A short tutorial on Weighted Sum Method for decision making. This tutorial is a part of "Introduction to MCDM".The tutorial introduces the procedure to identify better option among many alternatives.
This document provides an overview of optimization techniques. It defines optimization as identifying variable values that minimize or maximize an objective function subject to constraints. It then discusses various applications of optimization in finance, engineering, and data modeling. The document outlines different types of optimization problems and algorithms. It provides examples of unconstrained optimization algorithms like gradient descent, conjugate gradient, Newton's method, and BFGS. It also discusses the Nelder-Mead simplex algorithm for constrained optimization and compares the performance of these algorithms on sample problems.
Introduction to Decision Making
MULTI CRITERIA DECISION MAKING
STEPS IN A TYPICAL MCDM PROCESS
Popularity of Different MCDM Methods
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
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.
This document discusses various multi-criteria decision making (MCDM) methods. It describes the objectives and steps in MCDM methodology. Three MCDM methods are explained in detail: Compromise Programming (CP), Preference Ranking Organisation METHod of Enrichment Evaluation (PROMETHEE), and the Weighted Average Method. An example is provided to illustrate the application of each method.
Metaheuristic Algorithms: A Critical AnalysisXin-She Yang
The document discusses metaheuristic algorithms and their application to optimization problems. It provides an overview of several nature-inspired algorithms including particle swarm optimization, firefly algorithm, harmony search, and cuckoo search. It describes how these algorithms were inspired by natural phenomena like swarming behavior, flashing fireflies, and bird breeding. The document also discusses applications of these algorithms to engineering design problems like pressure vessel design and gear box design optimization.
A simple introduction about the Weight Product Method with an example.After going through this tutorial you can apply this method in simple decision making.
This document provides an overview of optimization techniques. It defines optimization as identifying variable values that minimize or maximize an objective function subject to constraints. It then discusses various applications of optimization in finance, engineering, and data modeling. The document outlines different types of optimization problems and algorithms. It provides examples of unconstrained optimization algorithms like gradient descent, conjugate gradient, Newton's method, and BFGS. It also discusses the Nelder-Mead simplex algorithm for constrained optimization and compares the performance of these algorithms on sample problems.
Introduction to Decision Making
MULTI CRITERIA DECISION MAKING
STEPS IN A TYPICAL MCDM PROCESS
Popularity of Different MCDM Methods
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
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.
This document discusses various multi-criteria decision making (MCDM) methods. It describes the objectives and steps in MCDM methodology. Three MCDM methods are explained in detail: Compromise Programming (CP), Preference Ranking Organisation METHod of Enrichment Evaluation (PROMETHEE), and the Weighted Average Method. An example is provided to illustrate the application of each method.
Metaheuristic Algorithms: A Critical AnalysisXin-She Yang
The document discusses metaheuristic algorithms and their application to optimization problems. It provides an overview of several nature-inspired algorithms including particle swarm optimization, firefly algorithm, harmony search, and cuckoo search. It describes how these algorithms were inspired by natural phenomena like swarming behavior, flashing fireflies, and bird breeding. The document also discusses applications of these algorithms to engineering design problems like pressure vessel design and gear box design optimization.
A simple introduction about the Weight Product Method with an example.After going through this tutorial you can apply this method in simple decision making.
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.
Tabu search is a metaheuristic technique that guides a local search procedure to explore the solution space beyond local optimality. It uses flexible memory-based processes to escape the trap of cycling. Particle swarm optimization is a swarm intelligence technique inspired by bird flocking where potential solutions fly through hyperspace to find optimal regions. Ant colony optimization is another swarm intelligence technique inspired by how ants find food, where artificial ants cooperate to find good solutions.
This document introduces statistical notation used in formulas. It discusses how variables are designated using letters like X, Y, Z. It also covers notation for sample size (N), individual scores (X1, X2), group numbers (n1, n2), and other concepts. Key points introduced are means (X-bar) vs parameters (mu), standard deviations (s) vs parameters (sigma), and correlations (rXY).
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.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
The document describes the Analytic Hierarchy Process (AHP), which is a structured technique for organizing and analyzing complex decisions. AHP involves constructing a hierarchy of criteria and alternatives, then making pairwise comparisons between elements to determine their relative importance. These comparisons are used to calculate weights for criteria and priorities for alternatives. The document provides an example of using AHP to select a car based on style, reliability, and fuel economy criteria. It also outlines the steps to determine criteria weights, alternative priorities, and consistency ratios in AHP.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Nature-Inspired Metaheuristic AlgorithmsXin-She Yang
This chapter introduces optimization problems and nature-inspired metaheuristics. Optimization problems involve minimizing or maximizing objective functions subject to constraints. Nature-inspired metaheuristics are computational algorithms inspired by natural phenomena, such as simulated annealing, genetic algorithms, particle swarm optimization, and ant colony optimization. They provide near-optimal solutions to complex optimization problems.
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document provides an introduction to genetic algorithms. It describes genetic algorithms as probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance. The key components of a genetic algorithm are described, including encoding solutions, initializing a population, selecting parents, applying genetic operators like crossover and mutation, evaluating fitness, and establishing termination criteria. An example problem of maximizing binary string ones is used to illustrate how a genetic algorithm works over multiple generations.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
This document provides an overview of design of experiments (DOE). It defines DOE as a set of statistical tools used to plan, execute, analyze, and interpret controlled tests to determine which factors impact process outcomes. The document discusses different types of experimental designs like full factorial and fractional factorial designs. It provides an example to illustrate how a two-factor factorial design can be used to study the effects of driver type and ball type on golf score. The benefits of DOE are identified as being able to determine main effects, interactions, and optimal variable settings. The importance of understanding DOE is explained as it allows choosing between alternatives, identifying key factors, and modeling processes.
20060411 Analytic Hierarchy Process (AHP)Will Shen
The Analytic Hierarchy Process (AHP) is a decision-making tool that breaks down complex decisions into a series of pairwise comparisons. It allows decision makers to incorporate both qualitative and quantitative factors. The AHP works by:
1) Computing weights for each decision criterion through pairwise comparisons.
2) Scoring alternatives based on each criterion.
3) Multiplying the weights and scores to obtain overall scores for each alternative.
4) Ranking the alternatives based on their overall scores. Consistency is also checked to ensure reliable results. An example is provided to illustrate the AHP process.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
Multi-Criteria Decision Making (MCDM) techniques can be used to evaluate alternatives based on multiple conflicting criteria. The document discusses various MCDM techniques including weighted score method, TOPSIS, and Analytic Hierarchy Process (AHP). Case studies are presented to demonstrate how to apply these techniques to select optimal construction materials and technologies by considering criteria such as strength, durability, cost, and environmental impact.
This document discusses cost-benefit analysis and cost-effectiveness analysis as methods to evaluate programs by comparing costs and benefits. It explains that CBA monetizes both costs and benefits, while CEA only monetizes costs and expresses benefits in non-monetary units. The document also discusses undertaking these analyses from different perspectives - social, government, and individual - and notes strengths like evaluating net worth but weaknesses like attribution and monetizing all impacts.
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document discusses the Analytic Hierarchy Process (AHP), a multiple criteria decision making (MCDM) method originally developed by Thomas L. Saaty. AHP breaks down a complex decision problem into a hierarchy, then uses pairwise comparisons to derive priority scales for ranking the decision alternatives. The summary is:
1. AHP allows decision makers to structure multiple conflicting criteria into a hierarchy and then use pairwise comparisons to determine criteria weights and alternative rankings.
2. It is a 3-stage process of building a hierarchy, determining priorities through pairwise comparisons, and checking consistency.
3. While useful for qualitative and quantitative decisions, AHP has limitations like difficulty adding/removing criteria and assumptions of perfect consistency
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.
Tabu search is a metaheuristic technique that guides a local search procedure to explore the solution space beyond local optimality. It uses flexible memory-based processes to escape the trap of cycling. Particle swarm optimization is a swarm intelligence technique inspired by bird flocking where potential solutions fly through hyperspace to find optimal regions. Ant colony optimization is another swarm intelligence technique inspired by how ants find food, where artificial ants cooperate to find good solutions.
This document introduces statistical notation used in formulas. It discusses how variables are designated using letters like X, Y, Z. It also covers notation for sample size (N), individual scores (X1, X2), group numbers (n1, n2), and other concepts. Key points introduced are means (X-bar) vs parameters (mu), standard deviations (s) vs parameters (sigma), and correlations (rXY).
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.
Genetic algorithms are a type of evolutionary algorithm inspired by Darwin's theory of evolution. They use operations like selection, crossover and mutation to evolve solutions to problems over multiple generations. Genetic algorithms work on a population of potential solutions encoded as chromosomes, evolving them toward better solutions. They have been applied to optimization and search problems in various domains like robotics, engineering and bioinformatics.
The document describes the Analytic Hierarchy Process (AHP), which is a structured technique for organizing and analyzing complex decisions. AHP involves constructing a hierarchy of criteria and alternatives, then making pairwise comparisons between elements to determine their relative importance. These comparisons are used to calculate weights for criteria and priorities for alternatives. The document provides an example of using AHP to select a car based on style, reliability, and fuel economy criteria. It also outlines the steps to determine criteria weights, alternative priorities, and consistency ratios in AHP.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Nature-Inspired Metaheuristic AlgorithmsXin-She Yang
This chapter introduces optimization problems and nature-inspired metaheuristics. Optimization problems involve minimizing or maximizing objective functions subject to constraints. Nature-inspired metaheuristics are computational algorithms inspired by natural phenomena, such as simulated annealing, genetic algorithms, particle swarm optimization, and ant colony optimization. They provide near-optimal solutions to complex optimization problems.
Experience Mazda Zoom Zoom Lifestyle and Culture by Visiting and joining the Official Mazda Community at http://www.MazdaCommunity.org for additional insight into the Zoom Zoom Lifestyle and special offers for Mazda Community Members. If you live in Arizona, check out CardinaleWay Mazda's eCommerce website at http://www.Cardinale-Way-Mazda.com
The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature. Genetic Algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems.
This document provides an introduction to genetic algorithms. It describes genetic algorithms as probabilistic optimization algorithms inspired by biological evolution, using concepts like natural selection and genetic inheritance. The key components of a genetic algorithm are described, including encoding solutions, initializing a population, selecting parents, applying genetic operators like crossover and mutation, evaluating fitness, and establishing termination criteria. An example problem of maximizing binary string ones is used to illustrate how a genetic algorithm works over multiple generations.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
This document provides an overview of design of experiments (DOE). It defines DOE as a set of statistical tools used to plan, execute, analyze, and interpret controlled tests to determine which factors impact process outcomes. The document discusses different types of experimental designs like full factorial and fractional factorial designs. It provides an example to illustrate how a two-factor factorial design can be used to study the effects of driver type and ball type on golf score. The benefits of DOE are identified as being able to determine main effects, interactions, and optimal variable settings. The importance of understanding DOE is explained as it allows choosing between alternatives, identifying key factors, and modeling processes.
20060411 Analytic Hierarchy Process (AHP)Will Shen
The Analytic Hierarchy Process (AHP) is a decision-making tool that breaks down complex decisions into a series of pairwise comparisons. It allows decision makers to incorporate both qualitative and quantitative factors. The AHP works by:
1) Computing weights for each decision criterion through pairwise comparisons.
2) Scoring alternatives based on each criterion.
3) Multiplying the weights and scores to obtain overall scores for each alternative.
4) Ranking the alternatives based on their overall scores. Consistency is also checked to ensure reliable results. An example is provided to illustrate the AHP process.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
Multi-Criteria Decision Making (MCDM) techniques can be used to evaluate alternatives based on multiple conflicting criteria. The document discusses various MCDM techniques including weighted score method, TOPSIS, and Analytic Hierarchy Process (AHP). Case studies are presented to demonstrate how to apply these techniques to select optimal construction materials and technologies by considering criteria such as strength, durability, cost, and environmental impact.
This document discusses cost-benefit analysis and cost-effectiveness analysis as methods to evaluate programs by comparing costs and benefits. It explains that CBA monetizes both costs and benefits, while CEA only monetizes costs and expresses benefits in non-monetary units. The document also discusses undertaking these analyses from different perspectives - social, government, and individual - and notes strengths like evaluating net worth but weaknesses like attribution and monetizing all impacts.
International Journal of Mathematics and Statistics Invention (IJMSI)inventionjournals
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
This document discusses the Analytic Hierarchy Process (AHP), a multiple criteria decision making (MCDM) method originally developed by Thomas L. Saaty. AHP breaks down a complex decision problem into a hierarchy, then uses pairwise comparisons to derive priority scales for ranking the decision alternatives. The summary is:
1. AHP allows decision makers to structure multiple conflicting criteria into a hierarchy and then use pairwise comparisons to determine criteria weights and alternative rankings.
2. It is a 3-stage process of building a hierarchy, determining priorities through pairwise comparisons, and checking consistency.
3. While useful for qualitative and quantitative decisions, AHP has limitations like difficulty adding/removing criteria and assumptions of perfect consistency
Selecting Best Tractor Ranking Wise by Software using MADM(Multiple –Attribut...IRJET Journal
This document discusses using a multi-attribute decision making (MADM) technique called TOPSIS to select the best tractor among alternatives. It begins with an introduction to MADM problems and the TOPSIS method. The TOPSIS method involves normalizing a decision matrix, determining ideal and negative ideal alternatives, calculating distances from each alternative to the ideals, and ranking alternatives based on relative closeness. The document then applies this process to select the best tractor among 6 models, using attributes like price, power, fuel efficiency, and maintenance cost to evaluate the alternatives. It aims to help buyers select the tractor best suited to their application needs based on technical specifications.
Grant Selection Process Using Simple Additive Weighting Approachijtsrd
Selection of educational grant is a key success factor for student learning and academic performance. Among popular methods, this paper contributes a real problem of selecting educational grant using data of grant application forms by one of the multi criteria decision making model, SAW method. This paper introduces nine criteria that are qualitative and positive for selecting grant for the students amongst fifteen application forms and also ranking them. Finally, the proposed method is demonstrated in a case study on selecting educational grant for students. Kyi Kyi Myint | Tin Tin Soe | Myint Myint Toe ""Grant Selection Process Using Simple Additive Weighting Approach"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25169.pdf
Paper URL: https://www.ijtsrd.com/computer-science/simulation/25169/grant-selection-process-using-simple-additive-weighting-approach/kyi-kyi-myint
Selection of Fuel by Using Analytical Hierarchy ProcessIJERA Editor
Selection of fuel is a very important and critical decision one has to make. Various criteria are to be considered while selecting a fuel. Some of important criteria are Fuel Economy, Availability of fuel, Pollution from vehicle, Maintenance of the vehicle. Selection of best fuel is a complex situation. It needs a multi-criteria analysis. Earlier, the solution to the problem were found by applying classical numerical methods which took into account only technical and economic merits of the various alternatives. By applying multi-criteria tools, it is possible to obtain more realistic results. This paper gives a systematic analysis for selection of fuel by using Analytical Hierarchy Process (AHP). This is a multi-criteria decision making process. By using AHP we can select the fuel by comparing various factors in a mathematical model. This is a scientific method to find out the best fuel by making pairwise comparisons.
Scenario You are the VP of Franchise services for the Happy Buns .docxkaylee7wsfdubill
Scenario:
You are the VP of Franchise services for the Happy Buns Restaurant. You have been assigned the task of evaluation the best location for the next HB that a prospective franchisee has suggested in the Columbus, Ohio, area. You are using the standard template that provides for which criteria (attributes) you should evaluate. But the specific weights for these are open to adjustment depending on the specific area. These are the six criteria that you will use to evaluate this decision.
·
Close to drive through traffic – traffic counts (avg. thousands/day)
·
Property cost/investment and taxes = NPV of investment ($$)
·
Size of building (square feet in thousands)
·
Size of parking (max number of customers parking)
·
Insurance costs (thousands $ per year)
·
Ease of access from streets (subjective evaluation from observation)
There are five possible locations. You have collected the data from various sources including your VP Finance, Real estate agents, etc. This document summarizes the raw data for each of the five locations: Abberton, Bellview, Casstown, Denton, and Eddington, all suburbs of Columbus. See Data Below.
Assignment
Review the information and data regarding the different alternatives for restaurant location. Develop a MADM table with the raw data. Convert the raw data to utilities (scaled on 0 to 1). Determine the relative weights of each criteria. Evaluate the Decision Table for the best alternative. Do a sensitivity analysis.
Write a report to your boss, Executive VP. Explain your analysis and your recommendation. Provide a rationale for your decision including the logic you used to determine your weights.
Data
Download this Word doc with the data:
Happy Buns Raw Data.docx
Summary of Raw Data
for location of Happy Buns
in the Columbus, Ohio, area.
Criteria
Location
Traffic count (avg. thousands/day)
NPV of investment
($000,000)
Bldg. size (sq ft. 000)
Lot size
(Max customer parking)
Insurance
($000 / yr)
Access
(subjective)
Abberton
17
1.3
3.0
44
5.2
Good
Bellview
10
2.1
3.8
54
5.6
Excellent
Casstown
11
1.5
2.6
65
5.0
Fair
Denton
20
3.0
3.6
52
6.4
Poor
Eddington
15
2.8
4.2
50
6.3
Good
written report and Excel file
SLP Assignment Expectations
Analysis
·
Accurate, complete analysis (in Excel and Word) using the MADM model and theory.
Written Report
·
Length requirements =
2–3 pages minimum
(not including Cover and Reference pages)
·
Provide a brief introduction/ background of the problem.
·
Complete and accurate Excel analysis.
·
Written analysis that supports Excel analysis, and provides thorough discussion of assumptions, rationale, and logic used.
·
Complete, meaningful, and accurate recommendation(s).
MADM Model and Theory
Multi-Attribute Decision Making (MADM)
This decision method assumes certainty. In other words, there are no probabilities of future states to determine. And the data and costs are assumed to be known and accurate. The most common type of decision is a preference decision. Th.
This document describes how to use the Analytic Hierarchy Process (AHP) to make a multi-criteria decision about purchasing an inventory management system. It involves defining the goal, criteria, and alternatives in a hierarchy. Pairwise comparisons are made between criteria and alternatives to assign weights. The weighted scores are calculated and the alternative with the highest score is selected. In this example, the goal is to purchase a system, the criteria are cost, functionality, supplier reputation, and user services, and the alternatives are Systems A, B, and C. System A is determined to have the highest total weighted score, making it the best choice.
Application Of Improved Best Worst Method (BWM) In Real-World ProblemsJim Webb
This document proposes an improved version of the Best Worst Method (BWM) for multi-criteria decision making called the BWM-I. The traditional BWM requires defining a single best and worst criterion, but real-world problems may have multiple criteria that are equally best or worst. The BWM-I allows for multiple best and worst criteria. It develops new vectors to account for multiple best/worst criteria and reduces the number of pairwise comparisons needed. The BWM-I is more flexible and able to realistically capture decision-maker preferences even when criteria have equal significance. It is applied to defining weight coefficients for criteria in renewable energy projects.
Comparison of Max100, SWARA and Pairwise Weight Elicitation MethodsIJERA Editor
Decision making is used in every part of life and realised by each action taken. The presence of correct and satisfactory solution to problems is very important for person, institution and organizations. Multiple Criteria Decision Making (MCDM) techniques are developed for this purpose. Based upon the former studies, it is seen that weight elicitation methods used in solving MCDM problems, have an important role at defining the importance of criteria and obtaining the best and satisfying results for decision makers. Theaim of the paperis to compare the results of range variability between the criteria for Max100, Stepwise Weight Assessment Ratio Analysis (SWARA) and Pairwise Comparison weight elicitation methods and to give suggestion about conditions of using of the methods. It is the first time SWARA is compared with Pairwise Comparison and Max100 methods, and it makes this study different. When results of the study is considered, it is seen that variability of Pairwise Comparison method is higher than that Max100 and SWARA methods. Besides, Max100 is found as the easiest method to use, and Pairwise Comparison method’s way of scoring is defined as the most reliable. In the light of the results obtained from the methods, some conditions of usage are suggested.
Multi criteria decision making in spatial data analysisPreeti Tiwari
There are a number of multi-criteria methods
that can be utilized to facilitate individual or
group decision-making:
1. Analytic Hierarchy Process (AHP)
2. AHP Combined Method
3. Fuzzy AHP
4. Fuzzy AHP Combined
5. Fuzzy AHP Group
6. Group Evaluation Method
7. Weighted Sum Method (WSM)
8. Weighted Product Method (WPM)
The document discusses various approaches to multi-criteria decision making (MCDM), including the weighted score method and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It provides an example of using the weighted score method to select among car alternatives based on criteria like style, reliability and fuel economy. It then works through the steps of the TOPSIS method to solve the same car selection problem, including normalizing scores, determining ideal and negative ideal solutions, calculating separation measures, and determining the alternative with the highest relative closeness to the ideal solution.
The document discusses multiple criteria decision making (MCDM) approaches. It introduces several common MCDM methods: the weighted score method, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, and Analytic Hierarchy Process (AHP). It then provides a detailed example of how to apply the weighted score method and TOPSIS method to a problem of selecting the best car based on criteria like style, reliability, fuel economy, and cost.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
Context-aware recommender systems (CARS) help improve the effectiveness of recommendations by adapting to users' preferences in different contextual situations. One approach to CARS that has been shown to be particularly effective is Context-Aware Matrix Factorization (CAMF). CAMF incorporates contextual dependencies into the standard matrix factorization (MF) process, where users and items are represented as collections of weights over various latent factors. In this paper, we introduce another CARS approach based on an extension of matrix factorization, namely, the Sparse Linear Method (SLIM). We develop a family of deviation-based contextual SLIM (CSLIM) recommendation algorithms by learning rating deviations in different contextual conditions. Our CSLIM approach is better at explaining the underlying reasons behind contextual recommendations, and our experimental evaluations over five context-aware data sets demonstrate that these CSLIM algorithms outperform the state-of-the-art CARS algorithms in the top-$N$ recommendation task. We also discuss the criteria for selecting the appropriate CSLIM algorithm in advance based on the underlying characteristics of the data.
A Novel Nonadditive Collaborative-Filtering Approach Using Multicriteria RatingsNat Rice
This document proposes a novel nonadditive collaborative filtering approach for recommender systems that use multicriteria ratings. It introduces traditional collaborative filtering approaches that use single-criterion or overall ratings. It then describes two existing approaches for multicriteria ratings - the similarity-based approach and the aggregation-function based approach. The key contribution is a new approach that uses the Choquet integral, a nonadditive technique from multicriteria decision making, to aggregate multicriteria ratings for recommending unrated items instead of the traditional weighted average method.
Multi criteria decision support system on mobile phone selection with ahp and...Reza Ramezani
This document proposes using multi-criteria decision making (MCDM) approaches, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to help users select a mobile phone. It outlines the evaluation process, which involves identifying important mobile phone selection criteria, calculating criteria weights using AHP, and then using TOPSIS to rank mobile phone alternatives based on how close they are to an ideal solution and how far they are from a negative ideal solution. The document provides examples of building pairwise comparison matrices in AHP and calculating ideal and non-ideal solutions and alternative distances in TOPSIS to demonstrate the selection approach.
4313ijmvsc02A FUZZY AHP APPROACH FOR SUPPLIER SELECTION PROBLEM: A CASE STUDY...ijmvsc
Supplier selection is one of the most important functions of a purchasing department. Since by deciding the best supplier, companies can save material costs and increase competitive advantage. However this decision becomes complicated in case of multiple suppliers, multiple conflicting criteria, and imprecise parameters. In addition the uncertainty and vagueness of the experts’ opinion is the prominent characteristic of the problem. Therefore an extensively used multi criteria decision making tool Fuzzy AHP can be utilized as an approach for supplier selection problem. This paper reveals the application of Fuzzy AHP in a gear motor company determining the best supplier with respect to selected criteria. The contribution of this study is not only the application of the Fuzzy AHP methodology for supplier selection problem, but also releasing a comprehensive literature review of multi criteria decision making problems. In addition by stating the steps of Fuzzy AHP clearly and numerically, this study can be a guide of the methodology to be implemented to other multiple criteria decision making problems.
This document discusses measuring the importance and weight of decision makers in group decision making processes. It introduces a method to calculate weights for decision makers based on the number of iterations their pairwise comparison matrices take to reach convergence when using the eigenvector method for criteria weighting. A case study applies this method to determine the weights of three decision makers (A, B, and C) who provide pairwise comparison matrices for weighting four criteria related to selecting a form. The number of iterations for each decision maker's matrix to converge determines their absolute and relative weights in the group decision making process.
Slides of the paper http://arxiv.org/abs/1505.04637
source code is available at https://github.com/albahnsen/CostSensitiveClassification/blob/master/costcla/models/cost_tree.py#L15
Abstract:
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.
Similar to Weighted Sum Method: An Introduction (20)
Introduction to Ant Colony Optimization TechniquesMrinmoy Majumder
Ant Colony Optimization is a metaheuristic algorithm inspired by the foraging behavior of ants. It involves simulating the way ants communicate and cooperate to find the shortest path between their nest and a food source.
History of Ant Colony Optimization
Ant Colony Optimization was first introduced by Marco Dorigo in the early 1990s, drawing inspiration from the pheromone trails that ants use to communicate with each other. Since then, it has been successfully applied to various optimization problems in computer science and engineering. It has proven to be particularly useful in solving routing problems, such as the traveling salesman problem.
Ant Colony Optimisation has been used in a wide range of applications, including routing optimisation, scheduling problems, and vehicle routing. By mimicking the collaborative and decentralised nature of ant colonies, this algorithm has proven to be effective in finding optimal solutions to complex problems.
For example, in routing optimisation, Ant Colony optimisation can be used to find the most efficient path for data packets to travel through a network by simulating how ants find the shortest path to a food source. This can help improve network efficiency and reduce congestion. Additionally, in vehicle routing applications, the algorithm can be used to optimise delivery routes for multiple vehicles by mimicking how ants communicate and coordinate with each other to efficiently explore and exploit different routes. This can ultimately lead to cost savings and faster delivery times.
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Seven Metaheuristics to Learn for your Next Data Science Project
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Ten Ideas to open startups in smart agriculture.pptxMrinmoy Majumder
Smart agriculture, also known as precision agriculture, refers to the integration of advanced technologies and data analytics in agricultural practices to enhance productivity, efficiency, and sustainability. It involves the use of sensors, drones, satellite imagery, and Internet of Things (IoT) devices to collect real-time data on soil conditions, weather patterns, crop growth, and livestock health. By analyzing this data, farmers can make informed decisions regarding irrigation schedules, fertilizer application, pest control measures, and overall resource management. This transformative approach to farming not only maximizes yields but also minimizes environmental impact by optimizing resource utilization and reducing waste.
Startups play a crucial role in the agricultural sector by driving innovation and introducing new technologies that can revolutionize farming practices. These startups bring fresh ideas and solutions to address the challenges faced by farmers, such as increasing productivity, reducing costs, and ensuring sustainable practices. With their agility and entrepreneurial spirit, startups can quickly adapt to market demands and collaborate with farmers to develop customized solutions that meet their specific needs. Additionally, startups can also create job opportunities in rural areas and contribute to economic growth in the agricultural sector.
The purpose of this essay is to explore the role of startups in revolutionizing farming practices and the potential benefits they bring to the agricultural sector. By introducing new technologies and innovative solutions, startups can help farmers overcome challenges and achieve greater productivity while promoting sustainability. Additionally, the essay aims to highlight how startups can contribute to rural development by creating job opportunities and driving economic growth in farming communities.
When was the first bottled drinking water sold.pptxMrinmoy Majumder
Archimedes introduced fluid mechanics around 250 BC, stating the conservation of mass within a control volume for constant-density fluids. Isaac Newton described fluid viscosity in his 1687 Principia. Leonardo da Vinci studied fluid dynamics at Plato's Academy. The Reynolds theory explains the interaction between mass and viscosity in fluid mechanics.
Archimedes introduced fluid mechanics, an ancient Greek concept, around 250 BC. The first law of fluid mechanics, the conservation of mass, states that mass is conserved within a control volume for constant-density fluids. In his 1687 Principia, Isaac Newton, an ancient Greek, described fluid viscosity for the first time. Fluid dynamics can be traced back to Leonardo da Vinci, who studied at Plato's Academy. The Reynolds theory of fluid mechanics explains how mass and viscosity interact.
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Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the h...Mrinmoy Majumder
Casestudy on Vulnerability Analysis of Wetlands under Changed Climate Scenarios with the help Water Cycle and Poly-Neural Networks.
Select the factors from literature and stakeholders survey to identify the most significant factors.
Separate these into two groups :
i)One group is for Reliability Enhancing Factors(R)
ii)Another group is for Risk Enhancing Factors(r)
Find the weightage of importance of each factors of each group with respect the impact of climate change on them. Use the Analytical Hierarchy Process Multi Criteria Decision Making method to determine the weightage. Here Climate Variables like Rainfall and Evapotranspiration can be selected as the criteria and all the factors as the alternative. Determine the weightage one group at a time.
Now place the sum of the value of the Reliability Enhancement Factors, (each multiplied with their weightage of importance) in Numerator and the sum of the value of Risk Enhancing Factor (each multiplied with their weightage of importance) in the Denominator.
To get the complete instructions enroll for the internshp at energyinstyle.website
1. The significance of addressing water and sanitation issues in developing countries: Explore how recent special issue calls for papers on water and sanitation reflect the urgent need to address these challenges in developing countries, where access to clean water and proper sanitation facilities is limited.
2. Examining innovative solutions:
3.Analyze the recent special issue calls for papers on water and sanitation to understand the emphasis on exploring innovative approaches and technologies that can improve access to clean water and sanitation facilities in developing countries.
4. Highlighting the impact on health and well-being: Shed light on the detrimental effects of inadequate water and sanitation on the health and well-being of individuals in developing countries. The special issue calls for papers aim to draw attention to the urgent need for action in order to prevent diseases and improve overall quality of life.
5. Encouraging collaboration and knowledge sharing: Emphasize the importance of collaboration between researchers, policymakers, and practitioners in finding sustainable solutions to water and sanitation challenges. The special issue calls for papers provide a platform for sharing knowledge, experiences, and best practices, fostering a global dialogue on addressing these pressing issues.
6. Promoting sustainable development goals: Discuss how the special issue calls for papers align with the United Nations Sustainable Development Goals
7.Emerging technologies for improving water quality: Discuss the latest research trends highlighted in the most recent special issue calls for papers, focusing on innovative technologies that aim to enhance water treatment processes and ensure better-quality drinking water for communities worldwide.
8. Sustainable approaches
Special Issues are available in different journals
1. Develop a smart irrigation system that utilizes sensors and data analytics to optimize water usage in agricultural fields, reducing water waste and improving crop yields.
2. Create a platform that connects farmers with precision agriculture technologies, such as drones and satellite imagery, to provide real-time monitoring and analysis of crop health and productivity.
3. Design a mobile application that enables farmers to remotely monitor and control their farm operations, including temperature, humidity, and nutrient levels in greenhouse environments.
4. Build an automated livestock monitoring system
In agricultural fields, data analytics is being used to optimise water usage, reduce waste, and increase crop yields. This technology is being combined with precision agriculture technologies to enable farmers to remotely monitor and control their farm operations.
But this article is not about the above ideas of starting startups but something more innovative is discussed.
Explore the latest advancements in hydro and energy informatics with seven ne...Mrinmoy Majumder
These special issue calls provide a unique opportunity for researchers and practitioners to contribute their work and contribute to the growing body of knowledge in hydro and energy informatics. Don't miss out on this chance to showcase your research and make a significant impact in these rapidly evolving fields. Discover cutting-edge research in hydro and energy informatics through seven exciting special issue calls by leading academic journals in the field. These special issue calls offer a platform for experts to share their innovative findings and insights, fostering collaboration and pushing the boundaries of hydro and energy informatics. Embrace this opportunity to stay at the forefront of advancements and contribute to shaping the future of these dynamic disciplines.
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An introductory explanation regarding the water cycle algorihtm.Complete tutorial can be found at www.baiatra.ws.The Water Cycle Algorithm is a nature-inspired optimization algorithm that mimics the movement and transformation of water in the Earth's hydrological cycle. It is based on the principles of evaporation, condensation, precipitation, and infiltration. This algorithm has gained popularity in solving complex optimization problems due to its ability to efficiently explore and exploit search spaces.
This algorithm is another famous metaheuristics or nature-based optimization technique proposed by Ali Sadollah in the year of 2015.
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Participate in completing the course on Optimization at baipatra.ws or an internship on energyinstyle.website
What is the difference between Free and Paid Subscriber of HydroGeek Newslett...Mrinmoy Majumder
Free subscribers to Hydro Geek Newsletters receive access to tutorials, scholarship and job news, calls for papers, start up ideas, old books, and calculators and case studies. Paid subscribers receive unlimited access to all content without any time restrictions. They can also comment on posts and access restricted premium weekly posts. Paid subscribers can announce opportunities once per month while founding members can do so twice per month. Paid subscribers also receive discounts on books, reports, journals, and water resource software.
Ten Most Recognizable Case Studies of Using Outlier.pptxMrinmoy Majumder
Outlier detection is a method used to identify outliers in data, such as power consumption in non-residential buildings, online leakage detection in water distribution systems, and efficient water quality prediction systems.
It is also used in healthcare fraud, coastal water temperature data, and monitoring water quality data.
The method has been applied in various case studies, such as a survey, healthcare fraud, and fault detection for circulating water pumps.
It is also used in acoustic feature-based leakage event detection for large-scale water distribution networks.
Five Ideas for opening startups in Virtual and Green WaterMrinmoy Majumder
Start-ups can offer services for identifying ideal locations for virtual and green water harvesting tanks. Real-time monitoring of water levels is provided, ensuring optimal water management. Water classifiers, developed using water quality sensors, separate green water from virtual water, ensuring automatic detection of water use. Leak detection in irrigation pipelines or water tanks can save thousands of dollars and prevent major disasters. Non-linear AI-based computer models can predict the availability of virtual and green water, ensuring the watershed is well-stocked. These services can help optimize water management and reduce costs associated with traditional water sources.
The National Sea Grant College Program and the Water Power Technologies Office announced projects in Alaska, Guam, and Hawaiʻi that will examine how the adoption of ocean renewable energy could support sustainable energy systems."
"For island and remote communities in the United States, developing resilient electricity infrastructure and energy systems can be fraught with challenges. These locations often rely on expensive, unreliable energy systems that are vulnerable to volatile energy supplies and costs, natural disasters, and impacts from climate change. That’s why the National Oceanic and Atmospheric Administration’s (NOAA) National Sea Grant College Program, in partnership with the U.S. Department of Energy’s (DOE) Water Power Technologies Office, is supporting three projects in Alaska, Guam, and Hawaiʻi that will examine how adoption of ocean renewable energy could support sustainable energy systems. "
"Analysis by the Council on Energy, Environment and Water (CEEW) shows that over 45 percent of districts in India have undergone concerning changes to landscape."
"Cities, in particular, have witnessed disrupting natural drainage patterns and encroachment on vital water bodies such as lakes and ponds that were originally intended to absorb stormwater. For instance, Hyderabad, home to 400 lakes and 48 flood-abso..."
Groundwater is nature's insurance...World Bank Report
"As “nature’s insurance,” groundwater protects food security, reduces poverty, and boosts resilient economic growth, but the resource is threatened by overexploitation and pollution. High-level political action is needed to prioritize groundwater and align the private and social costs of its use. A new World Bank report considers the economic value of groundwater, the costs of misusing it, and the opportunities to leverage it more effectively."
"Revolutionary new Swiss 'water battery' will be one of Europe's main renewable sources of energy"
A Swiss company has built what is being called a giant water battery deep under the Alps that provides an energy storage capacity equivalent to 400,000 electric car batteries. It could be a game-changer.
Click to visit Energy in Style for more info about the News
Latest Jobs and Scholarship Opportunities
Professor in Food, Water, Energy Nexus
Northeastern University’s College of Arts, Media and Design (CAMD) invites applications for open-rank, tenured or tenure-track positions in the thematic area of Food, Water, Energy Nexus.
South Africa Evaluator (Water, Energy, and/or Agriculture Sectors)
"Dexis is recruiting for an evaluator based in the South Central African region, with experience in the water, energy, and/or food sectors to be part of the WE4F Evaluation Team. This opportunity is a short-term technical assistance (STTA) consultant position and is contingent on USAID approval."
PhD Opportunity // Energy-Water-Nexus - Development of a modelling framework for integrated energy and water planning in water-scarce countries
The Inst
What is next in AI ML Modeling of Water Resource Development.pdfMrinmoy Majumder
In recent years like all the other fields of studies, the application of Artificial Intelligence and Machine Learning (AI&ML) on water resource development projects has increased manifold.
For example :
Sukanya, S., and Sabu Joseph. "Climate change impacts on water resources: An overview." Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence (2023): 55-76.
Kommadi, Bhagvan. "AI and ML Applications: 5G and 6G." (2023).
Joseph, Kiran, Ashok K. Sharma, Rudi van Staden, P. L. P. Wasantha, Jason Cotton, and Sharna Small. "Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review." Water 15, no. 11 (2023): 2046.
Yurtsever, Mustafa, and E. M. E. Ç. Murat. "Potable Water Quality Prediction Using Artificial Intelligence and Machine Learning Algorithms for Better Sustainability." Ege Academic Review 23, no. 2 (2023): 265-278.
However, the uncertainty involved in Hydrologic/Hydraulic or Water Quality Parameters is very hard to simulate, and even with the advent of such cognitive algorithms accuracy and reliability of the models nevertheless lack substance. In this field of study, there is still much to be done. Some interesting objectives can be :
Very Short Term Course on MAUT in Water Resource Management.pdfMrinmoy Majumder
What is MAUT?
“Multi-attribute utility theory (MAUT) combines a class of psychological measurement models and scaling procedures which can be applied to the evaluation of alternatives which have multiple value relevant attributes.”Von Winterfeldt and Fischer (1975).
Some example applications of MAUT in Water Resource Management?
Feeny, David, William Furlong, George W. Torrance, Charles H. Goldsmith, Zenglong Zhu, Sonja DePauw, Margaret Denton, and Michael Boyle. "Multiattribute and single-attribute utility functions for the health utilities index mark 3 system." Medical care 40, no. 2 (2002): 113-128.
Zheng, Yong, and David Xuejun Wang. "Hybrid Multi-Criteria Preference Ranking by Subsorting." arXiv preprint arXiv:2306.11233 (2023).
Lopes, Yuri Gama, and Adiel Teixeira de Almeida. "Assessment of synergies for selecting a project portfolio in the petroleum industry based on a multi-attribute utility function." Journal of Petroleum Science and Engineering 126 (2015): 131-140.
Anand, Adarsh, Mohini Agarwal, Deepti Aggrawal, Laurie Hughes, Parisa Maroufkhani, and Yogesh K. Dwivedi. "Successive generation introduction time for high technological products: an analysis based on different multi-attribute utility functions." Environment, Development and Sustainability (2022): 1-18.
Most Recommended news,products and publications from hydroinformaticsMrinmoy Majumder
This document summarizes the five most cited news articles, products, and publications related to hydroinformatics from June 2022. It also lists the top five most cited papers in hydroinformatics from major publishers like IWA, MDPI, Copernicus, Wiley, and Springer. Finally, it provides additional resources and opportunities related to hydroinformatics, including short courses, books, journals, and tools.
Latest Jobs, Scholarship Opportunities and CFPs in.pptxMrinmoy Majumder
Welcome to another edition of the Hydro Geek Newsletter. In this edition, I have collected some Jobs, Scholarship opportunities, and CFPs in the field of Hydroinformatics Engineering. The list is given next.
Latest Job Opportunites
Landscape Architect@ LOCUS Bengaluru, Karnataka, India
“Looking for passionate landscape architects for multiple typologies across scale, for masterplans, with an eye for aesthetics, functionality, cost, sustainability. Good knowledge of planting, materials, and finishes is required. Should be able to understand and produce design development drawings and detailed working drawings for landscape design and execution. Willingness to commit for longer durations will be an added advantage. Good work ethic, creative and desirous of fitting in a learning environment dealing with multi-scale architectural and planning projects meeting international standards. Send resume and portfolio to future@studiolocus.com.”
Click here to apply.
M. Tech - Entry Level Graduate Engineer at ATKINS @Bengaluru, Karnataka, India
ATKINS is hiring MTech Graduates passing out in 2023 for working in the field of water and environment at Atkins offices in Bangalore, Gurgaon & Mumbai, IN.The job role involves the Design of treatment plants, rivers, Dam and Maritime, Pipelines, and Water retaining structures; Modelling related to water and water infrastructure (storm, wastewater, water); Flood risk assessment, Flood alleviation, Water Quality modeling and related activities. Must have Civil Engg in B.Tech.
Click here to apply
GIS Expert for REWARD Program of Sambodhi Research and Communications@Bhubaneswar, Odisha, India
“This role provides coordinates with partner organizations like the World Bank, the Watershed Development Department (DSCWD), and the Government of Odisha for analyzing and interpreting the pixel-level satellite imagery and GIS data. S/he has to develop and compute indices of vegetation density and photosynthetic activities like NDVI, LAI, FAPAR as well as moisture (LSWI). S/he also has to support the PMU and partners in devising a sampling strategy for baseline based on listing data. The candidate must have worked on innovative techniques for analyzing and interpreting the GIS data (change detection technique). We are looking for candidates with experience working with the government/s on similar assignments in the past.”
Click here to apply
Seven Techniques that you will learn when you enrol forMTech in Hydroinformat...Mrinmoy Majumder
Hydro informatics is a subject that deals with the application of Data Science and ICT to Water Resource Development. The Master’s course is a two years interdisciplinary degree where Civil, Electrical, Mechanical, etc. can apply. Admission is through CCMT 2023 and DASA 2023.
You will learn the following techniques after completion of the course :
Multi-Criteria Decision Making Techniques like AHP, ANP, ELECTRE, PROMETHEE, etc.
Water Related Instruments like River Surveyor, Micro ADV, Multi-Parameter Water Quality Sensor
Decision Tree Algorithms
Geographical Information System and Image Processing
Optimization Techniques including Bio-Inspired Optimization Techniques
Artificial Neural Networks (ANN) including Polynomial Neural Networks
Internet of Things
and their applications in Water Resource Development.
The course also includes one-year real-life project opportunities.
The popularity of AI, machine learning, and other data science technologies has encouraged many aspiring entrepreneurs to launch businesses in these fields.
Water and data science can help to launch successful startups.
Five Case Studies. The detailed newsletter can be accessed at https://hydrogeek.substack.com/
Five Example Application of Hydroinformatics for Optimal Management of Ground...Mrinmoy Majumder
Significant advancements in water research, as well as new observational and computational capabilities and infrastructures (e.g., MOSES, TERENO, VISLAB, JUWELS), present an opportunity to develop the next generation of smart, data- and model-driven water monitoring systems. Software tools are intended to aid scientists worldwide in the analysis of larger and more complex systems, as well as in the exploration of hydrological extreme scenarios and their consequences.
Hydroinformatics is an interdisciplinary field that employs simulations, data science, and decision-making systems to create novel approaches to effectively, sustainably, and equitably manage water in a variety of scenarios.
Groundwater resource management in water supply areas is now an essential task in order to minimize anthropogenic interference and ensure the long-term use of available water.
Below five articles shows the way hydro informatics was used in the optimal management of groundwater :
Click the link for the full article : https://open.substack.com/pub/hydrogeek/p/five-innovative-applications-of-hydroinformatics?r=c8bxy&utm_campaign=post&utm_medium=web
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Enhanced data collection methods can help uncover the true extent of child abuse and neglect. This includes Integrated Data Systems from various sources (e.g., schools, healthcare providers, social services) to identify patterns and potential cases of abuse and neglect.
1. Weighted Sum Method
Dr. Mrinmoy Majumder
Course Name : Intro to Multi Criteria Decision Making Methods
Lecture No.09 out of 15
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2. What is Weighted Sum Method
• In decision making problems, the Weighted Sum Model or
Method(WSM) (Fishburn et.al.,1967) is the simplest known multi-
criteria decision making(MCDM) method for evaluating a number of
alternatives in terms of a number of decision criteria.
• Applicable only when all the data are in exactly the same unit.
Let wj describe the relative weight of significance of the criterion Cj and aij is the performance value or
normalized magnitude of alternative Ai when it is evaluated in terms of criterion Cj. Then, the total (i.e.,
when all the criteria are considered simultaneously) importance of alternative Ai, denoted as Ai
WSM-score
Reference : Fishburn, P.C. (1967). "Additive Utilities with Incomplete Product Set: Applications to Priorities
and Assignments". Operations Research Society of America (ORSA), Baltimore, MD, U.S.A.
4. Example of
ANP
Decision Goal : To buy a car
Criteria : Cost and Speed
Alternatives : Mercedes Benz(M),
Jaguar(J), Toyota(T)
Aggregation Methods to be used :
Weighted Sum Method
5. Example
Contd.
• If importance of Cost is more compared to the importance of Speed with respect to the
goal of the decision making, i.e., buying a car. The value of alternatives with respect to cost
and speed was normalized(value/sum of all values).Here Cost is a non-preferred criteria as
more the cost of the alternative less will it be preferred choice of selection
Goal :
Buy a
car
Cost
(Relative
Weight :
0.667)
Speed
(Relative
Weight :
0.333)
Sum of the product
function of Relative
Weight of Criteria and
the value of the
alternative for that
criteria
A
(WSMScore)
Rank based on
importance
M 0.500 0.200 0.5x0.667+0.2x0.333 0.400 1
J 0.300 0.500 0.3x0.667+0.5x0.333
0.367 2
T 0.200 0.300 0.2x0.667+0.3x0.333
0.233 3