This document discusses goal programming and its formulation and graphical solution. It describes goal programming as a method to solve linear programs with multiple objectives, with each objective viewed as a goal. Deviation variables are used to represent how much a goal is overachieved or underachieved. The goals are added to the constraint set and solved in priority sequence to minimize deviations. The document provides an example of using goal programming to solve a multi-criteria production planning problem at a computer company. It also describes using a scoring model to help a student choose between job offers by assigning weights and ratings to different selection criteria.
The document discusses integer programming and various methods to solve integer linear programming problems. It provides:
1) An overview of integer programming, defining it as an optimization problem where some or all variables must take integer values.
2) Three main types of integer programming problems - pure, mixed, and 0-1 integer problems.
3) Four methods for solving integer linear programming problems: rounding, cutting-plane, branch-and-bound, and additive algorithms.
4) A detailed example applying the cutting-plane and branch-and-bound methods to solve a sample integer programming problem.
Queuing theory is used to model waiting lines in systems where demand fluctuates. It can be used to optimize resource allocation to minimize costs associated with customer wait times and unused service capacity. The key elements of a queuing system include arrivals, a queue or waiting line, service channels, and a service discipline for determining order of service. Customers arrive according to a Poisson distribution and service times follow an exponential distribution. The goal of queuing analysis is to determine the number of service channels needed to balance wait time costs and idle resource costs.
The document discusses multiple criteria decision making (MCDM) approaches. It introduces several common MCDM methods: the weighted score method, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, and Analytic Hierarchy Process (AHP). It then provides a detailed example of how to apply the weighted score method and TOPSIS method to a problem of selecting the best car based on criteria like style, reliability, fuel economy, and cost.
The document summarizes the simplex method for solving linear programming problems when some constraints are not less than or equal constraints. It introduces the concept of adding artificial variables to "trick" the problem into having only less than or equal constraints. The document provides an example problem and walks through the simplex method steps to find the optimal solution. It also discusses special cases that can arise with the simplex method, including degeneracy where the objective function does not improve at a step.
This document provides an overview of the topics covered in Unit V: Linear Programming. It begins with an introduction to operations research and some example problems that can be modeled as linear programs. It then discusses formulations of linear programs, including the standard and slack forms. The document outlines the simplex algorithm for solving linear programs and how to convert between standard and slack forms. It provides examples demonstrating these concepts. The key topics covered are linear programming models, formulations, and the simplex algorithm.
The document discusses integer programming and various methods to solve integer linear programming problems. It provides:
1) An overview of integer programming, defining it as an optimization problem where some or all variables must take integer values.
2) Three main types of integer programming problems - pure, mixed, and 0-1 integer problems.
3) Four methods for solving integer linear programming problems: rounding, cutting-plane, branch-and-bound, and additive algorithms.
4) A detailed example applying the cutting-plane and branch-and-bound methods to solve a sample integer programming problem.
Queuing theory is used to model waiting lines in systems where demand fluctuates. It can be used to optimize resource allocation to minimize costs associated with customer wait times and unused service capacity. The key elements of a queuing system include arrivals, a queue or waiting line, service channels, and a service discipline for determining order of service. Customers arrive according to a Poisson distribution and service times follow an exponential distribution. The goal of queuing analysis is to determine the number of service channels needed to balance wait time costs and idle resource costs.
The document discusses multiple criteria decision making (MCDM) approaches. It introduces several common MCDM methods: the weighted score method, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, and Analytic Hierarchy Process (AHP). It then provides a detailed example of how to apply the weighted score method and TOPSIS method to a problem of selecting the best car based on criteria like style, reliability, fuel economy, and cost.
The document summarizes the simplex method for solving linear programming problems when some constraints are not less than or equal constraints. It introduces the concept of adding artificial variables to "trick" the problem into having only less than or equal constraints. The document provides an example problem and walks through the simplex method steps to find the optimal solution. It also discusses special cases that can arise with the simplex method, including degeneracy where the objective function does not improve at a step.
This document provides an overview of the topics covered in Unit V: Linear Programming. It begins with an introduction to operations research and some example problems that can be modeled as linear programs. It then discusses formulations of linear programs, including the standard and slack forms. The document outlines the simplex algorithm for solving linear programs and how to convert between standard and slack forms. It provides examples demonstrating these concepts. The key topics covered are linear programming models, formulations, and the simplex algorithm.
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 - 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 the Simplex method for solving linear programming problems involving profit maximization and cost minimization. It provides an overview of the concept and steps of the Simplex method, and gives an example of formulating and solving a farm linear programming model to maximize profits from two products. The document also discusses some complications that can arise in applying the Simplex method.
Dokumen tersebut membahas tentang program linier dan metode simpleks untuk menyelesaikan masalah program linier. Program linier digunakan untuk mengalokasikan sumber daya terbatas guna memaksimalkan keuntungan atau meminimalkan biaya. Metode simpleks adalah metode yang efisien untuk menyelesaikan masalah program linier dengan variabel keputusan lebih dari dua."
This tutorial discusses using Python, PuLP, and GLPK to solve linear programming problems. PuLP is a Python module that can generate LP files and interface with solvers like GLPK to solve linear problems. The tutorial covers using Python for programming, defining decision variables and constraints with PuLP, writing and solving LP models, and accessing solution results.
The document describes the steps of the dual simplex method for solving a maximization linear programming problem. It begins with ensuring all reduced costs in the simplex tableau are nonnegative before attempting the method. The key steps are: (1) check if the right-hand sides are nonnegative and stop if so, (2) pick an exiting variable if a right-hand side is negative, (3) use the minimum ratio test to select an entering variable, and (4) pivot and return to step 1. The example problem demonstrates applying these steps to solve a maximization problem using the dual simplex method.
This document summarizes a presentation on decision theory given by Bhushan Vijay Phirke to the MBA program at Rajarshi Shahu College of Engineering on April 17, 2020. It defines decision theory, outlines the six steps in the decision theory process, and describes key concepts like problem formulation, payoff tables, decision environments, and optimization criteria like optimism, pessimism, and regret. It concludes that decision theory provides a logical framework to help managers determine the most beneficial course of action when facing uncertainty.
This document discusses sequencing problems and queuing theory. It defines sequencing problems as determining the optimal order of jobs processed on machines to minimize total time. It describes different types of sequencing problems involving various numbers of jobs and machines. The document then provides algorithms for solving sequencing problems with two machines and more than two machines. It also discusses queuing theory concepts like arrival patterns, service mechanisms, queue discipline, and queuing models like M/M/1.
SUCCESS STORY: Lightning Reporting - Reducing Report Lead Time from 16 to 4.5...GoLeanSixSigma.com
UC San Diego employees apply Lean Six Sigma in education! Watch this 30 minute success story webinar to learn how Tehseen Lazzouni, a UCSD employee, is eliminating Non-Value-Add steps in reporting to improve productivity.
Mencari Nilai Modus Suatu Data Menggunakan Turbo PascalQonitha Amalia
Program tersebut merangkum proses penentuan nilai modus dari suatu data. Program melakukan proses looping untuk menghitung frekuensi masing-masing data dan menentukan nilai yang memiliki frekuensi tertinggi sebagai kandidat nilai modus, kemudian mengecek apakah nilai tersebut benar-benar merupakan modus dari data.
Sensitivity analysis in linear programming problem ( Muhammed Jiyad)Muhammed Jiyad
Topic includes : *Sensitivity Analysis *Objective function *Right Hand Side(RHS) *Sensitivity analysis using graph *Objective function coefficient *Reduced cost *Shadow pricing *Shadow pricing Microsoft Excel sensitivity report and solution.
This document discusses penalty functions, which convert constrained optimization problems into unconstrained problems by introducing penalties for violating constraints. It presents the example of minimizing f(x)=100/x subject to x≤5. Constraints are converted to quadratic penalty terms and added to the objective function. Small iterative steps and increasing the penalty severity r improves convergence to the minimum. Practice problems demonstrate implementing an iterative minimization method and applying penalty functions to additional examples.
Perencanaan agregat memainkan peran penting dalam menentukan parameter operasional seperti tingkat produksi, tenaga kerja, persediaan, dan subkontrak selama periode perencanaan agar dapat memenuhi permintaan dan memaksimalkan keuntungan rantai pasokan. Perusahaan perlu mempertimbangkan berbagai strategi seperti mengikuti permintaan, fleksibilitas waktu, atau menjaga tingkat produksi konstan sambil mengandalkan persediaan untuk men
Metode Transportasi (Masalah dalam Metode Transportasi)hazhiyah
Seringkali terjadi dalam kenyataan dimana total permintaan tidak sama dengan total penawaran. Masalah ketidakseimbangan dalam ini dalam metode transportasi dapat diatasi dengan mempergunakan persediaan dan permintaan bayangan (dummy). Selain masalah permintaan dan penawaran, dalam metode transportasi juga dikenal masalah lain yaitu degenerasi dan redudansi yang terjadi dalam penyelesaian masalah dalam metode transportasi baik itu di solusi awal atau pada solusi optimal
Teori permainan adalah pendekatan matematis untuk merumuskan situasi persaingan dan konflik antara berbagai kepentingan. Teori ini menganalisis proses pengambilan keputusan dari situasi persaingan yang melibatkan dua atau lebih pemain. Teori permainan dapat digunakan untuk menentukan strategi optimal bagi setiap pemain dalam situasi persaingan tertentu.
The document discusses the transportation problem and its solution methodology. It states that the transportation problem seeks to minimize the total shipping costs of transporting goods from multiple origins to destinations, given the unit shipping costs. It is solved in two phases - obtaining an initial feasible solution and then moving toward the optimal solution. Several methods are described for obtaining the initial feasible solution, including the Northwest Corner method, Least Cost method, and Vogel's Approximation Method. The document also discusses testing the initial solution for optimality using methods like the Stepping Stone method and Modified Distribution method.
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 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 - 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 the Simplex method for solving linear programming problems involving profit maximization and cost minimization. It provides an overview of the concept and steps of the Simplex method, and gives an example of formulating and solving a farm linear programming model to maximize profits from two products. The document also discusses some complications that can arise in applying the Simplex method.
Dokumen tersebut membahas tentang program linier dan metode simpleks untuk menyelesaikan masalah program linier. Program linier digunakan untuk mengalokasikan sumber daya terbatas guna memaksimalkan keuntungan atau meminimalkan biaya. Metode simpleks adalah metode yang efisien untuk menyelesaikan masalah program linier dengan variabel keputusan lebih dari dua."
This tutorial discusses using Python, PuLP, and GLPK to solve linear programming problems. PuLP is a Python module that can generate LP files and interface with solvers like GLPK to solve linear problems. The tutorial covers using Python for programming, defining decision variables and constraints with PuLP, writing and solving LP models, and accessing solution results.
The document describes the steps of the dual simplex method for solving a maximization linear programming problem. It begins with ensuring all reduced costs in the simplex tableau are nonnegative before attempting the method. The key steps are: (1) check if the right-hand sides are nonnegative and stop if so, (2) pick an exiting variable if a right-hand side is negative, (3) use the minimum ratio test to select an entering variable, and (4) pivot and return to step 1. The example problem demonstrates applying these steps to solve a maximization problem using the dual simplex method.
This document summarizes a presentation on decision theory given by Bhushan Vijay Phirke to the MBA program at Rajarshi Shahu College of Engineering on April 17, 2020. It defines decision theory, outlines the six steps in the decision theory process, and describes key concepts like problem formulation, payoff tables, decision environments, and optimization criteria like optimism, pessimism, and regret. It concludes that decision theory provides a logical framework to help managers determine the most beneficial course of action when facing uncertainty.
This document discusses sequencing problems and queuing theory. It defines sequencing problems as determining the optimal order of jobs processed on machines to minimize total time. It describes different types of sequencing problems involving various numbers of jobs and machines. The document then provides algorithms for solving sequencing problems with two machines and more than two machines. It also discusses queuing theory concepts like arrival patterns, service mechanisms, queue discipline, and queuing models like M/M/1.
SUCCESS STORY: Lightning Reporting - Reducing Report Lead Time from 16 to 4.5...GoLeanSixSigma.com
UC San Diego employees apply Lean Six Sigma in education! Watch this 30 minute success story webinar to learn how Tehseen Lazzouni, a UCSD employee, is eliminating Non-Value-Add steps in reporting to improve productivity.
Mencari Nilai Modus Suatu Data Menggunakan Turbo PascalQonitha Amalia
Program tersebut merangkum proses penentuan nilai modus dari suatu data. Program melakukan proses looping untuk menghitung frekuensi masing-masing data dan menentukan nilai yang memiliki frekuensi tertinggi sebagai kandidat nilai modus, kemudian mengecek apakah nilai tersebut benar-benar merupakan modus dari data.
Sensitivity analysis in linear programming problem ( Muhammed Jiyad)Muhammed Jiyad
Topic includes : *Sensitivity Analysis *Objective function *Right Hand Side(RHS) *Sensitivity analysis using graph *Objective function coefficient *Reduced cost *Shadow pricing *Shadow pricing Microsoft Excel sensitivity report and solution.
This document discusses penalty functions, which convert constrained optimization problems into unconstrained problems by introducing penalties for violating constraints. It presents the example of minimizing f(x)=100/x subject to x≤5. Constraints are converted to quadratic penalty terms and added to the objective function. Small iterative steps and increasing the penalty severity r improves convergence to the minimum. Practice problems demonstrate implementing an iterative minimization method and applying penalty functions to additional examples.
Perencanaan agregat memainkan peran penting dalam menentukan parameter operasional seperti tingkat produksi, tenaga kerja, persediaan, dan subkontrak selama periode perencanaan agar dapat memenuhi permintaan dan memaksimalkan keuntungan rantai pasokan. Perusahaan perlu mempertimbangkan berbagai strategi seperti mengikuti permintaan, fleksibilitas waktu, atau menjaga tingkat produksi konstan sambil mengandalkan persediaan untuk men
Metode Transportasi (Masalah dalam Metode Transportasi)hazhiyah
Seringkali terjadi dalam kenyataan dimana total permintaan tidak sama dengan total penawaran. Masalah ketidakseimbangan dalam ini dalam metode transportasi dapat diatasi dengan mempergunakan persediaan dan permintaan bayangan (dummy). Selain masalah permintaan dan penawaran, dalam metode transportasi juga dikenal masalah lain yaitu degenerasi dan redudansi yang terjadi dalam penyelesaian masalah dalam metode transportasi baik itu di solusi awal atau pada solusi optimal
Teori permainan adalah pendekatan matematis untuk merumuskan situasi persaingan dan konflik antara berbagai kepentingan. Teori ini menganalisis proses pengambilan keputusan dari situasi persaingan yang melibatkan dua atau lebih pemain. Teori permainan dapat digunakan untuk menentukan strategi optimal bagi setiap pemain dalam situasi persaingan tertentu.
The document discusses the transportation problem and its solution methodology. It states that the transportation problem seeks to minimize the total shipping costs of transporting goods from multiple origins to destinations, given the unit shipping costs. It is solved in two phases - obtaining an initial feasible solution and then moving toward the optimal solution. Several methods are described for obtaining the initial feasible solution, including the Northwest Corner method, Least Cost method, and Vogel's Approximation Method. The document also discusses testing the initial solution for optimality using methods like the Stepping Stone method and Modified Distribution method.
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.
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.
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 regions, identify optimal solutions, and address multiple optimal solutions, infeasible solutions, and unbounded solutions. The examples aid in understanding the key steps and components involved in linear programming model formulation and graphical solution methods.
The document discusses linear programming problems and how to formulate them. It provides definitions of key terms like linear, programming, objective function, decision variables, and constraints. It then explains the steps to formulate a linear programming problem, including defining the objective, decision variables, mathematical objective function, and constraints. Several examples of formulated linear programming problems are provided to maximize profit or minimize costs subject to various constraints.
The document discusses linear programming models for solving business optimization problems. It provides an overview of linear programming and the steps to formulate a linear programming model, which are to define decision variables, construct the objective function, and formulate constraints. The document also discusses graphical solutions to linear programming problems using examples of maximizing profit from two products given resource constraints and minimizing fertilizer costs given nutrient requirements.
Lean Cell Design -Presentation project part c rohan naik & aditya kambleRohan Naik
This document presents the results of a lean cell design final project. It evaluates four alternatives for a cell design based on various metrics like changeovers, workers, overall equipment effectiveness, work stations, and costs. It selects the third alternative with 3 changeovers, 6 workers, 90% OEE and 9 work stations as the optimal design. It also includes analysis of demand levels, cycle times and capacity. The cell-based design achieves the objectives of minimizing floor space, inventory and number of operators while allowing for future growth. Additional improvements are supermarket delivery and reduced changeover times.
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.
beyond linear programming: mathematical programming extensionsAngelica Angelo Ocon
This document discusses integer programming and binary integer programming. Integer programming involves decision variables that must take on integer values. Binary integer programming uses binary variables that can only be 0 or 1. Examples show how to formulate integer programming models using binary variables to represent yes/no decisions and constraints. The key aspects of integer programming are ensuring decision variables are integers and that the optimal solution is also integer.
The COCOMO model is a widely used software cost estimation model developed by Barry Boehm in 1981. It predicts effort, schedule, and staffing needs based on project size and characteristics. The Basic COCOMO model uses three development modes (Organic, Semidetached, Embedded) and a formula to estimate effort and schedule based on thousands of delivered source instructions. However, its accuracy is limited as it does not account for various project attributes. Function Point Analysis is an alternative size measurement that counts types of system functions to estimate effort and cost based on a project's adjusted function points.
The COCOMO model is a widely used software cost estimation model developed by Barry Boehm in 1981. It predicts effort, schedule, and staffing needs based on project size and characteristics. The Basic COCOMO model uses three development modes (Organic, Semidetached, Embedded) and a simple formula to estimate effort and schedule based on thousands of delivered source instructions. However, its accuracy is limited as it does not account for various project attributes known to influence costs. Function Point Analysis is an alternative size measurement that counts different types of system functions and complexity factors to estimate effort and cost.
The COCOMO model is a widely used software cost estimation model developed by Barry Boehm in 1981. It predicts effort, schedule, and staffing needs based on project size and characteristics. The Basic COCOMO model uses three development modes (Organic, Semidetached, Embedded) and a simple formula to estimate effort and schedule based on thousands of delivered source instructions. However, its accuracy is limited as it does not account for various project attributes known to influence costs. Function Point Analysis is an alternative method that measures software size based on weighted user inputs, outputs, files, inquiries and interfaces to estimate effort in person-months and cost based on organizational productivity.
The document discusses goal programming, which is used to solve linear programs with multiple objectives viewed as goals. It describes goal programming as attempting to reach a satisfactory level of multiple objectives by minimizing deviations between goals and what can actually be achieved given constraints. An example problem involves a hardware company with goals of achieving a $30 profit, fully utilizing wiring hours, avoiding assembly overtime, and producing at least 7 ceiling fans. The goal programming model for this problem is formulated and graphically solved to satisfy the higher priority goals as closely as possible before lower goals.
The document discusses goal programming, which is used to solve linear programs with multiple objectives viewed as goals. It describes goal programming as attempting to reach a satisfactory level of multiple objectives by minimizing deviations between goals and what can actually be achieved given constraints. An example problem is presented about a hardware company with goals of achieving a $30 profit, fully utilizing wiring hours, avoiding assembly overtime, and producing at least 7 ceiling fans. The goal programming model for this problem is formulated and graphical solving methods are described.
This document presents information on cost estimation using the COCOMO model. It discusses the basic, intermediate, and detailed COCOMO models. The basic model uses effort multipliers, staff size, and productivity equations to estimate effort and schedule for projects of different modes (organic, embedded, semidetached). The intermediate model adds 15 cost drivers to improve accuracy. The detailed model incorporates three product levels, phase-sensitive effort multipliers, and effort/time fractions for each development phase.
This document provides steps for crashing a project schedule to reduce its duration. It begins by explaining the two types of activity durations: normal and crash. It then outlines 9 steps to determine the maximum possible time reduction for a project: 1) draw the network diagram, 2) find the critical path using normal durations, 3) find the critical path using crash durations, 4) calculate the difference in critical paths, 5) calculate the slope for each activity, 6) select the activity with the lowest slope for crashing, 7) calculate the possible crash amount, 8) redraw the network and recalculate duration and cost, 9) compare to crashed critical path and repeat steps if different. It provides two notes: the critical path may change between
This document discusses various software metrics that can be used for software estimation, quality assurance, and maintenance. It describes black box metrics like function points and COCOMO, which focus on program functionality without examining internal structure. It also covers white box metrics, including lines of code, Halstead's software science, and McCabe's cyclomatic complexity, which measure internal program properties. Finally, it discusses using metrics like change rates and effort adjustment factors to estimate software maintenance costs.
CHAPTER TWO - OPERATIONS RESEARCH (2).pptxAynetuTerefe2
The document summarizes concepts of linear programming including its components and assumptions. It discusses that linear programming is an optimization method that allocates scarce resources in the best way subject to limiting conditions. The key components of a linear programming model are the objective function, decision variables, constraints, and parameters. Some assumptions of linear programming models include linearity, divisibility, certainty of parameters, and non-negativity. Examples are provided to illustrate how to formulate a linear programming model and solve problems graphically or using the simplex method.
The document discusses optimization techniques for project selection when there are limited capital resources. It describes formulating the problem as an integer linear program to select a subset of mutually exclusive projects that maximize total return subject to budget constraints. Specifically, it introduces integer programming and binary integer programming, provides an example problem modeling project selection as an integer program, and discusses relaxing the integrality constraints to obtain an initial linear programming solution.
This document provides instructions on how to model and solve linear programming problems in a spreadsheet using Excel's Solver tool. It begins with an introduction to LP problems and why spreadsheets are useful when there are more than two decision variables. It then explains how to access and enable the Solver add-in. The document outlines the steps to implement an LP model in a spreadsheet, including organizing the data, designating cells for decision variables and objectives/constraints. Finally, it provides examples of modeling different classic LP problems like resource allocation, transportation, and investment planning.
MAN 547_Reverse Engineering-Hardware and Software.pptxAhmed Sobhi
This document discusses reverse engineering hardware and software. It describes how reverse engineering hardware is used to acquire geometric data from physical objects using contact-based techniques like coordinate measurement machines or non-contact methods like optical scanning. The data is then processed by reverse engineering software to generate 3D models. The document outlines various contact and non-contact data acquisition methods and their advantages and disadvantages.
1) Gear analysis is based on rolling cylinders in contact. The fundamental law of gearing states that the angular velocity ratio between two meshing gears remains constant.
2) There are several types of gears including spur gears, helical gears, bevel gears, and worm gears. Spur gears have straight teeth while helical gears have slanted teeth to reduce noise.
3) Compound and planetary gear trains can achieve higher overall gear ratios than simple gear trains through the use of multiple meshing gear pairs and shafts. Planetary gears have the sun, planet, and ring gear configuration.
This document discusses factors to consider when choosing a coordinate measuring machine (CMM) inspection solution. It compares discrete point measurement using touch-trigger probes to scanning probes. Discrete point measurement is well-suited for controlling feature position and size, while scanning allows measuring feature form and profile. Scanning provides more data but incurs more stylus wear and can be affected by machine dynamics at high speeds. The ideal solution provides both high-speed scanning and discrete point measurement, along with flexible sensor options.
Gears are used to transmit rotational motion between shafts. The main types are spur gears, helical gears, bevel gears, worm gears, and rack and pinion gears. Gear trains use multiple gears meshed together to increase or decrease speed and torque. Simple gear trains use two gears on parallel shafts. Compound gear trains use multiple gears on one shaft. Planetary gear trains use a sun gear engaging planet gears fixed to a carrier, which engage an outer ring gear. They provide higher gear ratios and are used in automatic transmissions.
The document introduces an Operations Research course, describing its objectives to teach problem solving techniques and decision making skills through practical applications and assignments. Students will be evaluated through midterm and final exams, assignments, a project, and class participation. The lecture also discusses problem solving approaches in operations research and engineering.
The document discusses network flow problems in operations research and linear programming. It provides examples of the shortest route problem and minimal spanning tree problem. The shortest route problem aims to find the shortest paths from an origin to all destinations in a network. The minimal spanning tree problem seeks to connect all nodes in a network using the minimum total length of branches. Sample problems and step-by-step solutions are presented to illustrate how these network problems can be solved using algorithms that build solutions incrementally by adding nodes to a permanent set.
This document discusses spline interpolation techniques for estimating values between data points. It covers linear, quadratic, and cubic spline interpolation. Linear splines connect data points with straight lines. Quadratic splines use second order polynomials between points, requiring the function and first derivative to be continuous at knots. Cubic splines employ third order polynomials between points, with the function, first and second derivatives required to be continuous at knots. Examples are provided to demonstrate fitting data with each type of spline and evaluating the resulting functions.
This document discusses various types of storage systems used in manufacturing. It describes the functions of storage systems to store materials temporarily and allow retrieval when needed. Common types of materials stored include raw materials, work-in-process, finished goods, and spare parts. Performance is measured by storage capacity, density, throughput, utilization, and availability. Location strategies like randomized and dedicated storage are compared. Different equipment options like racks, shelves, drawers, and automated systems are outlined. Automated storage/retrieval systems and carousel systems are highlighted as automated options.
Cams are rotating or reciprocating elements that impart motion to followers. There are various types of cams including wedge, flat, radial, offset, cylindrical, spiral, conjugate, and globoidal cams. Followers can be classified based on their surface contact with the cam, type of motion, and line of motion. Key aspects of cam design include the base circle, prime circle, pitch point, pitch circle, trace point, pitch curve, cam angle, lift, cam profile, and pressure angle. Proper cam design considers factors like wear reduction, side thrust minimization, and optimal follower motion.
This document discusses different methods for solid modeling in CAD systems. It covers primitive creation functions which use basic shapes like boxes and cylinders. Constructive solid geometry (CSG) is described as using Boolean operations like union, subtraction and intersection on primitive shapes to build complex models. Sweeping and boundary representation (BREP) are also listed as other solid modeling techniques but not described further. The document provides examples of using primitive shapes and CSG Boolean operations to define a 3D part model.
The document discusses kinematics of rigid bodies, including definitions of translation, rotation about a fixed axis, and general plane motion. It provides equations relating position, velocity, and acceleration for particles undergoing translation and rotation. Examples are presented of determining velocities and accelerations of points on rigid bodies in translation, rotation, and rolling contact motion. Key concepts covered include absolute and relative velocity diagrams.
The document discusses kinematics of rigid bodies, including different types of motion such as translation, rotation about a fixed axis, and general plane motion. It provides equations to define velocity and acceleration for different types of rigid body motion. Sample problems are included to demonstrate how to analyze and calculate velocities, accelerations, angular velocities, and angular accelerations of points on rigid bodies undergoing various motions. Key concepts covered include absolute and relative velocity and acceleration in plane motion, and analyzing general plane motion as a combination of translation and rotation.
This document discusses mechanical mechanisms and Grashof's law for calculating the degrees of freedom of linkages. It provides Grubler's equation for determining degrees of freedom as a function of the number of links (L), number of pivot joints (PL), and fixed joints (PH). An example applies this equation to a 4-bar linkage with 1 degree of freedom. The document also covers Grashof's criteria for classifying 4-bar linkages as single crank, crank-rocker, double rocker or others based on the relative lengths of the links. Various examples of 4-bar configurations are presented.
This document discusses logistics operations and management. It covers topics like the different types of logistics workers, the functions of logistics like procurement, warehousing and transportation, and key aspects of operating warehouses and managing transportation. The goal of logistics is to satisfy customer demand by planning and controlling the acquisition, movement, storage and distribution of materials and products.
02_Ch(2)_Work Systems and How They Work.pptxAhmed Sobhi
Here are the key steps to solve this example:
(a) Standard time = Normal time * (1 + PFD allowance factor)
= 3.23 min * (1 + 0.15) = 3.23 * 1.15 = 3.72 min
(b) Irregular element time per cycle = Normal time / Frequency
= 1.25 min / 5 cycles = 0.25 min
(c) Standard time including irregular element
= Regular cycle time + Irregular element time per cycle
= 3.72 min + 0.25 min = 3.97 min
(d) In an 8-hr shift at standard time:
Units produced = (Shift time / Standard time)
This document discusses worker-machine systems and different types of automation. It defines worker-machine systems as those involving a worker operating powered equipment. The key strengths of humans and machines in such systems are described. Different types of powered equipment, classifications, numbers of workers/machines, and levels of operator attention are outlined. The document provides examples of calculating cycle times, work requirements, and the effects of factors like learning, efficiency, defects, and availability. Overall it provides an overview of analyzing worker-machine systems and determining resource needs.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network