The document discusses the assignment problem, which involves assigning jobs to workers in the most efficient way. It describes how workers have varying abilities for different jobs, so the costs of performing jobs differ. The assignment problem can be represented as a matrix showing the costs of each worker performing each job. The Hungarian method is described as an algorithmic approach to solving the assignment problem by finding the optimal assignment that minimizes total costs. It involves developing cost matrices and making assignments to iteratively arrive at an optimal solution.
This document discusses multi-objective optimization. It begins by defining multi-objective optimization as involving more than one objective function to optimize simultaneously. Objectives are often conflicting. The notion of an optimum must be redefined as a set of Pareto optimal solutions. Weighted sum methods are commonly used to generate Pareto optimal solutions by converting the multi-objective problem into single objective problems, but this has limitations such as an inability to find non-convex portions of the Pareto front. Evolutionary algorithms are now often used for multi-objective optimization.
S2 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
- Response surface methodology (RSM) uses statistical techniques to model and analyze problems with response variables influenced by multiple independent variables. The goal is to optimize the response.
- RSM has been used since the 1930s and was reviewed in landmark papers in 1966 and 1976. It is commonly used in industries, agriculture, medicine, and other fields to optimize processes and products.
- There are two main experimental strategies in RSM - first-order models to initially evaluate relationships between factors and responses, and second-order models to account for curvature and find optimal points if curvature is present.
A problem is provided which is solved by using graphical and analytical method of linear programming method and then it is solved by using geometrical concept and algebraic concept of simplex method.
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.
OPTIMIZATION TECHNIQUES
Optimization techniques are methods for achieving the best possible result under given constraints. There are various classical and advanced optimization methods. Classical methods include techniques for single-variable, multi-variable without constraints, and multi-variable with equality or inequality constraints using methods like Lagrange multipliers or Kuhn-Tucker conditions. Advanced methods include hill climbing, simulated annealing, genetic algorithms, and ant colony optimization. Optimization has applications in fields like engineering, business/economics, and pharmaceutical formulation to improve processes and outcomes under constraints.
The document describes using design of experiments (DoE) to optimize a pharmaceutical formulation. Two factors, the ratio of ingredients A:B and compressional force, were selected as independent variables. Response variables like disintegration time, hardness, and dissolution were measured for 9 formulations designed using a central composite design. Analysis of variance showed the compressional force factor significantly affected particle size index (PDI), while the ratio had little effect. The final equation related PDI to the factors in coded units. Point and interval predictions were also presented.
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.
This document discusses multi-objective optimization. It begins by defining multi-objective optimization as involving more than one objective function to optimize simultaneously. Objectives are often conflicting. The notion of an optimum must be redefined as a set of Pareto optimal solutions. Weighted sum methods are commonly used to generate Pareto optimal solutions by converting the multi-objective problem into single objective problems, but this has limitations such as an inability to find non-convex portions of the Pareto front. Evolutionary algorithms are now often used for multi-objective optimization.
S2 - Process product optimization using design experiments and response surfa...CAChemE
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
- Response surface methodology (RSM) uses statistical techniques to model and analyze problems with response variables influenced by multiple independent variables. The goal is to optimize the response.
- RSM has been used since the 1930s and was reviewed in landmark papers in 1966 and 1976. It is commonly used in industries, agriculture, medicine, and other fields to optimize processes and products.
- There are two main experimental strategies in RSM - first-order models to initially evaluate relationships between factors and responses, and second-order models to account for curvature and find optimal points if curvature is present.
A problem is provided which is solved by using graphical and analytical method of linear programming method and then it is solved by using geometrical concept and algebraic concept of simplex method.
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.
OPTIMIZATION TECHNIQUES
Optimization techniques are methods for achieving the best possible result under given constraints. There are various classical and advanced optimization methods. Classical methods include techniques for single-variable, multi-variable without constraints, and multi-variable with equality or inequality constraints using methods like Lagrange multipliers or Kuhn-Tucker conditions. Advanced methods include hill climbing, simulated annealing, genetic algorithms, and ant colony optimization. Optimization has applications in fields like engineering, business/economics, and pharmaceutical formulation to improve processes and outcomes under constraints.
The document describes using design of experiments (DoE) to optimize a pharmaceutical formulation. Two factors, the ratio of ingredients A:B and compressional force, were selected as independent variables. Response variables like disintegration time, hardness, and dissolution were measured for 9 formulations designed using a central composite design. Analysis of variance showed the compressional force factor significantly affected particle size index (PDI), while the ratio had little effect. The final equation related PDI to the factors in coded units. Point and interval predictions were also presented.
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.
S4 - Process/product optimization using design of experiments and response su...CAChemE
Session 3 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Schedule and details:
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
Mathematical Optimisation - Fundamentals and ApplicationsGokul Alex
My Session on Mathematical Optimisation Fundamentals and Industry applications for the Academic Knowledge Refresher Program organised by Kerala Technology University and College of Engineering Trivandrum, Department of Interdisciplinary Studies.
This document presents an overview of linear programming, including:
- Linear programming involves choosing a course of action when the mathematical model contains only linear functions.
- The objective is to maximize or minimize some quantity subject to constraints. A feasible solution satisfies all constraints while an optimal solution results in the largest/smallest objective value.
- Problem formulation involves translating a verbal problem statement into mathematical terms by defining decision variables and writing the objective and constraints in terms of these variables.
- An example problem is presented to maximize profit by determining the optimal number of products A and B to manufacture, given constraints on money invested and labor hours. The objective and constraints are written mathematically to formulate the problem as a linear program.
S3 - Process product optimization design experiments response surface methodo...CAChemE
Session 3/4 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
Application of linear programming technique for staff training of register se...Enamul Islam
This study aims to minimize training costs for staff at Patuakhali Science and Technology University using linear programming. It identifies two decision variables (permanent and non-permanent staff to be trained) and develops constraints based on time available and staff in different departments. The linear programming model is solved to find the optimal solution: 1 permanent staff should be sent for 5 days of training among departments to minimize costs. The research suggests this approach can help determine optimal staffing levels for future training programs.
The document discusses linear programming, including:
1. It describes the basic concepts of linear programming, such as decision variables, constraints, and the objective function needing to be linear.
2. It explains the steps to formulate a linear programming problem, such as identifying decision variables and constraints, and writing the objective function and constraints as linear combinations of the variables.
3. It provides examples of how to write linear programming problems in standard form to maximize or minimize objectives subject to constraints.
This document discusses linear programming and its concepts, formulation, and methods of solving linear programming problems. It provides the following key points:
1) Linear programming involves optimizing a linear objective function subject to linear constraints. It aims to find the best allocation of limited resources to achieve objectives.
2) Formulating a linear programming problem involves identifying decision variables, the objective function, and constraints. Problems can be solved graphically or algebraically using the simplex method.
3) The graphic method can be used for problems with two variables, involving plotting the constraints on a graph to find the optimal solution at a corner point of the feasible region.
Numerical analysis multivariable unconstrainedSHAMJITH KM
This document discusses unconstrained optimization of functions with multiple variables. It explains that the gradient vector and Hessian matrix are used to analyze these types of functions. The necessary condition for a stationary point is that the gradient vector must be equal to zero. The sufficient condition is that the Hessian matrix must be positive definite for a minimum or negative definite for a maximum. An example function is analyzed to classify its stationary points as maxima, minima or points of inflection. The solutions and working are shown step-by-step for this example.
The document discusses linear programming, which is a method for optimizing a linear objective function subject to linear equality and inequality constraints. It describes how to formulate a linear programming problem by defining the objective function and constraints in terms of decision variables. It also discusses graphical and algebraic solution methods, including identifying an optimal solution at an extreme point of the feasible region. Applications of linear programming are mentioned in areas like business, industry, and marketing.
- Response surface methodology (RSM) is used to optimize processes with multiple variables to maximize or minimize a response. It uses experimental design and regression analysis.
- The method of steepest ascent is used to sequentially move from an initial guess towards the optimum region using a first-order model. Additional experiments are conducted to fit higher-order models closer to the optimum.
- A second-order model that includes interaction and quadratic terms can identify if the stationary point is a maximum, minimum, or saddle point. Canonical analysis of the eigenvalues further characterizes the stationary point.
This presentation provides an overview of response surface methodology (RSM). RSM is a statistical technique used to build models and optimize responses based on the relationships between several input variables. The presentation covers the basics of RSM, including its introduction, methodology, examples, and applications. Key topics discussed are modeling responses with polynomial equations, designing experiments, and using RSM to find an optimal response by varying input variables.
Multiobjective optimization and Genetic algorithms in ScilabScilab
In this Scilab tutorial we discuss about the importance of multiobjective optimization and we give an overview of all possible Pareto frontiers. Moreover we show how to use the NSGA-II algorithm available in Scilab.
This document discusses multiobjective optimization problems which involve simultaneously maximizing or minimizing multiple criteria. It provides three key points:
1) Multiobjective optimization problems seek to find Pareto optimal solutions, where improving one objective cannot be achieved without worsening another. The set of Pareto optimal solutions is known as the Pareto frontier.
2) Several methods can solve multiobjective problems, including the weighted sum, ε-constraint, and lexicographic methods. These generate a set of Pareto optimal solutions.
3) The weighted sum method scalarizes objectives into a single objective using weights. The ε-constraint method optimizes one objective subject to constraints on the others. The lexicographic method sequentially optimizes objectives
Multi Objective Optimization and Pareto Multi Objective Optimization with cas...Aditya Deshpande
This document discusses multi-objective optimization and Pareto multi-objective optimization. It provides examples of multi-objective optimization problems with two or more competing objectives that must be optimized simultaneously. The key concepts covered include Pareto optimal solutions, which define the best trade-offs between objectives and are non-dominated by other solutions. Methods for solving multi-objective optimization problems include traditional approaches that aggregate objectives and Pareto techniques using genetic algorithms and multi-objective evolutionary algorithms.
This document provides an overview of particle filtering and sampling algorithms. It discusses key concepts like Bayesian estimation, Monte Carlo integration methods, the particle filter, and sampling algorithms. The particle filter approximates probabilities with weighted samples to estimate states in nonlinear, non-Gaussian systems. It performs recursive Bayesian filtering by predicting particle states and updating their weights based on new observations. While powerful, particle filters have high computational complexity and it can be difficult to determine the optimal number of particles.
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 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.
This document discusses variations of the interval linear assignment problem. It begins with an introduction to assignment problems and defines them as problems that assign resources to activities to minimize cost or maximize profit on a one-to-one basis. It then provides the mathematical model for standard assignment problems and discusses variations such as non-square matrices, maximization/minimization objectives, constrained assignments, and alternate optimal solutions. The document also gives examples of managerial applications and provides two numerical examples solving interval linear assignment problems using an interval Hungarian method.
Assignment Chapter - Q & A Compilation by Niraj ThapaCA Niraj Thapa
My name is Niraj Thapa. I have compiled Assignment Chapter including SM, PM & Exam Questions of AMA.
You feedback on this will be valuable inputs for me to proceed further.
This document discusses and compares different methods for solving assignment problems. It begins with an abstract that defines assignment problems as optimally assigning n objects to m other objects in an injective (one-to-one) fashion. It then provides an introduction to the Hungarian method and a new proposed Matrix Ones Assignment (MOA) method. The body of the document provides details on modeling assignment problems with cost matrices, formulations as linear programs, and step-by-step explanations of the Hungarian and MOA methods. It includes an example solved using the Hungarian method.
S4 - Process/product optimization using design of experiments and response su...CAChemE
Session 3 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
Schedule and details:
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
Mathematical Optimisation - Fundamentals and ApplicationsGokul Alex
My Session on Mathematical Optimisation Fundamentals and Industry applications for the Academic Knowledge Refresher Program organised by Kerala Technology University and College of Engineering Trivandrum, Department of Interdisciplinary Studies.
This document presents an overview of linear programming, including:
- Linear programming involves choosing a course of action when the mathematical model contains only linear functions.
- The objective is to maximize or minimize some quantity subject to constraints. A feasible solution satisfies all constraints while an optimal solution results in the largest/smallest objective value.
- Problem formulation involves translating a verbal problem statement into mathematical terms by defining decision variables and writing the objective and constraints in terms of these variables.
- An example problem is presented to maximize profit by determining the optimal number of products A and B to manufacture, given constraints on money invested and labor hours. The objective and constraints are written mathematically to formulate the problem as a linear program.
S3 - Process product optimization design experiments response surface methodo...CAChemE
Session 3/4 – Central composite designs, second order models, ANOVA, blocking, qualitative factors
An intensive practical course mainly for PhD-students on the use of designs of experiments (DOE) and response surface methodology (RSM) for optimization problems. The course covers relevant background, nomenclature and general theory of DOE and RSM modelling for factorial and optimisation designs in addition to practical exercises in Matlab. Due to time limitations, the course concentrates on linear and quadratic models on the k≤3 design dimension. This course is an ideal starting point for every experimental engineering wanting to work effectively, extract maximal information and predict the future behaviour of their system.
Mikko Mäkelä (DSc, Tech) is a postdoctoral fellow at the Swedish University of Agricultural Sciences in Umeå, Sweden and is currently visiting the Department of Chemical Engineering at the University of Alicante. He is working in close cooperation with Paul Geladi, Professor of Chemometrics, and using DOE and RSM for process optimization mainly for the valorization of industrial wastes in laboratory and pilot scales.”
The course took place at the University of Alicante and would not had been possible without the support of the Instituto Universitario de Ingeniería de Procesos Químicos.
Application of linear programming technique for staff training of register se...Enamul Islam
This study aims to minimize training costs for staff at Patuakhali Science and Technology University using linear programming. It identifies two decision variables (permanent and non-permanent staff to be trained) and develops constraints based on time available and staff in different departments. The linear programming model is solved to find the optimal solution: 1 permanent staff should be sent for 5 days of training among departments to minimize costs. The research suggests this approach can help determine optimal staffing levels for future training programs.
The document discusses linear programming, including:
1. It describes the basic concepts of linear programming, such as decision variables, constraints, and the objective function needing to be linear.
2. It explains the steps to formulate a linear programming problem, such as identifying decision variables and constraints, and writing the objective function and constraints as linear combinations of the variables.
3. It provides examples of how to write linear programming problems in standard form to maximize or minimize objectives subject to constraints.
This document discusses linear programming and its concepts, formulation, and methods of solving linear programming problems. It provides the following key points:
1) Linear programming involves optimizing a linear objective function subject to linear constraints. It aims to find the best allocation of limited resources to achieve objectives.
2) Formulating a linear programming problem involves identifying decision variables, the objective function, and constraints. Problems can be solved graphically or algebraically using the simplex method.
3) The graphic method can be used for problems with two variables, involving plotting the constraints on a graph to find the optimal solution at a corner point of the feasible region.
Numerical analysis multivariable unconstrainedSHAMJITH KM
This document discusses unconstrained optimization of functions with multiple variables. It explains that the gradient vector and Hessian matrix are used to analyze these types of functions. The necessary condition for a stationary point is that the gradient vector must be equal to zero. The sufficient condition is that the Hessian matrix must be positive definite for a minimum or negative definite for a maximum. An example function is analyzed to classify its stationary points as maxima, minima or points of inflection. The solutions and working are shown step-by-step for this example.
The document discusses linear programming, which is a method for optimizing a linear objective function subject to linear equality and inequality constraints. It describes how to formulate a linear programming problem by defining the objective function and constraints in terms of decision variables. It also discusses graphical and algebraic solution methods, including identifying an optimal solution at an extreme point of the feasible region. Applications of linear programming are mentioned in areas like business, industry, and marketing.
- Response surface methodology (RSM) is used to optimize processes with multiple variables to maximize or minimize a response. It uses experimental design and regression analysis.
- The method of steepest ascent is used to sequentially move from an initial guess towards the optimum region using a first-order model. Additional experiments are conducted to fit higher-order models closer to the optimum.
- A second-order model that includes interaction and quadratic terms can identify if the stationary point is a maximum, minimum, or saddle point. Canonical analysis of the eigenvalues further characterizes the stationary point.
This presentation provides an overview of response surface methodology (RSM). RSM is a statistical technique used to build models and optimize responses based on the relationships between several input variables. The presentation covers the basics of RSM, including its introduction, methodology, examples, and applications. Key topics discussed are modeling responses with polynomial equations, designing experiments, and using RSM to find an optimal response by varying input variables.
Multiobjective optimization and Genetic algorithms in ScilabScilab
In this Scilab tutorial we discuss about the importance of multiobjective optimization and we give an overview of all possible Pareto frontiers. Moreover we show how to use the NSGA-II algorithm available in Scilab.
This document discusses multiobjective optimization problems which involve simultaneously maximizing or minimizing multiple criteria. It provides three key points:
1) Multiobjective optimization problems seek to find Pareto optimal solutions, where improving one objective cannot be achieved without worsening another. The set of Pareto optimal solutions is known as the Pareto frontier.
2) Several methods can solve multiobjective problems, including the weighted sum, ε-constraint, and lexicographic methods. These generate a set of Pareto optimal solutions.
3) The weighted sum method scalarizes objectives into a single objective using weights. The ε-constraint method optimizes one objective subject to constraints on the others. The lexicographic method sequentially optimizes objectives
Multi Objective Optimization and Pareto Multi Objective Optimization with cas...Aditya Deshpande
This document discusses multi-objective optimization and Pareto multi-objective optimization. It provides examples of multi-objective optimization problems with two or more competing objectives that must be optimized simultaneously. The key concepts covered include Pareto optimal solutions, which define the best trade-offs between objectives and are non-dominated by other solutions. Methods for solving multi-objective optimization problems include traditional approaches that aggregate objectives and Pareto techniques using genetic algorithms and multi-objective evolutionary algorithms.
This document provides an overview of particle filtering and sampling algorithms. It discusses key concepts like Bayesian estimation, Monte Carlo integration methods, the particle filter, and sampling algorithms. The particle filter approximates probabilities with weighted samples to estimate states in nonlinear, non-Gaussian systems. It performs recursive Bayesian filtering by predicting particle states and updating their weights based on new observations. While powerful, particle filters have high computational complexity and it can be difficult to determine the optimal number of particles.
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 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.
This document discusses variations of the interval linear assignment problem. It begins with an introduction to assignment problems and defines them as problems that assign resources to activities to minimize cost or maximize profit on a one-to-one basis. It then provides the mathematical model for standard assignment problems and discusses variations such as non-square matrices, maximization/minimization objectives, constrained assignments, and alternate optimal solutions. The document also gives examples of managerial applications and provides two numerical examples solving interval linear assignment problems using an interval Hungarian method.
Assignment Chapter - Q & A Compilation by Niraj ThapaCA Niraj Thapa
My name is Niraj Thapa. I have compiled Assignment Chapter including SM, PM & Exam Questions of AMA.
You feedback on this will be valuable inputs for me to proceed further.
This document discusses and compares different methods for solving assignment problems. It begins with an abstract that defines assignment problems as optimally assigning n objects to m other objects in an injective (one-to-one) fashion. It then provides an introduction to the Hungarian method and a new proposed Matrix Ones Assignment (MOA) method. The body of the document provides details on modeling assignment problems with cost matrices, formulations as linear programs, and step-by-step explanations of the Hungarian and MOA methods. It includes an example solved using the Hungarian method.
A Comparative Analysis Of Assignment ProblemJim Webb
This document provides a comparative analysis of different methods for solving assignment problems, including the Hungarian method and a new proposed Matrix Ones Assignment (MOA) method. It first introduces assignment problems and describes their applications. It then explains the Hungarian method in detail through examples. Finally, it outlines the steps of the new MOA method, which aims to create ones in the assignment matrix to find optimal assignments. The document compares the two approaches and provides an example solved using the MOA method.
The document discusses the assignment problem, which involves assigning people, jobs, machines, etc. to minimize costs or maximize profits. It provides an example of assigning 4 men to 4 jobs to minimize total cost, walking through the Hungarian method steps. It also discusses how to handle imbalance by adding dummy rows or columns, and how to convert a maximization problem to minimization.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document discusses the backtracking algorithm and branch-and-bound algorithm. Backtracking is used to solve problems by intelligently constructing partial solutions and evaluating them to avoid wasting time on solutions that cannot be completed. Branch-and-bound is similar but used for optimization problems, requiring bounds on objective function values to prune search branches. Examples demonstrated include the N-queens problem, subset sum problem, and assignment problem solved with branch-and-bound.
The document provides information on operations research and the assignment problem. It discusses the steps to solve an assignment problem, which include: (1) writing the problem in a matrix form, (2) obtaining a reduced cost matrix through row and column operations, and (3) making assignments on a one-to-one basis by considering zeros in rows and columns. It also addresses issues like unbalanced matrices, maximization problems, and infeasible assignments.
This document discusses the assignment problem and provides an overview of the Hungarian algorithm for solving assignment problems. It begins by defining the assignment problem and describing it as a special case of the transportation problem. It then provides details on the Hungarian algorithm, including the key theorems and steps involved. An example problem of assigning salespeople to cities is presented and solved using the Hungarian algorithm to find the optimal assignment with minimum total cost. The document concludes that the Hungarian algorithm provides an efficient solution for minimizing assignment problems.
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
NEW APPROACH FOR SOLVING FUZZY TRIANGULAR ASSIGNMENT BY ROW MINIMA METHODIAEME Publication
The Fuzzy Assignment Problem (FAP) is a classic combinatorial optimization problem that has received a lot of attention. FAP has a wide range of uses. We suggest a new algorithm that combines to solve the FAP in this paper. Each column is maximized during the optimization process, and the best choice with the lowest cost is selected. The proposed method follows a standard methodology, is simple to execute, and takes less effort to compute. An order to obtain the best solution, the assignment problem is specifically solved here. We looked at how well trapezoidal fuzzy numbers performed. Then, to convert crisp numbers, we use the robust ranking method for trapezoidal fuzzy numbers. The optimality of the result provided by this new method is clarified by a numerical example.
Chapter 3.Simplex Method hand out last.pdfTsegay Berhe
This document provides material on solving linear programming problems using the simplex method. It begins with an introduction to the simplex method and how it can be used to solve linear programming problems analytically. It then presents the steps for solving a problem using the simplex method, including determining a starting basic feasible solution, selecting entering and leaving variables, and performing elementary row operations to arrive at the optimal solution. An example problem is also presented to illustrate how to set up and solve a linear programming problem using the simplex method.
This document provides information about getting fully solved assignments from an assignment help service. Students are instructed to send their semester and specialization name to the provided email address or call the given phone number to receive help with their assignments. Mailing is preferred over calling except in emergencies. The document then provides a sample assignment question related to operations research on the topics of linear programming, transportation problem, simulation, integer programming, PERT/CPM, and queuing systems.
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This document discusses the matching problem and provides an overview of algorithms and methods for solving it. It begins with an agenda that covers illustrating the problem, relevant definitions, modeling it mathematically, selecting algorithms like the Hungarian method, use cases, exercises, solving it using Excel, and reviewing literature. It then goes into more detail on each section, providing examples, visualizations, the formal problem model, and a step-by-step example of applying the Hungarian method to solve a matching problem.
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Send your semester & Specialization name to our mail id :
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On the Performance of the Pareto Set Pursuing (PSP) Method for Mixed-Variable...Amir Ziai
This document describes a study on modifying the Pareto Set Pursuing (PSP) method to solve multi-objective optimization problems with mixed continuous and discrete variables. The PSP method was originally developed for problems with only continuous variables. The modifications allow it to handle mixed variable problems. The performance of the modified PSP method is compared to other multi-objective algorithms based on metrics like efficiency, robustness, and closeness to the true Pareto front with a limited number of function evaluations. Preliminary results on benchmark problems and two engineering design examples show that the modified PSP is competitive when the number of function evaluations is limited, but its performance decreases as the number of design variables increases.
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This document contains answers to assignment questions on operations research. It defines operations research and describes types of operations research models including physical and mathematical models. It also outlines the phases of operations research including the judgment, research, and action phases. Additionally, it provides explanations and examples of linear programming problems and their graphical solution method, as well as addressing how to solve degeneracies in transportation problems and explaining the MODI optimality test procedure.
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Starting a business is like embarking on an unpredictable adventure. It’s a journey filled with highs and lows, victories and defeats. But what if I told you that those setbacks and failures could be the very stepping stones that lead you to fortune? Let’s explore how resilience, adaptability, and strategic thinking can transform adversity into opportunity.
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How to Start Up a Company: A Step-by-Step Guide Starting a company is an exciting adventure that combines creativity, strategy, and hard work. It can seem overwhelming at first, but with the right guidance, anyone can transform a great idea into a successful business. Let's dive into how to start up a company, from the initial spark of an idea to securing funding and launching your startup.
Introduction
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The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
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Profiles of Iconic Fashion Personalities.pdfTTop Threads
The fashion industry is dynamic and ever-changing, continuously sculpted by trailblazing visionaries who challenge norms and redefine beauty. This document delves into the profiles of some of the most iconic fashion personalities whose impact has left a lasting impression on the industry. From timeless designers to modern-day influencers, each individual has uniquely woven their thread into the rich fabric of fashion history, contributing to its ongoing evolution.
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How MJ Global Leads the Packaging Industry.pdfMJ Global
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations
Innovation Management Frameworks: Your Guide to Creativity & Innovation
Assignment
1. ASSIGNMENT PROBLEM
ASSIGNMENT PROBLEM
By
DR. NEHA GUPTA
FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 1 / 6
2. ASSIGNMENT PROBLEM
ASSIGNMENT PROBLEM
In every workplace, there are jobs to be done and there are people available to do them.
But everyone is not equally efficient at every job. Someone may be more efficient on
one and less efficient on the other job, while it might be otherwise for someone else. The
relative efficiency is reflected in terms of the time taken for, or the cost associated with,
performance of different jobs by different people. An obvious problem for a manager to
handle is to assign jobs to various workers in a manner that they can be done in the
most efficient way. Such problems are known as Assignment Problem.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 2 / 6
3. ASSIGNMENT PROBLEM
There are many situations where the assignment of people, machines, and so on may
be called for. Assignment of workers to machines, clerks to various checkout counters,
salesmen to different sales areas, service crews to different districts, are typical
examples of these. Assignment is a problem because people possess varying abilities
for performing different jobs and, therefore, the costs of performing those jobs are
different.
An assignment problem is a particular case of transportation problem where the
resources (say facilities) are assignees and the destinations are activities (say jobs).
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 3 / 6
4. ASSIGNMENT PROBLEM
Given n facilities and n jobs, with effectiveness (in terms of cost, profit, time etc.) of
each facility for each job. Then problem becomes to assign each facility to only one job
and vice-versa so that the given measure of effectiveness is optimized. The general
data matrix for assignment problem is as follows:
Jobs
Workers J1 J2 · · · Jn Supply
W1 c11 c12 · · · c1n 1
W2 c21 c22 · · · c2n 1
...
...
...
...
...
...
Wn cn1 cn2 · · · cnn 1
Demand 1 1 · · · 1 n
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 4 / 6
5. ASSIGNMENT PROBLEM
Suppose, xij represents the assignment of worker i to job j such that
xij =
1 if worker i is assigned to activity j
0 otherwise
Then mathematical model of the assignment problem can be stated as:
Minimize =
n
i=1
n
j=1
cij xij
subject to
n
j=1
xij = 1; for all i
n
i=1
xij = 1; for all j
xij = 0 or 1
(1)
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 5 / 6
6. ASSIGNMENT PROBLEM
Suppose, xij represents the assignment of worker i to job j such that
xij =
1 if worker i is assigned to activity j
0 otherwise
Then mathematical model of the assignment problem can be stated as:
Minimize =
n
i=1
n
j=1
cij xij
subject to
n
j=1
xij = 1; for all i
n
i=1
xij = 1; for all j
xij = 0 or 1
(1)
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 5 / 6
7. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
8. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
9. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
10. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
11. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
12. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
13. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
14. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
15. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
16. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
17. ASSIGNMENT PROBLEM
Hungarian Method
The Hungarian method (minimization case) can be summarized in the following steps:
Step 1 Develop the cost matrix from the given problem.
Step 2 Find the opportunity cost matrix.
Step 3 Make assignments in the opportunity cost matrix.
Step 4 Optimality criterion
If all the zero elements in the cost matrix are either marked with
square or crossed off and there is exactly one assignment in each
row and column, then it is an optimal solution. The total cost
associated with this solution is obtained by adding the original cost
elements in the occupied cells.
If a zero element in a row or column was chosen arbitrarily for
assignment, there exists an alternative optimal solution.
If there is no assignment in a row (or column), then this implies that
the total number of assignments are less than the number of
rows/columns in the square matrix. In such a situation proceed to
Step 5.
Step 5 Revise the opportunity cost matrix.
Step 6 Develop the new revised opportunity cost matrix.
Step 7 Repeat steps.
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6
18. ASSIGNMENT PROBLEM
Example:
A computer centre has three expert programmers. The centre wants three application
programmes to be developed. The head of the computer centre, after carefully
studying the programmes to be developed, estimates the computer time in minutes
required by the experts for the application programmes as follows:
A B C
1 120 100 80
2 80 90 110
3 110 140 120
ByDR. NEHA GUPTA (FACULTY OF COMMERCE & MANAGEMENT, SGT UNIVERSITY)ASSIGNMENT PROBLEM 6 / 6