphical and simplex methods for solving linear programming problems.
4) Using a simplex tableau to iteratively solve a sample product mix problem to find the optimal solution.
Linear programming (LP) is a technique used by operations managers to allocate scarce resources. LP involves defining an objective (e.g. maximize profit), decision variables (e.g. production levels), and constraints (e.g. resource limits). The objective and constraints must be expressed as linear equations. Common LP applications include determining optimal product mix, production plans, ingredient mixes, and resource allocation. LP problems are formulated by defining the objective and variables, writing the objective function, describing constraints, and specifying the mathematical model. Examples demonstrate how to set up LP models to maximize profit or minimize costs given production and resource constraints.
This document provides an introduction and overview of integer programming problems. It discusses different types of integer programming problems including pure integer, mixed integer, and 0-1 integer problems. It provides examples to illustrate how to formulate integer programming problems as mathematical models. The document also discusses common solution methods for integer programming problems, including the cutting-plane method. An example of the cutting-plane method is provided to demonstrate how it works to find an optimal integer solution.
This document discusses linear programming applications in marketing, manufacturing, and other areas. It provides examples to demonstrate how to model and solve linear programming problems involving media mix optimization, production scheduling, inventory management, and other scenarios. Specifically, it presents sample problems and solutions involving marketing mix optimization for a gambling club, sampling costs for a market research firm, production planning for a tie manufacturer, and multi-period production scheduling for an electric motor company. The chapter aims to illustrate how to apply linear programming to optimize objectives subject to constraints across various business applications.
This chapter discusses transportation, assignment, and transshipment models as special cases of linear programming network flow problems. It provides learning objectives and an outline of topics to be covered, which include introducing the transportation problem using an example of distributing office desks from factories to warehouses, formulating it as a linear program, and solving it using the transportation algorithm. The chapter also discusses the assignment problem using an example of assigning workers to repair jobs and the transshipment problem using an example of shipping snow blowers through distribution centers. It describes developing initial feasible solutions using the northwest corner rule and improving solutions using the stepping stone method.
The document describes several examples of using linear programming (LP) to solve optimization problems in marketing, manufacturing, and finance. It provides details of LP models for selecting an optimal media mix in advertising, determining the best product mix for a tie manufacturer, and identifying an ideal investment portfolio to maximize returns while meeting risk constraints. Excel solutions are presented for each case study.
This document provides an overview of integer programming, goal programming, and nonlinear programming. It begins with learning objectives and an outline of topics to be covered, which include integer programming, modeling with binary variables, goal programming, and nonlinear programming. Several examples are provided to illustrate integer programming problems and how they can be formulated and solved. Mixed-integer and binary variable modeling are explained. Goal programming and how it differs from linear programming in addressing multiple objectives is introduced.
Bba 3274 qm week 10 integer programmingStephen Ong
This document discusses integer programming and various types of integer programming problems that commonly arise in business. It provides an example of a pure integer programming problem involving production planning at a company that makes chandeliers and ceiling fans. The document also discusses mixed-integer programming problems and modeling problems using 0-1 variables, providing examples for capital budgeting and facility location problems. Various software packages are demonstrated for solving integer programming problems.
The document provides an outline of topics related to linear programming, including:
1) An introduction to linear programming models and examples of problems that can be solved using linear programming.
2) Developing linear programming models by determining objectives, constraints, and decision variables.
3) Graphical and simplex methods for solving linear programming problems.
4) Using a simplex tableau to iteratively solve a sample product mix problem to find the optimal solution.
Linear programming (LP) is a technique used by operations managers to allocate scarce resources. LP involves defining an objective (e.g. maximize profit), decision variables (e.g. production levels), and constraints (e.g. resource limits). The objective and constraints must be expressed as linear equations. Common LP applications include determining optimal product mix, production plans, ingredient mixes, and resource allocation. LP problems are formulated by defining the objective and variables, writing the objective function, describing constraints, and specifying the mathematical model. Examples demonstrate how to set up LP models to maximize profit or minimize costs given production and resource constraints.
This document provides an introduction and overview of integer programming problems. It discusses different types of integer programming problems including pure integer, mixed integer, and 0-1 integer problems. It provides examples to illustrate how to formulate integer programming problems as mathematical models. The document also discusses common solution methods for integer programming problems, including the cutting-plane method. An example of the cutting-plane method is provided to demonstrate how it works to find an optimal integer solution.
This document discusses linear programming applications in marketing, manufacturing, and other areas. It provides examples to demonstrate how to model and solve linear programming problems involving media mix optimization, production scheduling, inventory management, and other scenarios. Specifically, it presents sample problems and solutions involving marketing mix optimization for a gambling club, sampling costs for a market research firm, production planning for a tie manufacturer, and multi-period production scheduling for an electric motor company. The chapter aims to illustrate how to apply linear programming to optimize objectives subject to constraints across various business applications.
This chapter discusses transportation, assignment, and transshipment models as special cases of linear programming network flow problems. It provides learning objectives and an outline of topics to be covered, which include introducing the transportation problem using an example of distributing office desks from factories to warehouses, formulating it as a linear program, and solving it using the transportation algorithm. The chapter also discusses the assignment problem using an example of assigning workers to repair jobs and the transshipment problem using an example of shipping snow blowers through distribution centers. It describes developing initial feasible solutions using the northwest corner rule and improving solutions using the stepping stone method.
The document discusses process design and facility layout. It covers types of processes like projects, job shops, batch processing, repetitive/assembly and continuous processing. The key factors in selecting a process are variety, flexibility, volume and the tradeoff between them. Layout types include product layouts that group equipment by product steps, process layouts that group by function, and combination layouts. The document provides examples of line balancing to optimize workstation times and productivity.
Quantity Discount with constant carrying costJh Labonno
The document discusses a quantity discount schedule offered by a cup manufacturer to an arena for its annual demand of 2.3 million cups. The arena has an annual ordering cost of $320 and carrying cost per box of $1.90. The optimal order quantity that minimizes total annual inventory cost is determined to be 20,000 boxes, with a total cost of $893,368.
This document provides details on 10 decision problems involving operations research and decision theory. The problems cover topics like determining optimal inventory levels, whether to invest in market research, extending credit to customers, and deciding whether to drill for oil or lease land. Complex decision trees, probabilities, costs, and profits are presented to analyze the optimal choices for each scenario.
Linear programming - Model formulation, Graphical MethodJoseph Konnully
The document discusses linear programming, including an overview of the topic, model formulation, graphical solutions, and irregular problem types. It provides examples to demonstrate how to set up linear programming models for maximization and minimization problems, interpret feasible and optimal solution regions graphically, and address multiple optimal solutions, infeasible solutions, and unbounded solutions. The examples aid in understanding the key steps and components of linear programming models.
The document discusses key concepts in supply chain management. It defines supply chain management as integrating activities involved in procuring materials, transforming them into products, and delivering products to customers. It also discusses how supply chain decisions impact business strategies like low-cost, differentiation, and responsiveness strategies. The document notes that supply chains present risks and outlines ways to mitigate risks in processes, controls, and the operating environment. Finally, it discusses ethics, sustainability, and economics in supply chain management.
This document discusses factors to consider in making location decisions. It provides discussion questions about specific companies' location strategies, such as FedEx choosing Memphis for its central location. It also lists numerous qualitative and quantitative factors that can influence location decisions for companies operating domestically or internationally. These factors include labor costs, transportation costs, market access, incentives, and clustering tendencies. The document provides examples and models for analyzing location decisions.
The chapter discusses operations management strategies related to human resources and job design. It describes how job design involves specifying tasks and how they are completed. Components of job design include specialization, expansion through enlargement and enrichment, psychological factors, self-directed teams, and motivation systems. Effective job design considers ergonomics. Labor standards set target times for job completion and are used for various planning and costing purposes. Sources of labor standards include historical data, time studies, predetermined times, and work sampling.
This document outlines course material for Operations Research. It covers linear programming models, including graphical and simplex methods for solving linear programs. Specific chapters outlined include linear programming introduction and formulation, the simplex method, duality, transportation and network models. Examples of linear programming problems are provided, such as production planning, diet formulation, and blending problems. The key concepts of decision variables, objectives, and constraints in linear programming are defined.
The document discusses inventory management concepts including economic order quantity (EOQ) models. It defines key inventory costs: ordering/setup costs, holding/carrying costs, shortage costs. The EOQ model balances ordering and carrying costs to determine the optimal order quantity. An example calculates the EOQ, annual carrying cost, ordering cost, and total annual cost for a company with constant demand and known costs and demand values. The optimal order quantity minimizes total annual inventory costs.
The Least Cost Method is another method used to obtain the initial feasible solution for the transportation problem. Here, the allocation begins with the cell which has the minimum cost. The lower cost cells are chosen over the higher-cost cell with the objective to have the least cost of transportation.
SM CH 1 STRATEGIC MANAGEMENT ESSENTIALSShadina Shah
The document discusses strategic management, outlining its key stages and terms. It describes the strategic management process as having three main stages: strategy formulation, implementation, and evaluation. Some key points covered include defining strategic management, discussing the need for strategic planning, explaining why some firms do not strategically plan, and comparing similarities between business and military strategy.
This document discusses competitiveness, strategy, and productivity. It defines competitiveness as how effectively an organization meets customer wants and needs relative to competitors. Strategy is defined as plans for achieving organizational goals, while tactics are specific methods for accomplishing strategies. Productivity is a measure of output to input and is important for effective resource use. Factors that influence productivity include capital, technology, management methods, and other organizational factors. Measuring and improving productivity is key for organizational success.
This document provides an overview of operations research and linear programming. It defines operations research as optimal decision-making and modeling of deterministic and probabilistic systems from real life that involve allocating limited resources. Linear programming is introduced as an optimization technique for problems with a linear objective function and constraints. The document outlines the assumptions, formulation, and solution approach for linear programming models. Examples of linear programming formulations are provided for production mix, portfolio selection, and production planning problems.
This document presents an example decision problem to demonstrate decision tree analysis. It describes three potential decisions - expand, maintain status quo, or sell now - under two possible future states, good or poor foreign competitive conditions. It then outlines the steps to analyze the problem: 1) determine the best decision without probabilities using various criteria, 2) determine the best decision with probabilities using expected value and opportunity loss, 3) compute the expected value of perfect information, and 4) develop a decision tree showing expected values at each node.
This linear programming problem involves a company that produces plates and mugs with limited labor and clay resources. The objective is to maximize total profit. There are two decision variables - number of plates and number of mugs. The constraints are that labor used cannot exceed 40 hours, clay used cannot exceed 120 pounds, and production must be non-negative. The linear programming model combines the objective function and constraints to determine the optimal production levels.
Learning Objectives
Outline the process of strategic planning in the context of the global marketplace.
Examine both the external and internal factors that determine the conditions for development of strategy and resource allocation.
Illustrate how best to utilize the environmental conditions within the competitive challenges and resources of the firm to develop effective programs.
Suggest how to achieve a balance between local and regional/global priorities and concerns in the implementation of strategy.
Business Models in Strategic Management.PPTXAhmad Thanin
A business model is a company's core strategy for profitably doing business. Models generally include information like products or services the business plans to sell, target markets, and any anticipated expenses. The two levers of a business model are pricing and costs.
This document provides 10 teaching suggestions for instructors to help students better understand key concepts in decision analysis. Suggestions include having students describe personal decisions they made and which steps of the decision-making process they used; role playing to define problems and alternatives; discussing types of decisions under certainty, risk, and uncertainty; and using decision trees and Bayesian analysis to solve problems. The goal is for students to recognize how decision theory applies to important real-life decisions. Alternative examples provided apply concepts like expected monetary value to problems involving purchasing industrial robots.
This document provides an introduction and overview of goal programming (GP). It explains that GP is useful when an organization has multiple, sometimes conflicting goals that cannot all be optimized at the same time like in linear programming. GP establishes numeric goals for each objective and attempts to achieve each goal to a satisfactory level by minimizing deviations. The document outlines the basic components of a GP model, including defining goals and constraints, assigning priority levels to goals, and introducing deviational variables. It also provides an example to illustrate how to formulate a GP model and solve it graphically or using the modified simplex method.
The document discusses various location strategy considerations for operations management. It covers factors that affect location decisions such as labor productivity, exchange rates, political risks, and proximity to markets/suppliers. Methods for evaluating location alternatives are described, including the factor-rating method, locational break-even analysis, and center-of-gravity method. Specific location strategies for different industries like hotels, call centers, and how companies use geographic information systems are also summarized.
This document discusses several applications of linear programming (LP) models. It provides examples of how LP can be used to help with marketing applications like media mix optimization and marketing research sampling strategies. Manufacturing applications covered include production mix planning to maximize profit given material and demand constraints, and multi-period production scheduling to minimize total costs considering factors like inventory, labor hours, and warehouse space. The document provides detailed examples and mathematical formulations of LP models for a gambling club's advertising, a research firm's survey sample, and a manufacturer's product mix and production scheduling.
The document describes three classic applications of linear programming (LP):
1) A plywood company used LP to determine the optimal product mix, increasing profits by 20%.
2) An airline used LP to design employee work schedules most efficiently, saving $6 million annually.
3) An oil company used LP to coordinate supply, distribution, and marketing, reducing inventory and adding $14 million to profits.
The document discusses process design and facility layout. It covers types of processes like projects, job shops, batch processing, repetitive/assembly and continuous processing. The key factors in selecting a process are variety, flexibility, volume and the tradeoff between them. Layout types include product layouts that group equipment by product steps, process layouts that group by function, and combination layouts. The document provides examples of line balancing to optimize workstation times and productivity.
Quantity Discount with constant carrying costJh Labonno
The document discusses a quantity discount schedule offered by a cup manufacturer to an arena for its annual demand of 2.3 million cups. The arena has an annual ordering cost of $320 and carrying cost per box of $1.90. The optimal order quantity that minimizes total annual inventory cost is determined to be 20,000 boxes, with a total cost of $893,368.
This document provides details on 10 decision problems involving operations research and decision theory. The problems cover topics like determining optimal inventory levels, whether to invest in market research, extending credit to customers, and deciding whether to drill for oil or lease land. Complex decision trees, probabilities, costs, and profits are presented to analyze the optimal choices for each scenario.
Linear programming - Model formulation, Graphical MethodJoseph Konnully
The document discusses linear programming, including an overview of the topic, model formulation, graphical solutions, and irregular problem types. It provides examples to demonstrate how to set up linear programming models for maximization and minimization problems, interpret feasible and optimal solution regions graphically, and address multiple optimal solutions, infeasible solutions, and unbounded solutions. The examples aid in understanding the key steps and components of linear programming models.
The document discusses key concepts in supply chain management. It defines supply chain management as integrating activities involved in procuring materials, transforming them into products, and delivering products to customers. It also discusses how supply chain decisions impact business strategies like low-cost, differentiation, and responsiveness strategies. The document notes that supply chains present risks and outlines ways to mitigate risks in processes, controls, and the operating environment. Finally, it discusses ethics, sustainability, and economics in supply chain management.
This document discusses factors to consider in making location decisions. It provides discussion questions about specific companies' location strategies, such as FedEx choosing Memphis for its central location. It also lists numerous qualitative and quantitative factors that can influence location decisions for companies operating domestically or internationally. These factors include labor costs, transportation costs, market access, incentives, and clustering tendencies. The document provides examples and models for analyzing location decisions.
The chapter discusses operations management strategies related to human resources and job design. It describes how job design involves specifying tasks and how they are completed. Components of job design include specialization, expansion through enlargement and enrichment, psychological factors, self-directed teams, and motivation systems. Effective job design considers ergonomics. Labor standards set target times for job completion and are used for various planning and costing purposes. Sources of labor standards include historical data, time studies, predetermined times, and work sampling.
This document outlines course material for Operations Research. It covers linear programming models, including graphical and simplex methods for solving linear programs. Specific chapters outlined include linear programming introduction and formulation, the simplex method, duality, transportation and network models. Examples of linear programming problems are provided, such as production planning, diet formulation, and blending problems. The key concepts of decision variables, objectives, and constraints in linear programming are defined.
The document discusses inventory management concepts including economic order quantity (EOQ) models. It defines key inventory costs: ordering/setup costs, holding/carrying costs, shortage costs. The EOQ model balances ordering and carrying costs to determine the optimal order quantity. An example calculates the EOQ, annual carrying cost, ordering cost, and total annual cost for a company with constant demand and known costs and demand values. The optimal order quantity minimizes total annual inventory costs.
The Least Cost Method is another method used to obtain the initial feasible solution for the transportation problem. Here, the allocation begins with the cell which has the minimum cost. The lower cost cells are chosen over the higher-cost cell with the objective to have the least cost of transportation.
SM CH 1 STRATEGIC MANAGEMENT ESSENTIALSShadina Shah
The document discusses strategic management, outlining its key stages and terms. It describes the strategic management process as having three main stages: strategy formulation, implementation, and evaluation. Some key points covered include defining strategic management, discussing the need for strategic planning, explaining why some firms do not strategically plan, and comparing similarities between business and military strategy.
This document discusses competitiveness, strategy, and productivity. It defines competitiveness as how effectively an organization meets customer wants and needs relative to competitors. Strategy is defined as plans for achieving organizational goals, while tactics are specific methods for accomplishing strategies. Productivity is a measure of output to input and is important for effective resource use. Factors that influence productivity include capital, technology, management methods, and other organizational factors. Measuring and improving productivity is key for organizational success.
This document provides an overview of operations research and linear programming. It defines operations research as optimal decision-making and modeling of deterministic and probabilistic systems from real life that involve allocating limited resources. Linear programming is introduced as an optimization technique for problems with a linear objective function and constraints. The document outlines the assumptions, formulation, and solution approach for linear programming models. Examples of linear programming formulations are provided for production mix, portfolio selection, and production planning problems.
This document presents an example decision problem to demonstrate decision tree analysis. It describes three potential decisions - expand, maintain status quo, or sell now - under two possible future states, good or poor foreign competitive conditions. It then outlines the steps to analyze the problem: 1) determine the best decision without probabilities using various criteria, 2) determine the best decision with probabilities using expected value and opportunity loss, 3) compute the expected value of perfect information, and 4) develop a decision tree showing expected values at each node.
This linear programming problem involves a company that produces plates and mugs with limited labor and clay resources. The objective is to maximize total profit. There are two decision variables - number of plates and number of mugs. The constraints are that labor used cannot exceed 40 hours, clay used cannot exceed 120 pounds, and production must be non-negative. The linear programming model combines the objective function and constraints to determine the optimal production levels.
Learning Objectives
Outline the process of strategic planning in the context of the global marketplace.
Examine both the external and internal factors that determine the conditions for development of strategy and resource allocation.
Illustrate how best to utilize the environmental conditions within the competitive challenges and resources of the firm to develop effective programs.
Suggest how to achieve a balance between local and regional/global priorities and concerns in the implementation of strategy.
Business Models in Strategic Management.PPTXAhmad Thanin
A business model is a company's core strategy for profitably doing business. Models generally include information like products or services the business plans to sell, target markets, and any anticipated expenses. The two levers of a business model are pricing and costs.
This document provides 10 teaching suggestions for instructors to help students better understand key concepts in decision analysis. Suggestions include having students describe personal decisions they made and which steps of the decision-making process they used; role playing to define problems and alternatives; discussing types of decisions under certainty, risk, and uncertainty; and using decision trees and Bayesian analysis to solve problems. The goal is for students to recognize how decision theory applies to important real-life decisions. Alternative examples provided apply concepts like expected monetary value to problems involving purchasing industrial robots.
This document provides an introduction and overview of goal programming (GP). It explains that GP is useful when an organization has multiple, sometimes conflicting goals that cannot all be optimized at the same time like in linear programming. GP establishes numeric goals for each objective and attempts to achieve each goal to a satisfactory level by minimizing deviations. The document outlines the basic components of a GP model, including defining goals and constraints, assigning priority levels to goals, and introducing deviational variables. It also provides an example to illustrate how to formulate a GP model and solve it graphically or using the modified simplex method.
The document discusses various location strategy considerations for operations management. It covers factors that affect location decisions such as labor productivity, exchange rates, political risks, and proximity to markets/suppliers. Methods for evaluating location alternatives are described, including the factor-rating method, locational break-even analysis, and center-of-gravity method. Specific location strategies for different industries like hotels, call centers, and how companies use geographic information systems are also summarized.
This document discusses several applications of linear programming (LP) models. It provides examples of how LP can be used to help with marketing applications like media mix optimization and marketing research sampling strategies. Manufacturing applications covered include production mix planning to maximize profit given material and demand constraints, and multi-period production scheduling to minimize total costs considering factors like inventory, labor hours, and warehouse space. The document provides detailed examples and mathematical formulations of LP models for a gambling club's advertising, a research firm's survey sample, and a manufacturer's product mix and production scheduling.
The document describes three classic applications of linear programming (LP):
1) A plywood company used LP to determine the optimal product mix, increasing profits by 20%.
2) An airline used LP to design employee work schedules most efficiently, saving $6 million annually.
3) An oil company used LP to coordinate supply, distribution, and marketing, reducing inventory and adding $14 million to profits.
Exam 3 Extra Problems1 Formulating the Production Planning.docxcravennichole326
Exam 3 Extra Problems
1 Formulating the Production Planning Model
A company manufactures two electric products, air conditioners and fans. The assembly process
is similar in that both of them must pass through two stages of production: wiring and drilling.
Each air conditioner takes 7 hours of wiring and 5 hours of drilling, while each fan must go through
3 hours of wiring and 2 hours of drilling. During the next period, 260 hours of wiring time are
available and 160 hours of drilling time may be used. Each air conditioner sold yields a profit of
$27, and each fan sold yields a profit of $17.
1. Formulate the objective function algebraically.
2. Write the resource constraints. Would demand constraints or non-negativity constraints be
more appropriate for this system of equations?
3. Upon looking over previous years demand, management has decided that to ensure an ad-
equate supply of air conditioners for a contract, at least 24 units should be manufactured.
Additionally, because they incurred an oversupply in the previous period, management has
also requested that no more than 50 fans be produced during this period. Assist the company
by reformulating the necessary constraints from the above question.
1
The corresponding sensitivity report for the original problem is shown below:
Variable cells
Final Reduced Objective Allowable Allowable
Cell Name Value Cost Coefficient Increase Decrease
$B$6 Air conditioners 0 A 27 15.5 1E+30
$C$6 Fans 80 B 17 1E+30 6.2
Constraints
Final Shadow Constraint Allowable Allowable
Cell Name Value Price R.H. Side Increase Decrease
$D$4 Wiring 240 0 260 1E+30 20
$D$5 Drilling 160 8.5 160 13.3 160
4. Calculate the missing values “A” and “B” in the above report.
5. Suppose management has decided to sell some of it’s drilling capacity for $6 per hour. If 15
hours were sold, what is the impact on profit? What is the new objective function value?
6. The previous page mentions management requiring at least 24 units of air conditioners to be
produced. If this policy were to be implemented, how will this affect contribution?
2
2 Understanding the Sensitivity Report
A manufacturing company produces two products X and Y with contributions to profit per unit
of $10 and $9, respectively. Each product must pass through 4 departments during the manufac-
turing process. The company has formulated a linear programming model to develop a production
strategy to maximize profit, and satisfy the demand of 300 and 500 units for product X and Y ,
respectively. The corresponding sensitivity report for the problem is shown below:
Variable cells
Final Reduced Objective Allowable Allowable
Cell Name Value Cost Coefficient Increase Decrease
$B$16 Product X 300 0 10 17 1E+30
$B$17 Product Y 500 0 9 1E+30 5.6666667
Constraints
Final Shadow Constraint Allowable Allowable
Cell Name Value Price R.H. Side Increase Decrease
$B$21 Department 1 1300 0 6500 1E+30 5200
$B$22 Department 2 2100 0 6000 1E+30 3900
$B$23 Dep.
The document provides an overview of management science/operations research including:
- A brief history noting early developments and contributors.
- Applications across various industries showing cost savings and increased revenues.
- Current professional organizations and typical jobs for graduates.
- How management science techniques can help with system design, operation, and decision making.
- The benefits and tradeoffs of different model types like iconic, analog, and mathematical models.
Craig Friderichs Head of Health GSMA #MWC14 #mHealth3GDR
This document provides information about mobile health from the GSMA. It includes contact information and links to follow GSMA on social media. Graphics show the strong growth in mobile health services launched over the last 3 years, with the majority led by non-MNO consumer models. Additional charts analyze the business models and evolution of sectors like health, education and agriculture. The document emphasizes understanding the flow of money to define target markets, partners and sustainable business models in mobile health.
This document provides an overview of how to model and solve linear programming (LP) problems using spreadsheets. It discusses the steps to implement an LP model in a spreadsheet, including organizing the data, reserving cells for decision variables, and creating formulas for the objective function and constraints. The document then provides examples of modeling various LP problems, such as production planning, transportation, and blending, in spreadsheets. Guidelines for effective spreadsheet design to ensure communication, reliability, auditability and modifiability are also presented.
This document outlines concepts related to decision making under uncertainty and risk. It discusses six steps to decision making, including defining the problem, listing alternatives and outcomes, and identifying payoffs. It then covers various decision making models for uncertainty, like maximax, maximin, and expected monetary value. Sensitivity analysis is introduced as a way to examine how decisions may change with different input data. The document uses examples and tables to illustrate key concepts in decision analysis.
This document outlines concepts related to decision making under uncertainty and risk. It discusses six steps to decision making, including defining the problem, listing alternatives and outcomes, and identifying payoffs. It then covers various decision making models for uncertainty, like maximax, maximin, and expected monetary value. Sensitivity analysis is introduced as a way to examine how decisions may change with different input data. The document uses examples and tables to illustrate key concepts in decision analysis.
This document outlines concepts related to decision making under uncertainty and risk. It discusses six steps for decision making, including defining the problem, listing alternatives and outcomes, and identifying payoffs. It then covers various models for decision making under uncertainty, like maximax, maximin, and expected monetary value. Sensitivity analysis is presented as a way to examine how decisions may change with different input data or probabilities. The document uses examples like Thompson Lumber Company to illustrate concepts step-by-step.
This document outlines a chapter about location strategies. It discusses factors that influence location decisions for companies such as labor costs, proximity to markets and suppliers, and currency exchange rates. It also describes several methods for evaluating location alternatives, including the factor-rating method, locational break-even analysis, and transportation modeling. Additionally, it examines location strategies for different types of industries like services/retail versus goods production. Key considerations for selecting sites are summarized for different business models.
The document outlines various location strategy concepts including factors that influence location decisions, methods for evaluating location alternatives, and the importance of strategic location decisions. Specifically, it discusses:
1) Key factors that affect location decisions include labor costs, exchange rates, proximity to markets/suppliers, and clustering near competitors.
2) Common location evaluation methods are the factor-rating method, locational break-even analysis, and center-of-gravity method which aim to quantitatively assess alternative locations.
3) Strategic location decisions have long-term impacts and greatly influence a firm's costs, so these decisions require careful analysis of multiple location-specific factors.
The document provides examples of how to formulate linear programming models to represent managerial decision-making problems involving allocating limited resources. It discusses the key components of a linear programming problem including objectives, constraints, decision variables, and mathematical expressions. Five examples are given of problems from different industries and the steps taken to formulate each as a linear programming model are outlined.
The document discusses various factors and methods related to facility location decisions. It covers topics such as the importance of location strategy, factors that affect location like costs, proximity to markets/suppliers, and political/cultural issues. Methods discussed include factor-rating, break-even analysis, center-of-gravity, and transportation modeling to determine optimal locations. Additionally, it addresses considerations for different types of facility layouts including retail, warehouse, process-oriented and others.
The document discusses operations research (OR) and its role in managerial decision making. It defines OR and lists some common OR techniques. It discusses the five generations of OR and how it has evolved over time to incorporate more qualitative methods and address broader problems. It provides examples of linear programming problems and applications of linear programming in areas like production, marketing, finance, and personnel management. It also lists some limitations of linear programming.
CS225 Fundamentals of Computer ScienceCourse SyllabusFall 2013Dr.docxmydrynan
This document is a course syllabus for a programming in C++ project assignment. It provides instructions and test cases for a program to generate a loan payment report. Students are asked to write a program to calculate interest, debt paid, and loan balance over monthly payments until the loan is paid off. The program must output these calculations in a table with 5 columns for each month. It must also output the total interest paid at the end. Three test cases with different loan amounts and monthly payments are provided to test the program on.
Dr Changer Velu outlines the importance of business model innovation following the adoption of new digital technologies as the basis for productivity improvements.
Optimizing profit with linear programming modelAmit Deshmukh
This document presents a case study on optimizing profit for Golden Plastic Industry Limited in Nigeria using a linear programming model. It formulates a mathematical model to determine the optimal product mix and quantities of 8 plastic pipe products to maximize total profit, subject to constraints of available resources. The model was solved and provided the quantities of each product that should be produced to achieve maximum profit within the resource limits. The study concluded that applying linear programming techniques can help manufacturing industries like Golden Plastic Industry Limited optimize their decision making and profitability.
This document discusses operations research (OR) and its role in managerial decision making. It provides definitions of OR, describes common OR techniques like linear programming, and gives examples of OR applications in areas like production, marketing, finance, and personnel management. It also discusses the evolution of OR over multiple generations and limitations of linear programming as an OR technique. Several examples of linear programming problems in manufacturing and logistics settings are presented to illustrate the use of LP models.
This document outlines a course on mathematics for economists. It introduces linear programming techniques for solving constrained and unconstrained optimization problems. The course objectives are to learn how to formulate and solve linear programming problems using graphical and simplex methods, test for linear dependence and convexity/concavity, and solve nonlinear programming problems, differential equations, and difference equations as applied to economics. Sample problems are provided to demonstrate how to formulate linear programming problems in the standard way, specifying the objective, decision variables, constraints, and developing the objective function.
The document discusses factors that organizations consider when making location decisions. Location decisions are important as they impact investment requirements, operating costs, revenue, and competitive advantage. An organization may need to make a location decision due to market expansion, growth in demand that cannot be met at existing locations, or depletion of basic inputs. Location options include expanding existing facilities, adding new facilities, moving locations, or doing nothing. The general procedure for making a location decision involves identifying objectives, determining location factors, identifying general regions and community alternatives, and evaluating alternatives.
This study aimed to measure the service attitudes of Pakistani banks by assessing employee and customer perceptions. A survey was conducted of 275 employees and customers from 5 randomly selected banks using a SERVQUAL questionnaire. The findings showed significant differences between how employees and customers rated the banks' performance on tangible, reliability, responsiveness, assurance and empathy dimensions. Most customers were male aged 21-30, while most employees were also male aged 21-30 with 0-5 years of experience. The study recommends banks focus on customer-oriented service quality initiatives and employee training to reduce gaps in perceptions.
The document provides an introduction to financial statements and auditing. It discusses the purpose of financial statements, which is to provide useful information to users for economic decision making. It outlines the main users of financial statements and their interests. It also explains the need for auditing. Auditing verifies that financial statements are true and fair, and complies with reporting standards. It ensures the principal, or shareholders, have reliable information from the directors about the company's financial position and performance.
The document provides an overview of key aspects of Pakistan's Environmental Protection Act of 1997, including:
1) It establishes the Pakistan Environmental Protection Council and Agency to coordinate environmental protection, set standards, and enforce the law.
2) It prohibits various polluting activities that exceed standards, such as certain emissions, discharges, construction without approval, and hazardous waste imports.
3) It establishes penalties for violations, allows complaints to Environmental Tribunals, and provides for compensation. Tribunals and magistrates are empowered to enforce the law.
4) The Act aims to protect the environment, prevent pollution, and promote sustainable development in Pakistan through its regulatory and enforcement mechanisms.
This document provides an overview of Bank Alfalah Limited, including its vision, mission, history, leadership structure, and key business functions and practices. Some of the key points summarized:
- Bank Alfalah's vision is to be the premier financial services provider locally and internationally, offering a complete range of services under one roof. Its mission is to develop innovative products and deliver quality customer service.
- The bank was incorporated in 1997 and currently has over 100 branches across Pakistan. It was privatized in 1997 and is now majority owned by investors from Abu Dhabi.
- The bank engages in strategic planning, organizing its departments and teams, monitoring its internal and external environment, and focuses on profit
National Bank of Pakistan is the largest commercial bank in Pakistan with over 1,254 branches. It has a vision to be recognized as a leader with the highest standards of service quality and social responsibility. The bank has various departments including commercial and retail banking, treasury management, and audit and inspection. It offers numerous services and products to individuals, corporations, and the government. While NBP has strengths like its large size and market share, it also faces weaknesses such as low internal controls, outdated technology, and an unsatisfactory corporate culture that need further improvement.
Consumer law in Pakistan regulates business transactions and protects consumers. It covers areas like product quality, advertising, and unfair business practices. The key document discusses Pakistan's Consumer Protection Act, which established the Consumer Rights Commission to coordinate enforcement of consumer protections. It also summarizes provisions from acts governing contracts, environmental protection, sale of goods, motor vehicles, and compensation for accidents. The acts work to define legal agreements, protect the environment, regulate commerce, and provide redress for harm.
The Punjab Finance Minister presented an Rs 871.95 billion budget for the next fiscal year, with major allocations for education, health, agriculture, and infrastructure. The budget allocates Rs 290 billion for the Annual Development Program, including Rs 240 billion for development projects and Rs 50 billion in development grants. Significant portions of the budget are dedicated to improving education infrastructure and programs, expanding healthcare services and funding for immunization and disease prevention, supporting the agricultural sector through new irrigation projects, and increasing the salaries of government employees and minimum wage.
The Punjab Finance Minister presented an 871.95 billion rupee budget for the next fiscal year with focus on education, health, agriculture and communication. The budget allocates 290 billion rupees for development programs including 240 billion for the Annual Development Program and 50 billion for development grants. Key allocations include funds for education programs, new hospitals and health initiatives, agriculture and energy projects, and a small increase to government employee salaries.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.