The document discusses the simplex method for solving linear programming problems. It begins by explaining how the simplex method uses an algebraic approach to solve problems with more than two decision variables and constraints, unlike the graphical method. It then provides details on how to set up and solve a linear programming problem using the simplex method, including converting it to standard form, creating an initial simplex tableau, choosing pivot columns and rows, and performing pivot operations until an optimal solution is reached. An example problem is worked through step-by-step to demonstrate the simplex method.
The Big-M method is a variation of the simplex method for solving linear programming problems with "greater-than" constraints. It works by introducing artificial variables with a large coefficient M to transform inequality constraints into equality constraints, creating an initial feasible solution. The transformed problem is then solved via simplex elimination to arrive at an optimal solution while eliminating artificial variables. The document provides an example problem demonstrating the step-by-step Big-M method process of setting up and solving a linear program with inequalities.
The Big M Method is a variant of the simplex method for solving linear programming problems. It introduces artificial variables and a large number M to convert inequalities into equalities. The transformed problem is then solved using the simplex method, eliminating artificial variables until an optimal solution is found. However, the method has drawbacks in determining a sufficiently large M value and not knowing feasibility until optimality is reached. It is inferior to the two-phase method and not used in commercial solvers.
The document discusses iterative improvement algorithms and provides examples such as the simplex method for solving linear programming problems. It explains the standard form of a linear programming problem and gives an outline of the simplex method, which generates a sequence of feasible solutions with improving objective values until an optimal solution is found. Some notes on limitations of the simplex method and improvements like the ellipsoid and interior-point methods are also mentioned.
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.
The document discusses duality theory in linear programming (LP). It explains that for every LP primal problem, there exists an associated dual problem. The primal problem aims to optimize resource allocation, while the dual problem aims to determine the appropriate valuation of resources. The relationship between primal and dual problems is fundamental to duality theory. The document provides examples of primal and dual problems and their formulations. It also outlines some general rules for constructing the dual problem from the primal, as well as relations between optimal solutions of primal and dual problems.
The Big M Method is used to solve linear programming problems with inequality constraints. It involves (1) multiplying inequality constraints to make the right hand side positive, (2) introducing surplus and artificial variables for greater-than constraints, (3) adding a large penalty M to the objective for artificial variables, and (4) introducing slack variables to convert all constraints to equalities. The method is demonstrated on a sample minimization problem that is converted to standard form and solved using the simplex method.
The document discusses the simplex method for solving linear programming problems. It begins by explaining how the simplex method uses an algebraic approach to solve problems with more than two decision variables and constraints, unlike the graphical method. It then provides details on how to set up and solve a linear programming problem using the simplex method, including converting it to standard form, creating an initial simplex tableau, choosing pivot columns and rows, and performing pivot operations until an optimal solution is reached. An example problem is worked through step-by-step to demonstrate the simplex method.
The Big-M method is a variation of the simplex method for solving linear programming problems with "greater-than" constraints. It works by introducing artificial variables with a large coefficient M to transform inequality constraints into equality constraints, creating an initial feasible solution. The transformed problem is then solved via simplex elimination to arrive at an optimal solution while eliminating artificial variables. The document provides an example problem demonstrating the step-by-step Big-M method process of setting up and solving a linear program with inequalities.
The Big M Method is a variant of the simplex method for solving linear programming problems. It introduces artificial variables and a large number M to convert inequalities into equalities. The transformed problem is then solved using the simplex method, eliminating artificial variables until an optimal solution is found. However, the method has drawbacks in determining a sufficiently large M value and not knowing feasibility until optimality is reached. It is inferior to the two-phase method and not used in commercial solvers.
The document discusses iterative improvement algorithms and provides examples such as the simplex method for solving linear programming problems. It explains the standard form of a linear programming problem and gives an outline of the simplex method, which generates a sequence of feasible solutions with improving objective values until an optimal solution is found. Some notes on limitations of the simplex method and improvements like the ellipsoid and interior-point methods are also mentioned.
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.
The document discusses duality theory in linear programming (LP). It explains that for every LP primal problem, there exists an associated dual problem. The primal problem aims to optimize resource allocation, while the dual problem aims to determine the appropriate valuation of resources. The relationship between primal and dual problems is fundamental to duality theory. The document provides examples of primal and dual problems and their formulations. It also outlines some general rules for constructing the dual problem from the primal, as well as relations between optimal solutions of primal and dual problems.
The Big M Method is used to solve linear programming problems with inequality constraints. It involves (1) multiplying inequality constraints to make the right hand side positive, (2) introducing surplus and artificial variables for greater-than constraints, (3) adding a large penalty M to the objective for artificial variables, and (4) introducing slack variables to convert all constraints to equalities. The method is demonstrated on a sample minimization problem that is converted to standard form and solved using the simplex method.
Sensitivity analysis linear programming copyKiran Jadhav
This document discusses sensitivity analysis in linear programming. It begins by defining sensitivity analysis as investigating how changes to a linear programming model's parameters, like objective function coefficients or constraint coefficients, affect the optimal solution. It then discusses the basic parameter changes that can impact the solution, like right-hand side constants or new variables/constraints. The document also covers duality in linear programming and how the dual problem is derived from the primal problem by setting coefficient values to the resource costs at optimality. An example is provided to demonstrate how the dual problem is formulated.
The steps of the simplex method are outlined. Artificial variables are introduced when the initial tableau lacks an identity submatrix. This allows the problem to be solved using the simplex method. The artificial variables are given a large penalty coefficient (-M for maximization) to force them to zero in the optimal solution. The example problem is converted to standard form and artificial variables are added, allowing it to be solved by the simplex method.
The Simplex Method is an algorithm for solving linear programming problems. It involves setting up the problem in standard form, constructing an initial simplex tableau, and then iteratively selecting pivot columns and performing row operations until an optimal solution is found. The method terminates when all indicators in the tableau are positive or zero, at which point the basic and non-basic variables can be identified to read the optimal solution.
The simplex method is a linear programming algorithm that can solve problems with more than two decision variables. It works by generating a series of solutions, called tableaus, where each tableau corresponds to a corner point of the feasible solution space. The algorithm starts at the initial tableau, which corresponds to the origin. It then shifts to adjacent corner points, moving in the direction that optimizes the objective function. This process of generating new tableaus continues until an optimal solution is found.
Artificial variable technique big m method (1)ਮਿਲਨਪ੍ਰੀਤ ਔਜਲਾ
The Big-M method is used to handle artificial variables in linear programming problems. It assigns very large coefficients to the artificial variables in the objective function, making them undesirable to include in optimal solutions. This removes the artificial variables from the basis. As an example, the document presents a linear programming problem to minimize an objective function subject to constraints, and shows the steps of converting it to an equivalent problem using artificial variables and applying the Big-M method to arrive at an optimal solution without artificial variables.
Linear programming is a process used to optimize a linear objective function subject to linear constraints. It can be applied to problems in manufacturing, diets, transportation, allocation and more. Key components include decision variables, constraints, and an objective function. The process involves formulating the problem, identifying variables and constraints, solving using graphical or simplex methods, and interpreting the optimal solution. Linear programming provides a tool for modeling real-world problems mathematically and determining the best outcome.
This document provides an overview of linear programming. It discusses basic and basic feasible solutions, the geometric solution, definitions used in linear programming, and the simplex algorithm. It provides an example problem that is solved over multiple iterations using the simplex algorithm to find the optimal solution. Finally, it briefly discusses the primal dual relationship between linear programming problems.
Mba i qt unit-1.3_linear programming in omRai University
Linear programming is a technique used by operations managers to allocate scarce resources. It involves defining objectives, constraints, and decision variables to determine the optimal solution. Some common applications in operations management include determining optimal product mix, production levels, ingredient mix, transportation routes, and staff assignments. The steps to formulate a linear programming problem are to define the objective, decision variables, mathematical objective function, constraints, and write the linear program in final form. The optimal solution can be found graphically by plotting the constraints and objective function on a graph to identify the feasible region and optimal point.
- Duality theory states that every linear programming (LP) problem has a corresponding dual problem, and the optimal solutions of the primal and dual problems are related.
- The dual problem is obtained by converting the constraints of the primal to variables and vice versa.
- The dual simplex method starts with an infeasible but optimal solution and moves toward feasibility while maintaining optimality, unlike the regular simplex method which moves from a feasible to optimal solution.
This document discusses duality in linear programming. It defines the dual problem as another linear program systematically constructed from the original or primal problem, such that the optimal solutions of one provide the optimal solutions of the other. The document provides rules for constructing the dual problem based on whether the primal problem is a maximization or minimization problem. It also gives examples of writing the dual of a primal problem and solving both problems to verify the optimal objective values are equal. Finally, it discusses economic interpretations of duality and the relationship between primal and dual problems and solutions.
The document introduces slack variables, surplus variables, and artificial variables. Slack variables are added to ≤ constraints to convert them to equations. Surplus variables are subtracted from ≥ constraints. Artificial variables are added to = and ≥ constraints to satisfy non-negativity conditions. The document provides examples of converting linear programming problems to standard form using these variable types.
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 provides examples of constructing the dual problem of a linear programming primal problem and solving it using the two-phase simplex method. It first presents the rules for constructing the dual problem and then works through two examples. The first example derives the dual problem from the primal and solves it using the two-phase method. The second example shows how to find the optimal dual solution given the optimal primal solution using two methods - using the objective coefficients of the primal variables or using the inverse of the primal basic variable matrix.
This document discusses nonlinear programming (NLP) problems. NLP problems involve objective functions and/or constraints that contain nonlinear terms, making them more difficult to solve than linear programs. While exact solutions cannot always be found, algorithms can typically find approximate solutions within an acceptable error range of the optimum. However, for some NLP problems there is no reliable way to find the global maximum, as algorithms may stop at a local maximum instead. The document describes different types of NLP problems and techniques for solving them, including using Excel Solver with multiple starting values to attempt finding the global rather than just local optima.
Transportation Problem In Linear ProgrammingMirza Tanzida
This work is an assignment on the course of 'Mathematics for Decision Making'. I think, it will provide some basic concept about transportation problem in linear programming.
1) The document discusses the Hungarian method for solving assignment problems by finding the optimal assignment of jobs to machines that minimizes costs.
2) It provides examples of using the Hungarian method to solve assignment problems by finding minimum costs in the cost matrix and obtaining a feasible assignment with zero costs.
3) The optimal solution is determined by selecting the assignments indicated by the cells with zero costs in the final cost matrix after applying the Hungarian method steps.
The document summarizes the simplex method for solving linear programming problems. It provides examples to demonstrate how to set up the simplex tableau, choose entering and departing variables at each iteration, and arrive at the optimal solution. The key steps are to rewrite the objective function, convert inequalities to equalities using slack variables, choose pivots to make coefficients zero, and iterate until an optimal basic feasible solution is found.
The document provides an overview of the simplex method for solving linear programming problems. It discusses:
- The simplex method is an iterative algorithm that generates a series of solutions in tabular form called tableaus to find an optimal solution.
- It involves writing the problem in standard form, introducing slack variables, and constructing an initial tableau.
- The method then performs iterations involving selecting a pivot column and row, and applying row operations to generate new tableaus until an optimal solution is found.
- It also discusses how artificial variables are introduced for problems with non-strict inequalities and provides an example solved using the simplex method.
The document provides an overview of the simplex algorithm for solving linear programming problems. It begins with an introduction and defines the standard format for representing linear programs. It then describes the key steps of the simplex algorithm, including setting up the initial simplex tableau, choosing the pivot column and pivot row, and pivoting to move to the next basic feasible solution. It notes that the algorithm terminates when an optimal solution is reached where all entries in the objective row are non-negative. The document also briefly discusses variants like the ellipsoid method and cycling issues addressed by Bland's rule.
This document provides examples and explanations of laws of indices. It includes expressing numbers in index form, writing numbers in index notation, evaluating expressions using laws of indices, and simplifying combinations of indices. Examples range from single term expressions to more complex expressions combining multiple laws of indices. The document aims to teach readers how to manipulate expressions involving indices and apply the laws of indices.
Sensitivity analysis linear programming copyKiran Jadhav
This document discusses sensitivity analysis in linear programming. It begins by defining sensitivity analysis as investigating how changes to a linear programming model's parameters, like objective function coefficients or constraint coefficients, affect the optimal solution. It then discusses the basic parameter changes that can impact the solution, like right-hand side constants or new variables/constraints. The document also covers duality in linear programming and how the dual problem is derived from the primal problem by setting coefficient values to the resource costs at optimality. An example is provided to demonstrate how the dual problem is formulated.
The steps of the simplex method are outlined. Artificial variables are introduced when the initial tableau lacks an identity submatrix. This allows the problem to be solved using the simplex method. The artificial variables are given a large penalty coefficient (-M for maximization) to force them to zero in the optimal solution. The example problem is converted to standard form and artificial variables are added, allowing it to be solved by the simplex method.
The Simplex Method is an algorithm for solving linear programming problems. It involves setting up the problem in standard form, constructing an initial simplex tableau, and then iteratively selecting pivot columns and performing row operations until an optimal solution is found. The method terminates when all indicators in the tableau are positive or zero, at which point the basic and non-basic variables can be identified to read the optimal solution.
The simplex method is a linear programming algorithm that can solve problems with more than two decision variables. It works by generating a series of solutions, called tableaus, where each tableau corresponds to a corner point of the feasible solution space. The algorithm starts at the initial tableau, which corresponds to the origin. It then shifts to adjacent corner points, moving in the direction that optimizes the objective function. This process of generating new tableaus continues until an optimal solution is found.
Artificial variable technique big m method (1)ਮਿਲਨਪ੍ਰੀਤ ਔਜਲਾ
The Big-M method is used to handle artificial variables in linear programming problems. It assigns very large coefficients to the artificial variables in the objective function, making them undesirable to include in optimal solutions. This removes the artificial variables from the basis. As an example, the document presents a linear programming problem to minimize an objective function subject to constraints, and shows the steps of converting it to an equivalent problem using artificial variables and applying the Big-M method to arrive at an optimal solution without artificial variables.
Linear programming is a process used to optimize a linear objective function subject to linear constraints. It can be applied to problems in manufacturing, diets, transportation, allocation and more. Key components include decision variables, constraints, and an objective function. The process involves formulating the problem, identifying variables and constraints, solving using graphical or simplex methods, and interpreting the optimal solution. Linear programming provides a tool for modeling real-world problems mathematically and determining the best outcome.
This document provides an overview of linear programming. It discusses basic and basic feasible solutions, the geometric solution, definitions used in linear programming, and the simplex algorithm. It provides an example problem that is solved over multiple iterations using the simplex algorithm to find the optimal solution. Finally, it briefly discusses the primal dual relationship between linear programming problems.
Mba i qt unit-1.3_linear programming in omRai University
Linear programming is a technique used by operations managers to allocate scarce resources. It involves defining objectives, constraints, and decision variables to determine the optimal solution. Some common applications in operations management include determining optimal product mix, production levels, ingredient mix, transportation routes, and staff assignments. The steps to formulate a linear programming problem are to define the objective, decision variables, mathematical objective function, constraints, and write the linear program in final form. The optimal solution can be found graphically by plotting the constraints and objective function on a graph to identify the feasible region and optimal point.
- Duality theory states that every linear programming (LP) problem has a corresponding dual problem, and the optimal solutions of the primal and dual problems are related.
- The dual problem is obtained by converting the constraints of the primal to variables and vice versa.
- The dual simplex method starts with an infeasible but optimal solution and moves toward feasibility while maintaining optimality, unlike the regular simplex method which moves from a feasible to optimal solution.
This document discusses duality in linear programming. It defines the dual problem as another linear program systematically constructed from the original or primal problem, such that the optimal solutions of one provide the optimal solutions of the other. The document provides rules for constructing the dual problem based on whether the primal problem is a maximization or minimization problem. It also gives examples of writing the dual of a primal problem and solving both problems to verify the optimal objective values are equal. Finally, it discusses economic interpretations of duality and the relationship between primal and dual problems and solutions.
The document introduces slack variables, surplus variables, and artificial variables. Slack variables are added to ≤ constraints to convert them to equations. Surplus variables are subtracted from ≥ constraints. Artificial variables are added to = and ≥ constraints to satisfy non-negativity conditions. The document provides examples of converting linear programming problems to standard form using these variable types.
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 provides examples of constructing the dual problem of a linear programming primal problem and solving it using the two-phase simplex method. It first presents the rules for constructing the dual problem and then works through two examples. The first example derives the dual problem from the primal and solves it using the two-phase method. The second example shows how to find the optimal dual solution given the optimal primal solution using two methods - using the objective coefficients of the primal variables or using the inverse of the primal basic variable matrix.
This document discusses nonlinear programming (NLP) problems. NLP problems involve objective functions and/or constraints that contain nonlinear terms, making them more difficult to solve than linear programs. While exact solutions cannot always be found, algorithms can typically find approximate solutions within an acceptable error range of the optimum. However, for some NLP problems there is no reliable way to find the global maximum, as algorithms may stop at a local maximum instead. The document describes different types of NLP problems and techniques for solving them, including using Excel Solver with multiple starting values to attempt finding the global rather than just local optima.
Transportation Problem In Linear ProgrammingMirza Tanzida
This work is an assignment on the course of 'Mathematics for Decision Making'. I think, it will provide some basic concept about transportation problem in linear programming.
1) The document discusses the Hungarian method for solving assignment problems by finding the optimal assignment of jobs to machines that minimizes costs.
2) It provides examples of using the Hungarian method to solve assignment problems by finding minimum costs in the cost matrix and obtaining a feasible assignment with zero costs.
3) The optimal solution is determined by selecting the assignments indicated by the cells with zero costs in the final cost matrix after applying the Hungarian method steps.
The document summarizes the simplex method for solving linear programming problems. It provides examples to demonstrate how to set up the simplex tableau, choose entering and departing variables at each iteration, and arrive at the optimal solution. The key steps are to rewrite the objective function, convert inequalities to equalities using slack variables, choose pivots to make coefficients zero, and iterate until an optimal basic feasible solution is found.
The document provides an overview of the simplex method for solving linear programming problems. It discusses:
- The simplex method is an iterative algorithm that generates a series of solutions in tabular form called tableaus to find an optimal solution.
- It involves writing the problem in standard form, introducing slack variables, and constructing an initial tableau.
- The method then performs iterations involving selecting a pivot column and row, and applying row operations to generate new tableaus until an optimal solution is found.
- It also discusses how artificial variables are introduced for problems with non-strict inequalities and provides an example solved using the simplex method.
The document provides an overview of the simplex algorithm for solving linear programming problems. It begins with an introduction and defines the standard format for representing linear programs. It then describes the key steps of the simplex algorithm, including setting up the initial simplex tableau, choosing the pivot column and pivot row, and pivoting to move to the next basic feasible solution. It notes that the algorithm terminates when an optimal solution is reached where all entries in the objective row are non-negative. The document also briefly discusses variants like the ellipsoid method and cycling issues addressed by Bland's rule.
This document provides examples and explanations of laws of indices. It includes expressing numbers in index form, writing numbers in index notation, evaluating expressions using laws of indices, and simplifying combinations of indices. Examples range from single term expressions to more complex expressions combining multiple laws of indices. The document aims to teach readers how to manipulate expressions involving indices and apply the laws of indices.
This document outlines José Cupertino Ruiz Vargas's PhD thesis on searching for diboson resonances in CMS data. It begins with an introduction to the standard model of particle physics and motivations for physics beyond the standard model, including the Randall-Sundrum model with extra dimensions. It then describes the CMS detector and object identification techniques. The analysis strategy is to select events with two opposite-sign leptons and two jets, and estimate backgrounds using Monte Carlo simulations and data-driven techniques. Unblinded results show agreement between data and background predictions in control regions.
Ch 07 MATLAB Applications in Chemical Engineering_陳奇中教授教學投影片Chyi-Tsong Chen
The slides of Chapter 7 of the book entitled "MATLAB Applications in Chemical Engineering": Parameter Estimation. Author: Prof. Chyi-Tsong Chen (陳奇中教授); Center for General Education, National Quemoy University; Kinmen, Taiwan; E-mail: chyitsongchen@gmail.com.
Ebook purchase: https://play.google.com/store/books/details/MATLAB_Applications_in_Chemical_Engineering?id=kpxwEAAAQBAJ&hl=en_US&gl=US
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
The document discusses various methods for developing empirical dynamic models from process input-output data, including linear regression and least squares estimation. Simple linear regression can be used to develop steady-state models relating an output variable y to an input variable u. The least squares approach is introduced to calculate the parameter estimates that minimize the error between measured and predicted output values. Graphical methods are also presented for estimating parameters of first-order and second-order dynamic models by fitting step response data. Finally, the development of discrete-time models from continuous-time models using finite difference approximations is covered.
This document discusses high-throughput screening (HTS) workflows for identifying biologically active small molecules. It describes how robots are used to rapidly screen large libraries of compounds in assays and generate large datasets. Statistical and machine learning methods in R can then be used to build predictive models from these datasets to identify promising leads and guide the screening of additional compounds. Caveats regarding the applicability of models to new chemical spaces are also discussed.
This document outlines progress on Chapter III of an MSE for managing the Patagonian Toothfish. The goals are to explore the impact of model misspecification using a spatially-structured population model, and implement an MSE process. Next steps include conditioning the operating model on available data, designing harvest control rules and performance metrics, and evaluating management strategies under different scenarios and uncertainties. Several harvest control rules based on fishing mortality and catch are presented, along with preliminary results on their performance in depleting biomass, maintaining biomass above target levels, and balancing risk against catch. Further work is needed to explore tradeoffs between performance measures under different sources of uncertainty.
The document discusses a case study involving the evaluation of a measurement system for an important quality variable, CTQ1, at W.R. Grace. A measurement systems analysis (MSA) study was conducted involving the four worldwide sites that produce the raw material. The results showed a high %GR&R of 94.3% and P/T ratio of 116%, indicating significant measurement error. When analyzed separately, the sites showed varying levels of measurement capability, with one site having a %GR&R of 38.9%. The MSA study identified opportunities to improve the measurement system and link it back to process improvements.
SBMF provides a scalable approach to Bayesian matrix factorization. It uses Gibbs sampling for inference in a probabilistic matrix factorization model, with univariate Gaussian priors over the latent factors. This allows SBMF to have linear time and space complexity, unlike BPMF which has cubic time complexity. Experiments on movie rating datasets show SBMF achieves similar predictive performance to BPMF, while being significantly faster, especially for higher-dimensional latent spaces. SBMF provides a more scalable alternative to Bayesian matrix factorization.
The document describes extensions made to the CADET-CS chromatography model solver. Radial discretization was improved using a higher order WENO scheme, achieving second order convergence. A surface diffusion model was added, increasing the bandwidth of the Jacobian matrix. A self-association isotherm model was also included to model protein dimerization. Sensitivity analysis was performed using algorithmic differentiation for accuracy. An experimental design study used Monte Carlo simulation to determine optimal experiments for parameter estimation under measurement noise.
International Journal of Computational Engineering Research (IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Application of recursive perturbation approach for multimodal optimizationPranamesh Chakraborty
The document describes the application of the Recursive Perturbation Approach for Multimodal Optimization (RePAMO) algorithm to various classical optimization problems. It summarizes the results of applying RePAMO to constrained Himmelblau, Griewank, Schwefel, Guilin Hills, Six Hump Camel, Rastrigin 3D, Ackley 4D, and Michalewicz 5D functions. For each function, it provides the number of minima and maxima found, the number of function evaluations, and the number of generations. Overall, the algorithm was able to successfully find the optima for all test functions.
Computational tools for drug discoveryEszter Szabó
Discovery of a novel drug is an optimizing challenge against an array of chemical and biological attributes to reach the desired efficacy and safety profile. The immense complexity of the human body combined with the astronomically large druggable chemical space hinders the selection of molecules with such a balanced profile. Therefore, the medicinal chemistry toolbox embraces all computational techniques with predictive power to focus the chemical space to the most promising candidates for synthesis and testing. The diversity includes data analysis tools, physics-based simulations, biological target structure driven or ligand structure based approaches [1-3]. While the size of the compound collections vary from a couple of close analogues up to billions of virtual compounds to process[4]. This presentation will highlight general concepts and techniques applied in computer aided drug design, focusing on data and ligand based computational chemistry approaches and showcase solutions developed by ChemAxon.
[1] Gisbert Schneider, David E Clark, Angew Chem Int Ed Engl. 2019, 5;58(32):10792-10803.
[2] John G Cumming, Andrew M Davis, Sorel Muresan, Markus Haeberlein, Hongming Chen, Nat Rev Drug Discov, 2013, 12(12):948-62.
[3] Yu-Chen Lo, Stefano E Rensi, Wen Torng, Russ B Altman, Drug Discov Today 2018, 23(8):1538-1546
[4] Torsten Hoffmanm, Marcus Gastreich, Drug Discov Today, 2019, 24(5):1148-1156.
The document provides solutions to calculating various statistical measures - arithmetic mean, median, mode, harmonic mean, and geometric mean - for 5 sets of data. For each data set, the document calculates the measures using the relevant formulas. The statistical measures included arithmetic mean, median, mode, harmonic mean, and geometric mean. Formulas are provided for calculating each measure.
This document summarizes a paper on Cold-Start Reinforcement Learning with Softmax Policy Gradient. It introduces the limitations of existing sequence learning methods like maximum likelihood estimation and reward augmented maximum likelihood. It then describes the softmax policy gradient method which uses a softmax value function to overcome issues with warm starts and sample variance. The method achieves better performance on text summarization and image captioning tasks.
The document proposes a new hybrid conjugate gradient method called SW-A that combines the WYL and AMRI conjugate gradient methods. It presents the algorithm for SW-A and evaluates its performance on 18 standard unconstrained optimization test functions compared to WYL and AMRI in terms of number of iterations and CPU time. The results show that SW-A is able to solve all test problems while WYL solves 97% and AMRI solves 95%, demonstrating the effectiveness of the new hybrid method.
Adaptive Constraint Handling and Success History Differential Evolution for C...University of Maribor
Talk given in: 2017 IEEE Congress on Evolutionary Computation (CEC), taking place at Donostia - San Sebastian, Spain, June 5-8, 2017. Associated special session at CEC: Associated with Competition on Bound Constrained Single Objective Numerical Optimization III (June 6, 14:30-16:30, Room 4).
The document discusses rolling contact bearings. It begins by defining bearings and their purpose of supporting loads while permitting relative motion. It then discusses the different types of rolling contact bearings, including deep groove ball bearings, angular contact bearings, cylindrical roller bearings, taper roller bearings, and self-aligning bearings. The document also covers bearing materials, static load capacity, and Stribeck's equation for calculating static load capacity.
Operation research unit 3 Transportation problemDr. L K Bhagi
Formulation of Transportation Problem, Initial Feasible Solution
Methods, Degeneracy in Transport Problem and Optimality Test (Modi Method and stepping stone Method)
Operation research unit 2 Duality and methodsDr. L K Bhagi
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can calm the mind and body by lowering heart rate and blood pressure. Meditation may also have psychological benefits like improving mood and reducing rumination.
MEC395 Measurement System Analysis (MSA)Dr. L K Bhagi
Discussed SPC, variable Gauge R&R, Repeatability and Reproducibility with Examples calculation of variable Gauge R&R, Bias, Linearity and Stability with examples.
Sheet Metal Working, Temperature and sheet metal forming, Applications Sheet Metal Parts, Categories of sheet metal processes, Shearing, stages in shearing action, Punch and Die Sizes, Sheet Metal Bending
Eco-industrial park and cleaner productionDr. L K Bhagi
1. Industrial ecology is the study of material and energy flows through industrial systems.
2. It takes a multidisciplinary approach and examines issues from perspectives involving the environment, society, economics, and technology to promote sustainable development.
3. The goal is to shift industrial processes from linear open loop systems that produce waste, to closed loop systems where wastes can be used as inputs for new processes.
The document contains a series of questions and answers related to gears and gear design. It discusses topics like tooth interference, torque transmission ratios, speed reductions, minimum number of teeth, center distance calculations, and stress analysis. For each question, the relevant concepts and equations are explained to arrive at the solution. Gear terminology and relationships between different gear types and shaft arrangements are also covered.
The document discusses helical gears. Some key points:
- Helical gears have teeth cut at an angle (helix angle) ranging usually between 15-30 degrees, compared to spur gears which have straight teeth parallel to the shaft axis.
- Helical gears can be parallel, crossed, or herringbone. Herringbone gears cancel thrust loads by using two sets of teeth with opposite hands.
- Helical gears carry more load than equivalent spur gears because the teeth act over a larger effective area due to the helix angle. However, efficiency is lower for helical gears due to increased sliding contact.
- Additional geometry considerations are required for helical gears, including normal and transverse pit
Introduction to casting, Major classifications of casting, Casting terminology, Characteristics of molding sand, Constituents of foundry sand, Patterns and their types, Cores and types of cores, Gating system, Types of gates, Solidification, Riser system, Types of riser, Types of allowances, Directional Solidification, Defects in casting, Riser design(Chvorinov's rules), Advanced casting techniques:Shell molding, Permanent mould casting, Vacuum die casting, Low pressure die casting, Continuous casting, Squeeze casting, Slush casting, Vacuum casting, Die Casting, Centrifugal casting, Investment casting
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Design of Flat belt, V belt and chain drivesDr. L K Bhagi
Geometrical relationships, Analysis of belt tensions, Condition for maximum power transmission, Characteristics of belt drives, Selection of flat belt, V- belt, Selection of V belt, Roller chains, Geometrical relationship, Polygonal effect, Power rating of roller chains, Design of chain drive, Introduction to belt drives and belt construction, Introduction to chain drives
Springs - DESIGN OF MACHINE ELEMENTS-IIDr. L K Bhagi
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General introduction to manufacturing processesDr. L K Bhagi
Manufacturing processes definition, Classification of manufacturing processes, Typical examples of applications, Manufacturing capability, Selection of materials, Selection of manufacturing process
This document is a series of lecture slides about sheet metal working and bending processes. It discusses topics like mechanics of sheet metal bending, bend allowance, numerical problems calculating blank size and bending force, springback and methods to eliminate it, including overbending and stretch forming. It also covers drawing as a sheet metal forming operation used to make cup-shaped or complex curved parts by pushing metal into a die cavity with a punch.
Press tool operations, Shearing action, Shear operations, Numerical problems, Drawing, Draw die design, Spinning, Bending, Stretch forming, Embossing and coining, Types of sheet metal dies, Analysis of sheet metal
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
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The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
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Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
6. 6
Zmin= 6X+8Y
X+Y ≥ 10
2X +3Y ≥ 25
X +5Y ≥ 35
X, Y ≥ 0
Add slack Surplus
variable to convert
constraint equation into
equality
Z = 6X+8Y+0S1 − 0S2
− 0S3
X+Y−S1 =10
2X +3Y−S2 = 25
X +5Y − S3 = 35
X,Y, S1, S2, S3 ≥ 0
Operation Research Models
LPP Big M Method – Prob 7
7. 7
Ibfs (Initial Basic Feasible Solution)
DV = 0
−S =10 ; −S2 = 25; − S3 = 35 0r
S = − 10 ; S2 = − 25; S3 = − 35 0r
Z = 6X+8Y+0S1 − 0S2 − 0S3
X+Y−S1 =10
2X +3Y−S2 = 25
X +5Y − S3 = 35
X,Y, S1, S2, S3 ≥ 0
Operation Research Models
LPP Big M Method – Prob 7
8. 8
Ibfs (Initial Basic Feasible Solution)
DV = 0 and Surplus = 0
0 =10 ; 0 = 25; 0 = 35
Z = 6X+8Y+0S1 − 0S2 − 0S3
X+Y−S1 =10
2X +3Y−S2 = 25
X +5Y − S3 = 35
X,Y, S1, S2, S3 ≥ 0
Operation Research Models
LPP Big M Method – Prob 7
9. 9
X+Y ≥ 10
2X +3Y ≥ 25
X +5Y ≥ 35
X, Y ≥ 0
Along with Surplus
variable add Artificial
Variable to convert
constraint equation into
equality
X+Y−S1 +A1 =10
2X +3Y−S2+A2 = 25
X +5Y − S3 +A3 = 35
X,Y, S1, S2, S3, A1, A2, A3
≥ 0
Operation Research Models
LPP Big M Method – Prob 7
Zmin =
6X+8Y+0S1+0S2+ 0S3+MA1+MA2+MA3
Zmin=
6X+8Y
10. 10
Operation Research Models
LPP Big M Method – Prob 7
Zmin =
6X+8Y+0S1+0S2+ 0S3+MA1+MA2+MA3
Ibfs (Initial Basic Feasible Solution)
Put DV = 0 and Surplus variables = 0
A1=10 ;
A2= 25;
A3= 35
11. 11
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M M
θ
X Y S1 S2 S3 A1 A2 A3 RHS
(b)
M
M
M
A1
A2
A3
10
25
35
12. 12
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M M
θ
X Y S1 S2 S3 A1 A2 A3 RHS
(b)
M
M
M
A1
A2
A3
1
2
1
1
3
5
−1
0
0
0
−1
0
0
0
−1
1
0
0
0
1
0
0
0
1
10
25
35
13. 13
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M M
θ
X Y S1 S2 S3 A
1
A
2
A3 RHS
(b)
M
M
M
A1
A2
A3
1
2
1
1
3
5
−1
0
0
0
−1
0
0
0
−1
1
0
0
0
1
0
0
0
1
10
25
35
Zj
4
M
9
M
−M −M −M M M M
M+2M+M = 4M
14. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
14
https://www.youtube.com/watch?v=tRNwz
Sr9IXg
IITM Srinivasan sir
15. 15
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M M
θ
X Y S1 S2 S3 A
1
A
2
A3 RHS
(b)
M
M
M
A1
A2
A3
1
2
1
1
3
5
−1
0
0
0
−1
0
0
0
−1
1
0
0
0
1
0
0
0
1
10
25
35
10
25/3
7
Zj 4M 9M −M −M −M M M M
Zj− Cj 4M−
6
9M−
8
−M −M −M 0 0 0
Largest
positive
value Key Column:
decides incoming
Least
positive
Key
Row: decides
outgoing
variable
16. 16
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M M
θ
X Y S1 S2 S3 A
1
A
2
A3 RHS
(b)
M
M
M
A1
A2
A3
1
2
1
1
3
5
−1
0
0
0
−1
0
0
0
−1
1
0
0
0
1
0
0
0
1
10
25
35
10
25/3
7
Zj 4M 9M −M −M −M M M M
Zj− Cj 4M−
6
9M−
8
−M −M −M 0 0 0
Key Column:
decides incoming
Least
positive
Key
Row: decides
outgoing
variable
Key
Element
Largest
positive
value
17. 17
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M M
θ
X Y S1 S2 S3 A
1
A
2
A3 RHS
(b)
M
M
M
A1
A2
A3
1
2
1
1
3
5
−1
0
0
0
−1
0
0
0
−1
1
0
0
0
1
0
0
0
1
10
25
35
10
25/3
7
Zj 4M 9M −M −M −M M M M
Zj− Cj 4M−
6
9M−
8
−M −M −M 0 0 0
18. 18
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M M
θ
X Y S1 S2 S3 A
1
A
2
A3 RHS
(b)
M
M
M
A1
A2
A3
1
2
1
1
3
5
−1
0
0
0
−1
0
0
0
−1
1
0
0
0
1
0
0
0
1
10
25
35
10
25/3
7
Zj 4M 9M −M −M −M M M M
Zj− Cj 4M−
6
9M−
8
−M −M −M 0 0 0
19. 19
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
1
2
1/5
1
3
1
−1
0
0
0
−1
0
0
0
−1/5
1
0
0
0
1
0
10
25
7
Zj
Zj− Cj
Modifie
d key
row
20. 20
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
1
2
1/5
1
3
1
−1
0
0
0
−1
0
0
0
−1/5
1
0
0
0
1
0
10
25
7
Zj
Zj− Cj
Modifie
d key
row
22. 22
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
4/5
2
1/5
0
3
1
−1
0
0
0
−1
0
1/5
0
−1/5
1
0
0
0
1
0
3
25
7
Zj
Zj− Cj
MKR
NR
23. 23
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
4/5
2
1/5
0
3
1
−1
0
0
0
−1
0
1/5
0
−1/5
1
0
0
0
1
0
3
25
7
Zj
Zj− Cj
MKR
NR
OR
25. 25
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
4/5
7/5
1/5
0
0
1
−1
0
0
0
−1
0
1/5
3/5
−1/5
1
0
0
0
1
0
3
4
7
Zj
Zj− Cj
MKR
NR
NR
26. 26
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
4/5
7/5
1/5
0
0
1
−1
0
0
0
−1
0
1/5
3/5
−1/5
1
0
0
0
1
0
3
4
7
Zj 11𝑀
5
+
8
5
8 −M −M 4𝑀
5
−
8
5
M M
Zj− Cj
27. 27
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
4/5
7/5
1/5
0
0
1
−1
0
0
0
−1
0
1/5
3/5
−1/5
1
0
0
0
1
0
3
4
7
Zj 11𝑀
5
+
8
5
8 −M −M 4𝑀
5
−
8
5
M M
Zj− Cj 11𝑀
5
+
38
5
0 −M −M 4𝑀
5
−
8
5
0 0
28. 28
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
4/5
7/5
1/5
0
0
1
−1
0
0
0
−1
0
1/5
3/5
−1/5
1
0
0
0
1
0
3
4
7
Zj 11𝑀
5
+
8
5
8 −M −M 4𝑀
5
−
8
5
M M
Zj− Cj 11𝑀
5
+
38
5
0 −M −M 4𝑀
5
−
8
5
0 0
Key Column:
decides incoming
Largest
positive
value
29. 29
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M M
θ
X Y S1 S2 S3 A
1
A2 RHS
(b)
M
M
8
A1
A2
Y
4/5
7/5
1/5
0
0
1
−1
0
0
0
−1
0
1/5
3/5
−1/5
1
0
0
0
1
0
3
4
7
15/4
20/7
35
Zj 11𝑀
5
+
8
5
8 −M −M 4𝑀
5
−
8
5
M M
Zj− Cj 11𝑀
5
+
38
5
0 −M −M 4𝑀
5
−
8
5
0 0
Key Column:
decides incoming
Least
positive
Key
Row: decides
outgoing
variable
Largest
positive
value
30. 30
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A
1
RHS
(b)
M
6
8
A1
X
Y
4/5
7/5
1/5
0
0
1
−1
0
0
0
−1
0
1/5
3/5
−1/5
1
0
0
3
4
7
15/4
20/7
35
Zj 11𝑀
5
+
8
5
8 −M −M 4𝑀
5
−
8
5
M
Zj− Cj 11𝑀
5
+
38
5
0 −M −M 4𝑀
5
−
8
5
0 0
31. 31
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A
1
RHS
(b)
M
6
8
A1
X
Y
4/5
7/5
1/5
0
0
1
−1
0
0
0
−1
0
1/5
3/5
−1/5
1
0
0
3
4
7
Zj
Zj− Cj
32. 32
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A
1
RHS
(b)
M
6
8
A1
X
Y
4/5
1
1/5
0
0
1
−1
0
0
0
−5/7
0
1/5
3/7
−1/5
1
0
0
3
20/7
7
Zj
Zj− Cj
MKR
33. 33
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A
1
RHS
(b)
M
6
8
A1
X
Y
4/5
1
1/5
0
0
1
−1
0
0
0
−5/7
0
1/5
3/7
−1/5
1
0
0
3
20/7
7
Zj
Zj− Cj
MKR
35. 35
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A
1
RHS
(b)
M
6
8
A
1
X
Y
0
1
1/5
0
0
1
−1
0
0
4/7
−5/7
0
−1/7
3/7
−1/5
1
0
0
5/7
20/7
7
Zj
Zj
− Cj
MKR
NR
37. 37
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A1 RHS
(b)
M
6
8
A1
X
Y
0
1
0
0
0
1
−1
0
0
4/7
−5/7
1/7
−1/7
3/7
−2/7
1
0
0
5/7
20/7
45/7
Zj
Zj− Cj
MKR
NR
NR
38. 38
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A1 RHS
(b)
M
6
8
A1
X
Y
0
1
0
0
0
1
−1
0
0
4/7
−5/7
1/7
−1/7
3/7
−2/7
1
0
0
5/7
20/7
45/7
Zj 6 8 −M
4𝑀
7
−
22
7
−
𝑀
7
+
2
7
M
Zj− Cj
MKR
NR
NR
39. 39
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A1 RHS
(b)
M
6
8
A1
X
Y
0
1
0
0
0
1
−1
0
0
4/7
−5/7
1/7
−1/7
3/7
−2/7
1
0
0
5/7
20/7
45/7
Zj
6 8 −M
4𝑀
7
−
22
7
−
𝑀
7
+
2
7
M
Zj− Cj
0 0 −M 4𝑀
7
−
22
7
−
𝑀
7
+
2
7
0
Key Column: decides
incoming variable
Largest
positive
value
40. 40
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0 M
θ
X Y S1 S2 S3 A1 RHS
(b)
M
6
8
A1
X
Y
0
1
0
0
0
1
−
1
0
0
4/7
−5/7
1/7
−1/7
3/7
−2/7
1
0
0
5/7
20/7
45/7
5/4
−4
45
Zj
6 8 −M
4𝑀
7
−
22
7
−
𝑀
7
+
2
7
M
Zj− Cj
0 0 −M 4𝑀
7
−
22
7
−
𝑀
7
+
2
7
0
Key Column:
decides incoming
Largest
positive
value
Least
positive
Key
Row: decides
outgoing
variable
Key
Element
41. 41
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0
θ
X Y S1 S2 S3 RHS
(b)
0
6
8
S2
X
Y
0
1
0
0
0
1
−
1
0
0
4/7
−5/7
1/7
−1/7
3/7
−2/7
5/7
20/7
45/7
Zj
Zj− Cj
42. 42
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0
θ
X Y S1 S2 S3 RHS
(b)
0
6
8
S2
X
Y
0
1
0
0
0
1
−
1
0
0
4/7
−5/7
1/7
−1/7
3/7
−2/7
5/7
20/7
45/7
Zj
Zj− Cj
43. 43
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0
θ
X Y S1 S2 S3 RHS
(b)
0
6
8
S2
X
Y
0
1
0
0
0
1
−7/4
0
0
1
−5/7
1/7
−1/4
3/7
−2/7
5/4
20/7
45/7
Zj
Zj− Cj
MKR
44. 44
Operation Research Models
LPP Big M Method – Prob 7
Cj
6 8 0 0 0
θ
X Y S1 S2 S3 RHS
(b)
0
6
8
S2
X
Y
0
1
0
0
0
1
−7/4
0
0
1
−5/7
1/7
−1/4
3/7
−2/7
5/4
20/7
45/7
Zj
Zj− Cj
MKR
OR
62. 62
Operation Research Models
LPP Big M Method – Prob 8
Cj
4 1 0 0 M M
θ
𝑥1 𝑥2 S1 S2 A
1
A
2
RHS
(b)
M
M
A1
A2
3
1
4
5
−1
0
0
−1
1
0
0
1
20
15
Zj
63. 63
Operation Research Models
LPP Big M Method – Prob 8
Cj
4 1 0 0 M M
θ
𝑥1 𝑥2 S1 S2 A
1
A
2
RHS
(b)
M
M
A1
A2
3
1
4
5
−1
0
0
−1
1
0
0
1
20
15
Zj
4
M
9
M
−M −M M M
64. 64
Operation Research Models
LPP Big M Method – Prob 8
Cj
4 1 0 0 M M
θ
𝑥1 𝑥2 S1 S2 A
1
A
2
RHS
(b)
M
M
A1
A2
3
1
4
5
−1
0
0
−1
1
0
0
1
20
15
5
3
Zj
4M 9M −M −M M M
Zj−Cj 4M−4 9M−1 −M −M 0 0
Max
positive
value
Key Column:
decides incoming
Least
positive
Key
Row: decides
outgoing
variable
Key
Element
65. 65
Operation Research Models
LPP Big M Method – Prob 8
Cj
4 1 0 0 M
θ
𝑥1 𝑥2 S1 S2 A
1
RHS
(b)
M
1
A1
𝑥2
3
1
4
5
−1
0
0
−1
1
0
0
15
Zj
Zj−Cj
66. 66
Operation Research Models
LPP Big M Method – Prob 8
Cj
4 1 0 0 M
θ
𝑥1 𝑥2 S1 S2 A
1
RHS
(b)
M
1
A1
𝑥2
3
1/5
4
1
−1
0
0
−1/5
1
0
20
15/5=3
Zj
Zj−Cj
Old Row
MKR
109. 109
Operation Research Models
LPP Big M Method – Prob 9
Cj
1 5 0 0
θ =
𝒃
𝒂
𝑥1 𝒙𝟐 S1 S2 RHS
(b)
0
5
S2
𝒙𝟐
5/4
3/4
0
1
3/4
1/4
1
0
5/2
3/2
Zj
Cj − Zj
MKR
NR
110. 110
Operation Research Models
LPP Big M Method – Prob 9
Cj
1 5 0 0
θ =
𝒃
𝒂
𝑥1 𝒙𝟐 S1 S2 RHS
(b)
0
5
S2
𝒙𝟐
5/4
3/4
0
1
3/4
1/4
1
0
5/2
3/2
Zj
15/4 5 5/4 0
Cj − Zj
−11/4 0 −5/4 0
MKR
NR
So, the optimal Solution we get without
𝑥1
The optimal Solution
𝑥1 = 0; 𝑥2 =
3
2
and 𝑍𝑚𝑎𝑥 = 𝑥1 + 5𝑥2 = 0 +
15
2
, 𝑍𝑚𝑎𝑥 =
15
2
111. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
111
112. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
112
113. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
113
114. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
114
115. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
115
There is no region that satisfies both the
constraints.
This LP is infeasible
Refer: Slide no. 97
117. 117
Operation Research Models
LPP Two Phase Method
LPP using Graphical
Method
LPP using iteration Method
≤
inequality
= and ≥
inequality
Simplex
Method
Big M
Method
Two Phase
Method
118. 118
Operation Research Models
LPP Two Phase Method
In Two Phase Method, the whole procedure of solving a linear
programming problem (LPP) involving artificial variables is
divided into two phases.
In phase I, we form a new objective function (Auxiliary
function) by assigning zero to all variable (slack and surplus
variables) and +1 or −1 to each of the artificial variables
𝑍𝑚𝑖𝑛 or 𝑍𝑚𝑎𝑥 . Then we try to eliminate the artificial variables
from the basis. The solution at the end of phase I serves as a
basic feasible solution for phase II.
In phase II, Auxiliary function is replaced by the original
objective function and the usual simplex algorithm is used to
find an optimal solution.
140. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
140
The objective function and constraints are functions of two types of variables,
_______________ variables and ____________ variables.
A. Positive and negative
B. Controllable and uncontrollable.
C. Strong and weak
D. None of the above
141. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
141
In graphical representation the bounded region is known as _________ region.
A. Solution
B. basic solution
C. feasible solution
D. optimal
142. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
142
The optimal value of the objective function for the following L.P.P.
Max z = 4X1 + 3X2
subject to
X1 + X2 ≤ 50
X1 + 2X2 ≤ 80
2X1 + X2 ≥ 20
X1, X2 ≥ 0
is
(a) 200
(b) 330
(c) 420
(d) 500
143. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
143
To formulate a problem for solution by the Simplex method, we must add artificial
variable to
(a) only equality constraints
(b) only LHS is greater than equal to RHS constraints
(c) both (a) and (b)
(d) None of the above
144. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
144
In graphical method of LPP, If at all there is a feasible solution (feasible area of polygon)
exists then, the feasible area region has an important property known as ____________in
geometry
a) convexity Property
b) Convex polygon
c) Both of the above
d) None of the above
145. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
145
146. Dr. L K Bhagi, School of Mechanical
Engineering, LPU
146
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