This document provides an overview of linear programming models and concepts. It begins with definitions of linear programming and its key components: decision variables, objective function, and constraints. Several examples are then presented to illustrate linear programming problems and their graphical and Excel-based solutions. Sensitivity analysis concepts like shadow prices and ranges of optimality/feasibility are explained. The document concludes with examples of alternative optimal solutions and infeasible/unbounded models.
This document provides an introduction to linear programming models. It begins by defining the components of a linear programming model, including decision variables, objective function, and constraints. It then discusses the importance of linear programming in various applications like manufacturing and finance. The document presents the assumptions of linear programming models and provides an example problem about production planning at Galaxy Industries. It formulates this problem as a linear programming model to maximize profit. Finally, it discusses solving the model graphically and with Excel Solver, as well as analyzing the optimal solution through sensitivity analysis and post-optimality changes.
This document provides an introduction to linear programming models. It discusses key components of linear models including decision variables, objective functions, and constraints. It then presents a prototype example of using linear programming to optimize production levels at Galaxy Industries. The optimal solution is found using Excel Solver and sensitivity analysis is performed to analyze how changes impact the optimal solution. Various scenarios where models may not have a unique optimal solution are also discussed.
Linear programming is a mathematical modeling technique used to determine optimal resource allocation to achieve objectives. It involves converting a problem into a linear mathematical model with decision variables, constraints, and an objective function. The optimal solution is found by systematically increasing the objective function value until infeasibility is reached. For example, a linear programming model was used to determine the optimal production mix and levels of two drug combinations to maximize profit given resource constraints. The optimal solution was found to be 320 dozen of drug X1 and 360 dozen of drug X2, utilizing all available resources and achieving $4,360 in weekly profit.
This document discusses solving optimization problems using linear programming. It begins with an introduction to linear programming, describing how it seeks to maximize or minimize an objective function subject to constraints. The document then presents an example problem from Galaxy Industries that produces trucks and cars, with the goal of maximizing weekly profit. The problem is formulated as a linear program with decision variables, objective function, and constraints. Finally, the document covers solving the problem graphically and performing sensitivity analysis on the objective function coefficients and right-hand side constraint values.
The document outlines the policies, course objectives, and schedule for CHE 536 Engineering Optimization taught by Prof. Shi-Shang Jang at National Tsing Hua University. The class will meet every Thursday from 2-5pm in room 221 of the Chemical Engineering building. The course aims to teach problem formulation, numerical optimization algorithms, and their applications. Homework is due biweekly and grades are based on homework, a midterm exam, and a term project. Topics include single variable optimization, unconstrained optimization, linear programming, and nonlinear programming.
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine the optimal solution to decision problems with limited resources. Linear programming is then introduced as a type of mathematical modeling where the objective function and constraints are linear. The key aspects of a linear programming problem are defined as the decision variables, objective function to maximize or minimize, and constraints. Graphical solutions and examples of linear programming problems are also provided.
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 provides an introduction to operations research. It defines operations research as seeking to improve problem solutions through analysis and mathematical models. It gives examples of common optimization problems involving transportation networks, resource allocation, and facility layout. The document classifies optimization problems as either unconstrained or constrained. It explains constrained problems involve an objective function and constraints. Finally, it outlines common solution methods for constrained optimization problems like linear programming.
This document provides an introduction to linear programming models. It begins by defining the components of a linear programming model, including decision variables, objective function, and constraints. It then discusses the importance of linear programming in various applications like manufacturing and finance. The document presents the assumptions of linear programming models and provides an example problem about production planning at Galaxy Industries. It formulates this problem as a linear programming model to maximize profit. Finally, it discusses solving the model graphically and with Excel Solver, as well as analyzing the optimal solution through sensitivity analysis and post-optimality changes.
This document provides an introduction to linear programming models. It discusses key components of linear models including decision variables, objective functions, and constraints. It then presents a prototype example of using linear programming to optimize production levels at Galaxy Industries. The optimal solution is found using Excel Solver and sensitivity analysis is performed to analyze how changes impact the optimal solution. Various scenarios where models may not have a unique optimal solution are also discussed.
Linear programming is a mathematical modeling technique used to determine optimal resource allocation to achieve objectives. It involves converting a problem into a linear mathematical model with decision variables, constraints, and an objective function. The optimal solution is found by systematically increasing the objective function value until infeasibility is reached. For example, a linear programming model was used to determine the optimal production mix and levels of two drug combinations to maximize profit given resource constraints. The optimal solution was found to be 320 dozen of drug X1 and 360 dozen of drug X2, utilizing all available resources and achieving $4,360 in weekly profit.
This document discusses solving optimization problems using linear programming. It begins with an introduction to linear programming, describing how it seeks to maximize or minimize an objective function subject to constraints. The document then presents an example problem from Galaxy Industries that produces trucks and cars, with the goal of maximizing weekly profit. The problem is formulated as a linear program with decision variables, objective function, and constraints. Finally, the document covers solving the problem graphically and performing sensitivity analysis on the objective function coefficients and right-hand side constraint values.
The document outlines the policies, course objectives, and schedule for CHE 536 Engineering Optimization taught by Prof. Shi-Shang Jang at National Tsing Hua University. The class will meet every Thursday from 2-5pm in room 221 of the Chemical Engineering building. The course aims to teach problem formulation, numerical optimization algorithms, and their applications. Homework is due biweekly and grades are based on homework, a midterm exam, and a term project. Topics include single variable optimization, unconstrained optimization, linear programming, and nonlinear programming.
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine the optimal solution to decision problems with limited resources. Linear programming is then introduced as a type of mathematical modeling where the objective function and constraints are linear. The key aspects of a linear programming problem are defined as the decision variables, objective function to maximize or minimize, and constraints. Graphical solutions and examples of linear programming problems are also provided.
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 provides an introduction to operations research. It defines operations research as seeking to improve problem solutions through analysis and mathematical models. It gives examples of common optimization problems involving transportation networks, resource allocation, and facility layout. The document classifies optimization problems as either unconstrained or constrained. It explains constrained problems involve an objective function and constraints. Finally, it outlines common solution methods for constrained optimization problems like linear programming.
The document discusses linear programming (LP) and provides examples of solving LP problems graphically. It describes the steps to formulate an LP model which are: 1) study the problem, 2) formulate the objective function, 3) formulate the constraints, and 4) add non-negativity restrictions. It then provides an example LP model for a company that manufactures toys to maximize profit under resource constraints. The solution procedure involves graphing the constraints to determine the feasible region, then moving an objective function line through the region to find the optimal solution point where two constraints intersect.
1. The document discusses quantitative analysis and decision making methods used in management. It describes modeling approaches such as linear programming that involve representing real-world problems mathematically.
2. Key steps in problem solving and decision making are identified, including defining the problem, determining alternatives, evaluating alternatives, and choosing a solution.
3. Quantitative models allow managers to systematically evaluate alternatives using factors like costs, revenues, profits, and constraints.
The document summarizes key concepts regarding linear programming problems. It discusses:
1. Linear programming problems aim to optimize an objective function subject to constraints. They can model many practical operations research problems.
2. The document provides an example problem of determining production levels to maximize profit. It demonstrates formulating the problem as a mathematical model and solving it graphically and with the simplex method.
3. The simplex method solves linear programming problems by examining vertex points of the feasible solution space. It involves setting up the problem in standard form and using minimum ratio and pivot element calculations to systematically search for an optimal solution.
The document discusses sensitivity analysis and the simplex method for solving linear programming problems. It provides the following key points:
1. Sensitivity analysis helps determine how sensitive the optimal solution is to changes in the coefficients and constraints of a linear programming model.
2. The simplex method is used to solve linear programming problems by moving from one basic feasible solution to an adjacent feasible solution to improve the objective function value.
3. Shadow prices and reduced costs can provide insights into how changes to the right-hand sides of constraints and objective function coefficients would impact the optimal solution.
This document provides an introduction to integer programming, including:
- Integer programming models involve decision variables that must take on integer values, unlike linear programming which allows fractional values. Solving integer programs is more difficult.
- There are three types of integer programming models: pure integer, 0-1 integer, and mixed integer.
- Integer programming is used when non-integer solutions are impractical, like number of machines. Rounding solutions can affect costs significantly.
- Several examples of integer programming models are provided for problems like machine selection, facility location, and investment allocation.
- Two common solution methods are described: branch-and-bound and cutting-plane. Branch-and-bound systematically
This document provides an introduction to linear programming. It defines linear programming as a mathematical modeling technique used to optimize resource allocation. The key requirements are a well-defined objective function, constraints on available resources, and alternative courses of action represented by decision variables. The assumptions of linear programming include proportionality, additivity, continuity, certainty, and finite choices. Formulating a problem as a linear program involves defining the objective function and constraints mathematically. Graphical and analytical solutions can then be used to find the optimal solution. Linear programming has many applications in fields like industrial production, transportation, and facility location.
This chapter discusses sensitivity analysis in linear programming, which examines how changes to the objective function coefficients or right-hand side values affect the optimal solution. It introduces the concepts of ranges of optimality and feasibility, which define the ranges of coefficient or right-hand side values where the optimal solution remains the same. The chapter also discusses shadow prices, which measure the impact of changing a right-hand side value, and provides an example to illustrate these concepts.
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine optimal solutions to decision problems with limited resources. Linear programming problems have decision variables, an objective function to maximize or minimize, and constraints. An optimal solution is a feasible solution that gives the most favorable objective function value. Graphical methods can find the optimal solution by determining the feasible region and optimal point.
The document discusses linear programming, which is a mathematical modeling technique used to allocate limited resources optimally. It provides examples of linear programming problems and their formulation. Key aspects covered include defining decision variables and constraints, developing the objective function, and interpreting feasible and optimal solutions. Graphical and algebraic solution methods like the simplex method are also introduced.
This document discusses linear programming techniques for managerial decision making. Linear programming can determine the optimal allocation of scarce resources among competing demands. It consists of linear objectives and constraints where variables have a proportionate relationship. Essential elements of a linear programming model include limited resources, objectives to maximize or minimize, linear relationships between variables, homogeneity of products/resources, and divisibility of resources/products. The linear programming problem is formulated by defining variables and constraints, with the objective of optimizing a linear function subject to the constraints. It is then solved using graphical or simplex methods through an iterative process to find the optimal solution.
The document discusses linear programming and its key concepts. It begins by defining linear programming as using a mathematical model to allocate scarce resources to maximize profit or minimize cost. It then provides the steps to solve linear programming problems: [1] identify the problem as solvable by LP, [2] formulate a mathematical model, [3] solve the model, and [4] implement the solution. The document also discusses modeling techniques like defining decision variables, objective functions, and constraints. It provides examples of LP formulations and solutions using both graphical and algebraic methods. Finally, it discusses special issues that can arise like infeasible, unbounded, and redundant solutions or the existence of multiple optimal solutions.
The document discusses linear programming and its key concepts. It begins by defining linear programming as using a mathematical model to allocate scarce resources to maximize profit or minimize cost. It then provides the steps to solve linear programming problems: [1] identify the problem as solvable by LP, [2] formulate a mathematical model, [3] solve the model, and [4] implement the solution. The document also discusses modeling techniques like defining decision variables, objective functions, and constraints. It provides examples of LP formulations and solutions using both graphical and algebraic methods. Finally, it discusses special issues that can arise like infeasible, unbounded, and redundant solutions or the existence of multiple optimal solutions.
This document provides an introduction to operations research. It defines operations research as a scientific approach to decision making that seeks to determine how best to operate a system under conditions of allocating scarce resources. The document discusses the origin and applications of operations research. It also outlines some common operations research techniques like linear programming, transportation problems, assignment problems, and PERT-CPM. Finally, it provides definitions of operations research from different authors and discusses the scope and methodology of operations research.
This document discusses optimization problem formulation. It begins by introducing optimization algorithms and their use in computer-aided design. It then discusses the key components of formulating an optimization problem: identifying design variables and constraints, defining the objective function, and setting variable bounds. Two examples are provided to illustrate this process for optimizing a truss structure design and car suspension design. The document provides the details necessary to mathematically formulate engineering optimization problems.
The document discusses linear programming and its applications, providing an introduction to linear programming, its components and assumptions, and common applications such as product mix problems, diet problems, and blending problems. Linear programming is presented as a mathematical technique to help managers make optimal resource allocation decisions under certain constraints by formulating problems as linear models to maximize or minimize objectives. Examples are provided to illustrate how to set up linear programming models for different situations.
The document discusses linear programming and its applications, providing an introduction to linear programming, its components and assumptions, and common applications such as product mix problems, diet problems, and blending problems. Linear programming is presented as a mathematical technique to help managers make optimal resource allocation decisions under certain constraints by formulating problems as linear models to maximize or minimize objectives. Examples are provided to illustrate how to set up linear programming models for different situations.
The document provides information about linear programming, including:
- Linear programming is a technique to optimize allocation of scarce resources among competing demands. It involves determining variables, constraints, and an objective function.
- The linear programming model consists of linear objectives and constraints, where variables have a proportionate relationship (e.g. increasing labor increases output proportionately).
- Essential elements of a linear programming model include limited resources, an objective to maximize or minimize, linear relationships between variables, identical resources/products, and divisible resources.
- Linear programming problems can be solved graphically by plotting constraints and objective function to find the optimal point, or algebraically using the simplex method through iterative tables.
This document discusses real-time optimization (RTO) in process control systems. RTO involves regularly recalculating the optimal set points for process variables, such as every hour or day, by solving a constrained steady-state optimization problem. The optimization problem involves both an economic model to maximize objectives like profit or minimize costs, and a process model with constraints. Common types of optimization problems in RTO include optimizing operating conditions like distillation column parameters, feedstock allocation, and scheduling of activities. The document outlines the basic requirements and formulation of RTO optimization problems.
Real-time optimization (RTO) involves regularly recalculating optimal set points, such as every hour or day, by solving a constrained steady-state optimization problem. The optimization problem involves an economic model with an objective function to maximize profits or minimize costs, as well as a process model and constraints. Common types of optimization variables include operating conditions like distillation column reflux ratios, and scheduling of activities. The chapter discusses formulation and solution methods for RTO problems, including linear programming and other approaches.
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
The document discusses linear programming (LP) and provides examples of solving LP problems graphically. It describes the steps to formulate an LP model which are: 1) study the problem, 2) formulate the objective function, 3) formulate the constraints, and 4) add non-negativity restrictions. It then provides an example LP model for a company that manufactures toys to maximize profit under resource constraints. The solution procedure involves graphing the constraints to determine the feasible region, then moving an objective function line through the region to find the optimal solution point where two constraints intersect.
1. The document discusses quantitative analysis and decision making methods used in management. It describes modeling approaches such as linear programming that involve representing real-world problems mathematically.
2. Key steps in problem solving and decision making are identified, including defining the problem, determining alternatives, evaluating alternatives, and choosing a solution.
3. Quantitative models allow managers to systematically evaluate alternatives using factors like costs, revenues, profits, and constraints.
The document summarizes key concepts regarding linear programming problems. It discusses:
1. Linear programming problems aim to optimize an objective function subject to constraints. They can model many practical operations research problems.
2. The document provides an example problem of determining production levels to maximize profit. It demonstrates formulating the problem as a mathematical model and solving it graphically and with the simplex method.
3. The simplex method solves linear programming problems by examining vertex points of the feasible solution space. It involves setting up the problem in standard form and using minimum ratio and pivot element calculations to systematically search for an optimal solution.
The document discusses sensitivity analysis and the simplex method for solving linear programming problems. It provides the following key points:
1. Sensitivity analysis helps determine how sensitive the optimal solution is to changes in the coefficients and constraints of a linear programming model.
2. The simplex method is used to solve linear programming problems by moving from one basic feasible solution to an adjacent feasible solution to improve the objective function value.
3. Shadow prices and reduced costs can provide insights into how changes to the right-hand sides of constraints and objective function coefficients would impact the optimal solution.
This document provides an introduction to integer programming, including:
- Integer programming models involve decision variables that must take on integer values, unlike linear programming which allows fractional values. Solving integer programs is more difficult.
- There are three types of integer programming models: pure integer, 0-1 integer, and mixed integer.
- Integer programming is used when non-integer solutions are impractical, like number of machines. Rounding solutions can affect costs significantly.
- Several examples of integer programming models are provided for problems like machine selection, facility location, and investment allocation.
- Two common solution methods are described: branch-and-bound and cutting-plane. Branch-and-bound systematically
This document provides an introduction to linear programming. It defines linear programming as a mathematical modeling technique used to optimize resource allocation. The key requirements are a well-defined objective function, constraints on available resources, and alternative courses of action represented by decision variables. The assumptions of linear programming include proportionality, additivity, continuity, certainty, and finite choices. Formulating a problem as a linear program involves defining the objective function and constraints mathematically. Graphical and analytical solutions can then be used to find the optimal solution. Linear programming has many applications in fields like industrial production, transportation, and facility location.
This chapter discusses sensitivity analysis in linear programming, which examines how changes to the objective function coefficients or right-hand side values affect the optimal solution. It introduces the concepts of ranges of optimality and feasibility, which define the ranges of coefficient or right-hand side values where the optimal solution remains the same. The chapter also discusses shadow prices, which measure the impact of changing a right-hand side value, and provides an example to illustrate these concepts.
The document discusses operations research and linear programming. It defines operations research as a scientific approach to determine optimal solutions to decision problems with limited resources. Linear programming problems have decision variables, an objective function to maximize or minimize, and constraints. An optimal solution is a feasible solution that gives the most favorable objective function value. Graphical methods can find the optimal solution by determining the feasible region and optimal point.
The document discusses linear programming, which is a mathematical modeling technique used to allocate limited resources optimally. It provides examples of linear programming problems and their formulation. Key aspects covered include defining decision variables and constraints, developing the objective function, and interpreting feasible and optimal solutions. Graphical and algebraic solution methods like the simplex method are also introduced.
This document discusses linear programming techniques for managerial decision making. Linear programming can determine the optimal allocation of scarce resources among competing demands. It consists of linear objectives and constraints where variables have a proportionate relationship. Essential elements of a linear programming model include limited resources, objectives to maximize or minimize, linear relationships between variables, homogeneity of products/resources, and divisibility of resources/products. The linear programming problem is formulated by defining variables and constraints, with the objective of optimizing a linear function subject to the constraints. It is then solved using graphical or simplex methods through an iterative process to find the optimal solution.
The document discusses linear programming and its key concepts. It begins by defining linear programming as using a mathematical model to allocate scarce resources to maximize profit or minimize cost. It then provides the steps to solve linear programming problems: [1] identify the problem as solvable by LP, [2] formulate a mathematical model, [3] solve the model, and [4] implement the solution. The document also discusses modeling techniques like defining decision variables, objective functions, and constraints. It provides examples of LP formulations and solutions using both graphical and algebraic methods. Finally, it discusses special issues that can arise like infeasible, unbounded, and redundant solutions or the existence of multiple optimal solutions.
The document discusses linear programming and its key concepts. It begins by defining linear programming as using a mathematical model to allocate scarce resources to maximize profit or minimize cost. It then provides the steps to solve linear programming problems: [1] identify the problem as solvable by LP, [2] formulate a mathematical model, [3] solve the model, and [4] implement the solution. The document also discusses modeling techniques like defining decision variables, objective functions, and constraints. It provides examples of LP formulations and solutions using both graphical and algebraic methods. Finally, it discusses special issues that can arise like infeasible, unbounded, and redundant solutions or the existence of multiple optimal solutions.
This document provides an introduction to operations research. It defines operations research as a scientific approach to decision making that seeks to determine how best to operate a system under conditions of allocating scarce resources. The document discusses the origin and applications of operations research. It also outlines some common operations research techniques like linear programming, transportation problems, assignment problems, and PERT-CPM. Finally, it provides definitions of operations research from different authors and discusses the scope and methodology of operations research.
This document discusses optimization problem formulation. It begins by introducing optimization algorithms and their use in computer-aided design. It then discusses the key components of formulating an optimization problem: identifying design variables and constraints, defining the objective function, and setting variable bounds. Two examples are provided to illustrate this process for optimizing a truss structure design and car suspension design. The document provides the details necessary to mathematically formulate engineering optimization problems.
The document discusses linear programming and its applications, providing an introduction to linear programming, its components and assumptions, and common applications such as product mix problems, diet problems, and blending problems. Linear programming is presented as a mathematical technique to help managers make optimal resource allocation decisions under certain constraints by formulating problems as linear models to maximize or minimize objectives. Examples are provided to illustrate how to set up linear programming models for different situations.
The document discusses linear programming and its applications, providing an introduction to linear programming, its components and assumptions, and common applications such as product mix problems, diet problems, and blending problems. Linear programming is presented as a mathematical technique to help managers make optimal resource allocation decisions under certain constraints by formulating problems as linear models to maximize or minimize objectives. Examples are provided to illustrate how to set up linear programming models for different situations.
The document provides information about linear programming, including:
- Linear programming is a technique to optimize allocation of scarce resources among competing demands. It involves determining variables, constraints, and an objective function.
- The linear programming model consists of linear objectives and constraints, where variables have a proportionate relationship (e.g. increasing labor increases output proportionately).
- Essential elements of a linear programming model include limited resources, an objective to maximize or minimize, linear relationships between variables, identical resources/products, and divisible resources.
- Linear programming problems can be solved graphically by plotting constraints and objective function to find the optimal point, or algebraically using the simplex method through iterative tables.
This document discusses real-time optimization (RTO) in process control systems. RTO involves regularly recalculating the optimal set points for process variables, such as every hour or day, by solving a constrained steady-state optimization problem. The optimization problem involves both an economic model to maximize objectives like profit or minimize costs, and a process model with constraints. Common types of optimization problems in RTO include optimizing operating conditions like distillation column parameters, feedstock allocation, and scheduling of activities. The document outlines the basic requirements and formulation of RTO optimization problems.
Real-time optimization (RTO) involves regularly recalculating optimal set points, such as every hour or day, by solving a constrained steady-state optimization problem. The optimization problem involves an economic model with an objective function to maximize profits or minimize costs, as well as a process model and constraints. Common types of optimization variables include operating conditions like distillation column reflux ratios, and scheduling of activities. The chapter discusses formulation and solution methods for RTO problems, including linear programming and other approaches.
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
2. 2
• A Linear Programming model seeks to maximize or
minimize a linear function, subject to a set of linear
constraints.
• The linear model consists of the following
components:
– A set of decision variables.
– An objective function.
– A set of constraints.
2.1 Introduction to Linear Programming
3. 3
Introduction to Linear Programming
• The Importance of Linear Programming
– Many real world problems lend themselves to linear
programming modeling.
– Many real world problems can be approximated by linear models.
– There are well-known successful applications in:
• Manufacturing
• Marketing
• Finance (investment)
• Advertising
• Agriculture
4. 4
• The Importance of Linear Programming
– There are efficient solution techniques that solve linear
programming models.
– The output generated from linear programming packages
provides useful “what if” analysis.
Introduction to Linear Programming
5. 5
Introduction to Linear Programming
• Assumptions of the linear programming model
– The parameter values are known with certainty.
– The objective function and constraints exhibit
constant returns to scale.
– There are no interactions between the decision
variables (the additivity assumption).
– The Continuity assumption: Variables can take on
any value within a given feasible range.
6. 6
The Galaxy Industries Production Problem –
A Prototype Example
• Galaxy manufactures two toy doll models:
– Space Ray.
– Zapper.
• Resources are limited to
– 1000 pounds of special plastic.
– 40 hours of production time per week.
7. 7
• Marketing requirement
– Total production cannot exceed 700 dozens.
– Number of dozens of Space Rays cannot exceed
number of dozens of Zappers by more than 350.
• Technological input
– Space Rays requires 2 pounds of plastic and
3 minutes of labor per dozen.
– Zappers requires 1 pound of plastic and
4 minutes of labor per dozen.
The Galaxy Industries Production Problem –
A Prototype Example
8. 8
• The current production plan calls for:
– Producing as much as possible of the more profitable product,
Space Ray ($8 profit per dozen).
– Use resources left over to produce Zappers ($5 profit
per dozen), while remaining within the marketing guidelines.
• The current production plan consists of:
Space Rays = 450 dozen
Zapper = 100 dozen
Profit = $4100 per week
The Galaxy Industries Production Problem –
A Prototype Example
8(450) + 5(100)
10. 10
A linear programming model
can provide an insight and an
intelligent solution to this problem.
11. 11
• Decisions variables:
– X1 = Weekly production level of Space Rays (in dozens)
– X2 = Weekly production level of Zappers (in dozens).
• Objective Function:
– Weekly profit, to be maximized
The Galaxy Linear Programming Model
12. 12
Max 8X1 + 5X2 (Weekly profit)
subject to
2X1 + 1X2 1000 (Plastic)
3X1 + 4X2 2400 (Production Time)
X1 + X2 700 (Total production)
X1 - X2 350 (Mix)
Xj> = 0, j = 1,2 (Nonnegativity)
The Galaxy Linear Programming Model
13. 13
2.3 The Graphical Analysis of Linear
Programming
The set of all points that satisfy all the
constraints of the model is called
a
FEASIBLE REGION
14. 14
Using a graphical presentation
we can represent all the constraints,
the objective function, and the three
types of feasible points.
19. 19
The search for an optimal solution
Start at some arbitrary profit, say profit = $2,000...
Then increase the profit, if possible...
...and continue until it becomes infeasible
Profit =$4360
500
700
1000
500
X2
X1
20. 20
Summary of the optimal solution
Space Rays = 320 dozen
Zappers = 360 dozen
Profit = $4360
– This solution utilizes all the plastic and all the production hours.
– Total production is only 680 (not 700).
– Space Rays production exceeds Zappers production by only 40
dozens.
21. 21
– If a linear programming problem has an optimal
solution, an extreme point is optimal.
Extreme points and optimal solutions
22. 22
• For multiple optimal solutions to exist, the objective
function must be parallel to one of the constraints
Multiple optimal solutions
•Any weighted average of
optimal solutions is also an
optimal solution.
23. 23
2.4 The Role of Sensitivity Analysis
of the Optimal Solution
• Is the optimal solution sensitive to changes in
input parameters?
• Possible reasons for asking this question:
– Parameter values used were only best estimates.
– Dynamic environment may cause changes.
– “What-if” analysis may provide economical and
operational information.
24. 24
• Range of Optimality
– The optimal solution will remain unchanged as long as
• An objective function coefficient lies within its range of
optimality
• There are no changes in any other input parameters.
– The value of the objective function will change if the
coefficient multiplies a variable whose value is nonzero.
Sensitivity Analysis of
Objective Function Coefficients.
27. 27
• Reduced cost
Assuming there are no other changes to the input parameters,
the reduced cost for a variable Xj that has a value of “0” at the
optimal solution is:
– The negative of the objective coefficient increase of the variable
Xj (-DCj) necessary for the variable to be positive in the optimal
solution
– Alternatively, it is the change in the objective value per unit
increase of Xj.
• Complementary slackness
At the optimal solution, either the value of a variable is zero, or
its reduced cost is 0.
28. 28
• In sensitivity analysis of right-hand sides of constraints
we are interested in the following questions:
– Keeping all other factors the same, how much would the
optimal value of the objective function (for example, the profit)
change if the right-hand side of a constraint changed by one
unit?
– For how many additional or fewer units will this per unit
change be valid?
Sensitivity Analysis of
Right-Hand Side Values
29. 29
• Any change to the right hand side of a binding
constraint will change the optimal solution.
• Any change to the right-hand side of a non-
binding constraint that is less than its slack or
surplus, will cause no change in the optimal
solution.
Sensitivity Analysis of
Right-Hand Side Values
30. 30
Shadow Prices
• Assuming there are no other changes to the
input parameters, the change to the objective
function value per unit increase to a right hand
side of a constraint is called the “Shadow Price”
31. 31
1000
500
X2
X1
500
When more plastic becomes available (the
plastic constraint is relaxed), the right hand
side of the plastic constraint increases.
Production time
constraint
Maximum profit = $4360
Maximum profit = $4363.4
Shadow price =
4363.40 – 4360.00 = 3.40
Shadow Price – graphical demonstration
The Plastic
constraint
32. 32
Range of Feasibility
• Assuming there are no other changes to the
input parameters, the range of feasibility is
– The range of values for a right hand side of a constraint, in
which the shadow prices for the constraints remain
unchanged.
– In the range of feasibility the objective function value changes
as follows:
Change in objective value =
[Shadow price][Change in the right hand side value]
33. 33
Range of Feasibility
1000
500
X2
X1
500
Increasing the amount of
plastic is only effective until a
new constraint becomes active.
The Plastic
constraint
This is an infeasible solution
Production time
constraint
Production mix
constraint
X1 + X2 700
A new active
constraint
36. 36
– Sunk costs: The shadow price is the value of an
extra unit of the resource, since the cost of the
resource is not included in the calculation of the
objective function coefficient.
– Included costs: The shadow price is the premium
value above the existing unit value for the resource,
since the cost of the resource is included in the
calculation of the objective function coefficient.
The correct interpretation of shadow prices
37. 37
Other Post - Optimality Changes
• Addition of a constraint.
• Deletion of a constraint.
• Addition of a variable.
• Deletion of a variable.
• Changes in the left - hand side coefficients.
38. 38
2.5 Using Excel Solver to Find an
Optimal Solution and Analyze Results
• To see the input screen in Excel click Galaxy.xls
• Click Solver to obtain the following dialog box.
Equal To:
By Changing cells
These cells contain
the decision variables
$B$4:$C$4
To enter constraints click…
Set Target cell $D$6
This cell contains
the value of the
objective function
$D$7:$D$10 $F$7:$F$10
All the constraints
have the same direction,
thus are included in
one “Excel constraint”.
39. 39
Using Excel Solver
• To see the input screen in Excel click Galaxy.xls
• Click Solver to obtain the following dialog box.
Equal To:
$D$7:$D$10<=$F$7:$F$10
By Changing cells
These cells contain
the decision variables
$B$4:$C$4
Set Target cell $D$6
This cell contains
the value of the
objective function
Click on ‘Options’
and check ‘Linear
Programming’ and
‘Non-negative’.
40. 40
• To see the input screen in Excel click Galaxy.xls
• Click Solver to obtain the following dialog box.
Equal To:
$D$7:$D$10<=$F$7:$F$10
By Changing cells
$B$4:$C$4
Set Target cell $D$6
Using Excel Solver
41. 41
Space Rays Zappers
Dozens 320 360
Total Limit
Profit 8 5 4360
Plastic 2 1 1000 <= 1000
Prod. Time 3 4 2400 <= 2400
Total 1 1 680 <= 700
Mix 1 -1 -40 <= 350
GALAXY INDUSTRIES
Using Excel Solver – Optimal Solution
42. 42
Space Rays Zappers
Dozens 320 360
Total Limit
Profit 8 5 4360
Plastic 2 1 1000 <= 1000
Prod. Time 3 4 2400 <= 2400
Total 1 1 680 <= 700
Mix 1 -1 -40 <= 350
GALAXY INDUSTRIES
Using Excel Solver – Optimal Solution
Solver is ready to provide
reports to analyze the
optimal solution.
43. 43
Using Excel Solver –Answer Report
Microsoft Excel 9.0 Answer Report
Worksheet: [Galaxy.xls]Galaxy
Report Created: 11/12/2001 8:02:06 PM
Target Cell (Max)
Cell Name Original Value Final Value
$D$6 Profit Total 4360 4360
Adjustable Cells
Cell Name Original Value Final Value
$B$4 Dozens Space Rays 320 320
$C$4 Dozens Zappers 360 360
Constraints
Cell Name Cell Value Formula Status Slack
$D$7 Plastic Total 1000 $D$7<=$F$7 Binding 0
$D$8 Prod. Time Total 2400 $D$8<=$F$8 Binding 0
$D$9 Total Total 680 $D$9<=$F$9 Not Binding 20
$D$10 Mix Total -40 $D$10<=$F$10 Not Binding 390
44. 44
Using Excel Solver –Sensitivity
Report
Microsoft Excel Sensitivity Report
Worksheet: [Galaxy.xls]Sheet1
Report Created:
Adjustable Cells
Final Reduced Objective Allowable Allowable
Cell Name Value Cost Coefficient Increase Decrease
$B$4 Dozens Space Rays 320 0 8 2 4.25
$C$4 Dozens Zappers 360 0 5 5.666666667 1
Constraints
Final Shadow Constraint Allowable Allowable
Cell Name Value Price R.H. Side Increase Decrease
$D$7 Plastic Total 1000 3.4 1000 100 400
$D$8 Prod. Time Total 2400 0.4 2400 100 650
$D$9 Total Total 680 0 700 1E+30 20
$D$10 Mix Total -40 0 350 1E+30 390
45. 45
• Infeasibility: Occurs when a model has no feasible
point.
• Unboundness: Occurs when the objective can become
infinitely large (max), or infinitely small (min).
• Alternate solution: Occurs when more than one point
optimizes the objective function
2.7 Models Without Unique Optimal
Solutions
50. 50
• Solver does not alert the user to the existence of
alternate optimal solutions.
• Many times alternate optimal solutions exist
when the allowable increase or allowable
decrease is equal to zero.
• In these cases, we can find alternate optimal
solutions using Solver by the following procedure:
Solver – An Alternate Optimal Solution
51. 51
• Observe that for some variable Xj the
Allowable increase = 0, or
Allowable decrease = 0.
• Add a constraint of the form:
Objective function = Current optimal value.
• If Allowable increase = 0, change the objective to
Maximize Xj
• If Allowable decrease = 0, change the objective to
Minimize Xj
Solver – An Alternate Optimal Solution
52. 52
2.8 Cost Minimization Diet Problem
• Mix two sea ration products: Texfoods, Calration.
• Minimize the total cost of the mix.
• Meet the minimum requirements of Vitamin A,
Vitamin D, and Iron.
53. 53
• Decision variables
– X1 (X2) -- The number of two-ounce portions of
Texfoods (Calration) product used in a serving.
• The Model
Minimize 0.60X1 + 0.50X2
Subject to
20X1 + 50X2 100 Vitamin A
25X1 + 25X2 100 Vitamin D
50X1 + 10X2 100 Iron
X1, X2 0
Cost per 2 oz.
% Vitamin A
provided per 2 oz.
% required
Cost Minimization Diet Problem
54. 54
10
2 4 5
Feasible Region
Vitamin “D” constraint
Vitamin “A” constraint
The Iron constraint
The Diet Problem - Graphical solution
55. 55
• Summary of the optimal solution
– Texfood product = 1.5 portions (= 3 ounces)
Calration product = 2.5 portions (= 5 ounces)
– Cost =$ 2.15 per serving.
– The minimum requirement for Vitamin D and iron are met with
no surplus.
– The mixture provides 155% of the requirement for Vitamin A.
Cost Minimization Diet Problem
56. 56
• Linear programming software packages solve
large linear models.
• Most of the software packages use the algebraic
technique called the Simplex algorithm.
• The input to any package includes:
– The objective function criterion (Max or Min).
– The type of each constraint: .
– The actual coefficients for the problem.
Computer Solution of Linear Programs With
Any Number of Decision Variables
, ,