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МетодаStatistika1,836VI i mašinsko učenje1,661Nauke o odlučivanju932Istraživačke metode1,354Merenje1,745Kauzalnost i dokazi532Istraživačka praksa118
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Стварне методе које одговарају вашем филтеру.
СортирајПопуларностА–ШШ–АНајновије
decision making

SF-GRA

SF-GRA (Spherical extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2019
decision making

SF-MABAC

SF-MABAC (Spherical extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2019
decision making

SF-MARCOS

SF-MARCOS (Spherical extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2019
decision making

SF-MOORA

SF-MOORA (Spherical extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2019
decision making

SF-PROMETHEE

SF-PROMETHEE (Spherical extension of PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019 in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2021
decision making

SF-SAW

SF-SAW (Spherical extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Yörükoğlu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2021
decision making

SF-TODIM

SF-TODIM (Spherical extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu, F. Kahraman, C. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2019
decision making

SF-TOPSIS

SF-TOPSIS (Spherical extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Kahraman in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2019
decision making

SF-VIKOR

SF-VIKOR (Spherical extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Sharaf (book chapter); SF foundation Kutlu Gündoğdu-Kahraman 2019 in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2021
decision making

SF-WASPAS

SF-WASPAS (Spherical extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Boltürk & Kutlu Gündoğdu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2021
decision making

SF-WPM

SF-WPM (Spherical extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Kutlu Gündoğdu & Yörükoğlu in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2021
decision making

SFZN-CRADIS

SFZN-CRADIS (Spherical Fuzzy Z-Number CRADIS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2024
decision making

SFZN-CRITIC

SFZN-CRITIC (Spherical Fuzzy Z-Number CRITIC Weighting) is a weighting multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2024
decision making

SFZN-MARCOS

SFZN-MARCOS (Spherical Fuzzy Z-Number MARCOS Ranking) is a distance multi-criteria decision-making (MCDM) method introduced by Niu, J. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2024
game theory

Shapley Value

The Shapley Value is a solution concept for coalition games that distributes total payoff fairly among players based on their marginal contributions to coalitions. Introduced by Lloyd Shapley in 1953, the Shapley Value is the unique payoff distribution that satisfies four intuitive axioms: efficiency (total payoff is d

2 извора1953
optimization

Simheuristics

Simheuristics is a hybrid algorithmic framework that integrates Monte Carlo or discrete-event simulation into metaheuristic search procedures to solve stochastic combinatorial optimization problems. Introduced by Juan et al. in 2015, it addresses settings where objective function evaluations involve random variables, p

1 извор2015
operations research

Simplex Method

The Simplex Method, developed by George Dantzig in 1947, is a foundational algorithm for solving linear programming problems. It systematically explores vertices of the feasible region to find the optimal solution where the objective function is maximized or minimized subject to linear constraints.

2 извора1947
optimization

Simulated Annealing

Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in co

2 извора1983
experimental design

Simulation-assisted Six Sigma DMAIC

Simulation-assisted Six Sigma DMAIC embeds discrete-event or Monte Carlo simulation models inside the classic DMAIC cycle (Define, Measure, Analyze, Improve, Control) to test process changes virtually before committing to physical implementation. By running thousands of simulated scenarios, teams quantify variation, id

2 извора2000
decision making

SIMUS

SIMUS (Sequential Interactive Model for Urban Systems) is a ranking multi-criteria decision-making (MCDM) method introduced by Munier, N. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2011
decision making

SIR

SIR (Superiority and Inferiority Ranking) is a outranking multi-criteria decision-making (MCDM) method introduced by Xu, X. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2001
decision making

SIWEC

SIWEC (Simple Weight Calculation) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Puška, A. Nedeljković, M. Pamučar, D. Božanić, D. Simić, V. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2024
quality management

Six Sigma DMAIC

Six Sigma DMAIC is a data-driven, five-phase process improvement methodology — Define, Measure, Analyze, Improve, and Control — used to reduce defects and process variation to fewer than 3.4 defects per million opportunities. Originating at Motorola in the 1980s and systematized by practitioners including Pyzdek and Ke

1 извор2014
healthcare management

Six Sigma in Healthcare

Six Sigma is a data-driven quality improvement methodology originating at Motorola in 1986 that aims to reduce process variation and defects to achieve near-perfect quality (3.4 defects per million opportunities). In healthcare, Six Sigma uses statistical analysis and structured project methodology (DMAIC: Define-Measu

3 извора1986
optimization

Slime Mould Algorithm

The Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribut

1 извор2020
decision making

SMAA

SMAA (Stochastic Multiobjective Acceptability Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Lahdelma, R., Hokkanen, J., Salminen, P. in 1998. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор1998
decision making

SMAA2

SMAA2 (SMAA-2 — Stochastic extension of SMAA2) is a ranking multi-criteria decision-making (MCDM) method introduced by Lahdelma, R. & Salminen, P. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2001
decision making

SMART

SMART (Simple Multi-Attribute Rating Technique) is a ranking multi-criteria decision-making (MCDM) method introduced by Edwards, W. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор1986
decision making

SMART-WEIGHT

SMART-WEIGHT (SMART Weighting — Direct importance rating normalisation (Edwards SMART weight step)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Edwards, W., Barron, F. H. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible

1 извор1994
operations management

SMED

Single Minute Exchange of Die (SMED) is a systematic approach developed by Shigeo Shingo in the 1980s to drastically reduce the time required to changeover equipment from producing one product to another. The methodology, part of the Toyota Production System, aims to reduce setup time to a single-digit minute range (id

2 извора1985
decision making

SNHF-TOPSIS

SNHF-TOPSIS (TOPSIS with Maximizing Deviation in Simplified Neutrosophic Hesitant Fuzzy Environment) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M. Naz, S. Smarandache, F. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible r

1 извор2019
clinical psychology

Social Phobia Inventory

The Social Phobia Inventory (SPIN) is a 17-item self-report measure of social anxiety disorder symptoms. Developed by Connor, Davidson, and colleagues in 2000, the SPIN assesses fear, avoidance, and physiological symptoms related to social anxiety. It is widely used for screening and monitoring social anxiety disorder

2 извора2000
problem structuring

SODA

Strategic Options Development and Analysis (SODA) is a facilitated, qualitative method for structuring complex organisational problems. Developed by Colin Eden in 1988, it uses cognitive maps — directed graphs of causal constructs — to capture and integrate the subjective views of multiple stakeholders. SODA is most va

1 извор1988
soft computing

Soft Set Theory

Soft Set Theory is a mathematical framework for handling uncertainty and imprecision through parameterized families of sets. Introduced by Dmitriy Molodtsov in 1999, it provides an approximate description of objects in a universe by mapping each parameter in a chosen parameter set to a crisp subset of that universe. Un

1 извор1999
problem structuring

Soft Systems Methodology

Soft Systems Methodology (SSM) is an interpretive, action-research approach for structuring and managing complex, ill-defined ('soft') problem situations involving human activity. Developed by Peter Checkland at Lancaster University throughout the 1970s and formally presented in 1981, SSM guides practitioners through i

1 извор1981
decision making

Sorensen-Dice Coefficient

Sorensen-Dice coefficient, also called Dice coefficient or Czekanowski index, measures the similarity between two sets or samples based on presence and absence of attributes. Introduced independently by Thorvald Sorensen (1948) and Lee Dice (1945), this index ranges from 0 (completely dissimilar) to 1 (identical). It i

2 извора1945
decision making

SOWA

SOWA (Spatial Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Makropoulos, C. K.; Butler, D. in 2003/2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2003
agronomy

Sowing Date Optimization

Sowing Date Optimization is a decision support pipeline for determining optimal crop planting dates that align phenological development with favorable environmental windows, maximizing yield and reducing climate risk. Developed by crop modelers (Aggarwal, Semenov) in the 2000s, this method combines crop simulation, cli

2 извора2006
sport psychology

Sport Confidence Inventory

The SCI is a 13-item questionnaire measuring general, trait-level confidence in sport ability—the athlete's habitual belief in their capability to execute skills and perform well in their sport. Developed by Vealey in 1986, the SCI is one of the most widely used instruments for assessing athlete self-confidence and pre

2 извора1986
decision making

SPOTIS

SPOTIS (Stable Preference Ordering Towards Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Dezert, J., Tchamova, A., Han, D., Tacnet, J. M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2020
decision making

SPROBID

SPROBID (Simplified PROBID using Top/Bottom Quartile Ideal Sets) is a ranking multi-criteria decision-making (MCDM) method introduced by Wang, Z., Rangaiah, G. P., Wang, X. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

1 извор2021
game theory

Stackelberg Competition

Stackelberg Competition models sequential oligopolistic markets where one firm (the leader) commits to a quantity first, and other firms (followers) observe this choice and respond. Introduced by Heinrich von Stackelberg in 1934, the model captures first-mover advantage in quantity-setting competition. The resulting St

2 извора1934
forestry

Stand Basal Area Measurement

Stand basal area is a fundamental forest mensuration metric representing the total cross-sectional area of tree stems per unit land area, typically expressed in square meters per hectare. Formalized across twentieth-century forestry literature (notably by Husch, Beers, and Kershaw), basal area serves as a key indicator

4 извора1960
clinical psychology

State-Trait Anxiety Inventory

The State-Trait Anxiety Inventory (STAI) is a 40-item self-report questionnaire designed to measure two distinct dimensions of anxiety: state anxiety (temporary anxiety in response to a specific situation) and trait anxiety (stable tendency to experience anxiety across situations). Developed by Charles D. Spielberger a

2 извора1970
simulation

Stochastic Dynamic Programming

Stochastic Dynamic Programming (SDP) is a mathematical optimization framework for sequential decision problems where outcomes are partly random. It extends Bellman's principle of optimality to stochastic environments, representing problems as Markov Decision Processes (MDPs) and computing optimal policies by solving re

2 извора1957
econometrics

Stochastic Frontier Analysis

Stochastic Frontier Analysis is a frontier regression model, introduced by Aigner, Lovell and Schmidt in 1977, that estimates a production, cost, or profit function while separating each unit's technical inefficiency from ordinary statistical noise. It splits the error term into a symmetric random component and a one-s

2 извора1977
simulation

Stochastic Genetic Algorithm

The Stochastic Genetic Algorithm (SGA) is a population-based metaheuristic that mimics biological evolution — selection, crossover, and mutation — to search for near-optimal solutions in complex, nonlinear, or combinatorial spaces. Its randomized operators make it robust to local optima and broadly applicable across en

2 извора1975
simulation

Stochastic Goal Programming

Stochastic Goal Programming (SGP) extends classical goal programming to handle uncertainty in goal targets, constraint coefficients, or right-hand-side parameters. By incorporating probabilistic constraints and stochastic objective components, it finds solutions that satisfy multiple goals at acceptable probability lev

2 извора1968
simulation

Stochastic Integer Programming

Stochastic Integer Programming (SIP) is an optimization framework that combines integer (discrete) decision variables with explicit probabilistic modeling of uncertainty. It seeks the best here-and-now decision that minimizes expected cost (or maximizes expected benefit) across a distribution of future scenarios, accou

2 извора1955
simulation

Stochastic Linear Programming

Stochastic Linear Programming (SLP) extends classical linear programming to settings where some model parameters — costs, demands, resource availability — are uncertain and modeled as random variables. By optimizing expected costs over a probability distribution of scenarios, SLP produces decisions that remain feasible

2 извора1955
simulation

Stochastic Mixed-Integer Programming

Stochastic Mixed-Integer Programming (SMIP) is an optimization framework that finds the best mix of binary, integer, and continuous decisions when key parameters — costs, demands, capacities — are uncertain and modeled as probability distributions over a set of scenarios. It extends classical MIP by embedding scenario

2 извора1990
simulation

Stochastic NSGA-II

Stochastic NSGA-II extends the NSGA-II evolutionary algorithm to handle objective functions that are noisy, uncertain, or probabilistic. By averaging or sampling stochastic objectives across multiple evaluations, it identifies Pareto-optimal solutions that are robust to uncertainty, making it suitable for engineering d

2 извора2001
optimization

Stochastic Optimization

Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine

2 извора1951
simulation

Stochastic Particle Swarm Optimization

Stochastic Particle Swarm Optimization (Stochastic PSO) is a swarm-intelligence metaheuristic that extends the standard PSO framework by incorporating explicit stochastic elements — random inertia weights, probabilistic velocity resets, or noise injections — to escape local optima and maintain population diversity thro

2 извора1995
simulation

Stochastic Queueing Simulation

Stochastic Queueing Simulation models waiting-line systems where arrival and service processes follow probability distributions rather than fixed rates. By simulating thousands of random events, it estimates performance measures — mean waiting time, queue length, server utilization — under realistic uncertainty, making

2 извора1953
simulation

Stochastic Tabu Search

Stochastic Tabu Search (STS) is an extension of classical Tabu Search that introduces randomness into the neighborhood exploration and move-selection phases. By combining tabu memory — which forbids recently visited solutions — with probabilistic acceptance or random candidate sampling, STS escapes local optima more ef

2 извора1990
decision making

STOCHASTIC-UTA

STOCHASTIC-UTA (Stochastic UTilités Additives (preference-disaggregation under uncertainty)) is a ranking multi-criteria decision-making (MCDM) method introduced by Stavrou, D. I.; Ventikos, N. P.; Tsoukalas, V. D. (2018) — STOCHASTIC-UTA seminal chapter Jacquet-Lagrèze, E.; Siskos, J. (1982) — classical UTA foundation

1 извор1982
problem structuring

Strategic Choice Approach

The Strategic Choice Approach (SCA) is an interactive, workshop-based problem structuring method developed by John Friend and Allen Hickling, first published in 1987 and refined in the definitive third edition of Planning Under Pressure (2005). SCA helps groups of planners and stakeholders manage interconnected decisio

1 извор2005
decision making

Stratified Best Worst Method

Stratified BWM is an extension of the Best Worst Method that applies the BWM logic recursively across multiple hierarchical layers. Instead of weighting criteria at a single level, it identifies the best and worst criterion within each level of a hierarchy, then aggregates weights across levels. This enables more reali

2 извора2015
game theory

Subgame Perfect Equilibrium

Subgame Perfect Equilibrium (SPE) is a refinement of Nash Equilibrium for sequential games, introduced by Reinhard Selten in 1965. It requires that strategy profiles constitute a Nash Equilibrium in every subgame, eliminating non-credible threats and incredible promises. Backward induction is the primary computational

2 извора1965
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