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ARAS (Additive Ratio Assessment) is a ranking multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
SAW (Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
AHP-BOCR is an extension of the Analytic Hierarchy Process that incorporates strategic perspectives through the BOCR framework: Benefits, Opportunities, Costs, and Risks. Instead of optimizing a single objective, AHP-BOCR decomposes decisions into four strategic dimensions and uses a formula (Benefits × Opportunities)
APLOCO (Automatic Pairwise Linear Order Combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Konstantinos, N., Xenakis, A., Kehagias, A. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
ABC Analysis is a demand-value segmentation technique that divides inventory items into three classes — A, B, and C — based on their annual usage value (unit cost multiplied by annual demand). Rooted in the Pareto principle and codified for inventory management by Silver, Pyke, and Peterson (1998), it guides managers t
AHP (Analytic Hierarchy Process) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
ANP (Analytic Network Process (AHP with feedback and interdependences)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Saaty, T. L. in 1996. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
AROMAN (Alternative Ranking Order Method Accounting for Two-Step Normalisation) is a ranking multi-criteria decision-making (MCDM) method introduced by Zdravković, M., Hamid, M., Radovanović, M. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) is a ranking multi-criteria decision-making (MCDM) method introduced by Kara, K., Yalçın, G. C., Kaygısız, E. G., Simic, V., Örnek, A. Ş., Pamucar, D. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a st
BWM-Sort is a variant of the Best Worst Method introduced by Jafar Rezaei around 2015. It combines pairwise comparison of criteria with alternative sorting, enabling decision-makers to prioritize both evaluation dimensions and final ranked outcomes in a single integrated framework.
CONSENSUS-REACHING (Consensus Reaching — Iterative aggregation of expert opinions toward group consensus) is a ranking multi-criteria decision-making (MCDM) method introduced by Herrera-Viedma, E., Herrera, F., Chiclana, F. in 2002. It turns a decision matrix of alternatives scored on multiple criteria into a structure
The Copenhagen Burnout Inventory (CBI) is a multidimensional burnout assessment tool designed to measure exhaustion and disengagement in occupational settings. Developed by Kristensen and colleagues in 2005, the CBI distinguishes among personal, work-related, and client-related burnout, making it particularly valuable
PCA-WEIGHT (PCA Weighting — Principal Component Analysis based objective weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pearson, K. in 1901. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Hwang, C. L., Yoon, K. in 1981. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
The A* Search Algorithm, developed by Peter E. Hart, Nils J. Nilsson, and Bertram Raphael in 1968, is an optimal path-finding algorithm that combines the benefits of Dijkstra's algorithm with heuristic guidance. It efficiently finds the shortest path by balancing actual distance from the start with estimated distance t
The African Vultures Optimization Algorithm (AVOA) is a metaheuristic algorithm introduced by Moghdani and Salimifard in 2020, inspired by the search and scavenging behavior of African vultures. Vultures employ sophisticated collaborative strategies to locate carrion across vast distances, using thermal air currents an
Agent-Based Ant Colony Optimization (AB-ACO) models individual ants as autonomous agents that probabilistically construct solutions by following and depositing pheromone trails on a search graph. By coupling agent-level behavioral rules with a shared pheromone environment, the collective system converges on high-qualit
Agent-based dynamic programming (ABDP) embeds Bellman's dynamic programming framework within individual agents of an agent-based model, enabling each agent to solve sequential, multi-stage decision problems using backward induction or value-function iteration. The result is a population of optimizing agents whose inter
An Agent-Based Genetic Algorithm (ABGA) partitions a genetic algorithm's population across a network of autonomous agents, each maintaining a local sub-population and evolving it independently. Agents periodically exchange individuals (migration) based on proximity or communication rules, enabling parallel exploration
Agent-Based Goal Programming (ABGP) integrates agent-based simulation with goal programming optimization to model systems where multiple autonomous decision-makers pursue competing, prioritized goals. It enables researchers to study how decentralized, adaptive behavior at the agent level leads to system-level outcomes
Agent-Based Integer Programming (ABIP) couples the behavioral richness of agent-based modeling with the combinatorial rigor of integer programming. Individual agents pursue local objectives while a global IP solver enforces discrete feasibility constraints, enabling realistic modeling of multi-actor systems where decis
Agent-based NSGA-II embeds the NSGA-II evolutionary algorithm inside an agent-based simulation loop so that objective values for each candidate solution are determined by running a full agent simulation rather than by evaluating a closed-form function. This coupling enables multi-objective optimization over systems who
Agent-Based Queueing Simulation (AB-QS) combines agent-based modeling with queueing theory to simulate systems where autonomous, decision-making entities interact through waiting lines and service points. Each entity (patient, customer, job) is modeled as an independent agent with its own state and behavioral rules, en
Agent-Based Tabu Search (ABTS) embeds the tabu search metaheuristic inside a multi-agent framework where autonomous agents each run independent or cooperating tabu search threads, sharing promising solutions to escape local optima and collectively explore large combinatorial or continuous search spaces more effectively
Aggregate Planning (or Sales & Operations Planning, S&OP) is a collaborative, iterative process that balances demand and supply at a high level—typically grouping products into families and planning over a 3–18 month horizon. Developed formally by Tom Wallace and popularized through APICS, aggregate planning helps orga
AHPSORT (AHPSort — AHP-based classification of alternatives into ordered categories) is a sorting multi-criteria decision-making (MCDM) method introduced by Ishizaka, A., Nemery, P., Pearman, C. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
AHSPR (Asymmetric Hesitant Fuzzy Sigmoid Preference Relations (Zhou-Xu 2016)) is a preference relations multi-criteria decision-making (MCDM) method introduced by Zhou, W. Xu, Z. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
The Ambivalent Sexism Inventory (ASI) is a 22-item self-report measure developed by Peter Glick and Susan T. Fiske in 1996 to assess both hostile and benevolent sexism toward women. The scale captures the dual nature of sexism: overtly antagonistic attitudes and paternalistic but ultimately restrictive attitudes that p
Ant Colony Optimization (ACO) is a metaheuristic algorithm introduced by Marco Dorigo and colleagues in the early 1990s that solves combinatorial optimisation problems by simulating the collective foraging behaviour of ants. Real ants lay pheromone trails on paths and preferentially follow stronger trails; ACO turns th
The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, e
The Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, division, addition, subtraction) as the inspiration for search strategies. Unlike nature-inspired algorithms, AOA uses the inherent propertie
The Arrow-Debreu model is a general equilibrium framework where prices adjust to clear all markets simultaneously, and consumers and firms optimize given those prices. Introduced by Kenneth Arrow and Gerard Debreu in 1954, the model extends Adam Smith's invisible hand concept into a rigorous mathematical framework. Arr
Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among
Assembly Line Balancing is the problem of distributing a sequence of assembly tasks across a series of workstations on a production line such that work is evenly distributed, idle time is minimized, and throughput constraints are satisfied. The goal is to assign tasks to stations such that the total work time at each s
The Augmented Lagrangian Method, developed by Magnus R. Hestenes and M. J. D. Powell in 1969, is a powerful technique for solving constrained optimization problems. It converts a constrained problem into a sequence of unconstrained subproblems by augmenting the Lagrangian with a quadratic penalty term, enabling efficie
AVERAGE-RANKING (Average ranking — per-alternative mean rank) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
B-WENSLO (Fuzzy WEight deNomination based on Slope coefficient (triangular fuzzy extension)) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Demir, G., Ulusoy, S. K. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
BALANCED-SPOTIS (Balanced SPOTIS — Balanced Stable Preference Ordering Towards Ideal Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Shekhovtsov, A., Dezert, J., Sałabun, W. in 2025. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible
The Bat Algorithm (BA) is a nature-inspired metaheuristic optimization method proposed by Xin-She Yang in 2010. It mimics the echolocation behavior of microbats to balance global exploration and local exploitation. Each artificial bat adjusts its position, velocity, and emission frequency, with loudness and pulse rate
Bayesian Ant Colony Optimization (BACO) is a hybrid metaheuristic that embeds Bayesian inference into the Ant Colony Optimization framework. By treating pheromone intensities or algorithm parameters as probability distributions updated with collected evidence, BACO improves convergence reliability and robustness compar
Bayesian Dynamic Programming (BDP) combines Bellman's dynamic programming framework with Bayesian inference to optimize sequential decisions when transition probabilities or reward structures are unknown. At each stage, the agent updates beliefs about the environment using observed outcomes, then computes an optimal po
A Bayesian Genetic Algorithm (BGA) replaces traditional crossover and mutation operators with a probabilistic Bayesian network learned from selected high-fitness individuals. At each generation the algorithm builds a graphical model of promising solution structure, then samples new offspring from that model, enabling t
Bayesian Goal Programming (BGP) integrates Bayesian statistical inference with classic goal programming to handle uncertainty in targets and parameters. Instead of treating goal thresholds as fixed constants, BGP encodes them as probability distributions, updates beliefs using observed data, and then solves the resulti
Bayesian Integer Programming (BIP) integrates Bayesian probabilistic reasoning with integer programming to solve combinatorial optimization problems under uncertainty. Instead of treating parameters as fixed, it encodes prior beliefs about uncertain coefficients and updates them with observed data, producing a posterio
Bayesian Linear Programming (BLP) integrates Bayesian statistical inference with classical linear programming to handle uncertainty in model parameters such as objective function coefficients, constraint coefficients, or right-hand-side values. Instead of treating parameters as fixed or governed by worst-case bounds, B
Bayesian Mixed-Integer Programming (BO-MIP) couples a probabilistic surrogate model — typically a Gaussian process — with a mixed-integer programming solver to efficiently optimize expensive black-box objectives defined over spaces that contain both continuous and discrete or integer-valued decision variables. It is es
Bayesian Nash Equilibrium (BNE) extends Nash Equilibrium to games with incomplete information, where players lack full knowledge of others' payoff functions. Introduced by John Harsanyi in 1967, BNE models strategic interaction under uncertainty by representing unknown payoffs as players' private types drawn from a pro
Bayesian NSGA-II integrates Gaussian process surrogate models (Bayesian metamodels) into the NSGA-II evolutionary loop to solve expensive multi-objective optimization problems. By replacing costly true function evaluations with fast probabilistic predictions, it discovers high-quality Pareto-front approximations with f
Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate mode
Bayesian Particle Swarm Optimization (Bayesian PSO) integrates Bayesian probabilistic reasoning into the standard particle swarm framework. Particles update their velocities and positions guided not only by personal and global best positions but also by a Bayesian posterior that encodes prior knowledge about the soluti
Bayesian Queueing Simulation combines Bayesian statistical inference with stochastic queueing simulation to model waiting-line systems under parameter uncertainty. Instead of treating arrival and service rates as fixed known values, it places prior distributions over them, updates these with observed data to obtain pos
Bayesian Simulated Annealing (BSA) integrates Bayesian prior knowledge about the objective landscape into the simulated annealing search process. By encoding beliefs about promising regions as prior distributions and updating them as the search progresses, BSA focuses computational effort on high-probability areas of t
Bayesian Six Sigma DMAIC integrates Bayesian statistical inference into the classical Define-Measure-Analyze-Improve-Control quality-improvement framework. Rather than relying solely on frequentist hypothesis tests and point estimates, it incorporates prior knowledge — from expert judgment, historical production data,
Bayesian Tabu Search (BTS) is a hybrid metaheuristic that couples the memory-based forbidden-move mechanism of classic Tabu Search with a Bayesian probabilistic model. The Bayesian component learns from past evaluations to score candidate moves, focusing the search on promising regions while the tabu list prevents cycl
The Beck Anxiety Inventory (BAI) is a 21-item self-report scale designed to measure the severity of somatic and cognitive symptoms of anxiety in adolescents and adults. Developed by Aaron T. Beck and Robert A. Steer in 1993, the BAI is widely used in clinical assessment, treatment monitoring, and research to quantify a
The Beck Depression Inventory (BDI) is a 21-item self-report instrument designed to measure the severity of depressive symptoms in adolescents and adults. Developed by Aaron T. Beck in 1961 and revised as the BDI-II in 1996, it has become one of the most widely used screening and monitoring tools in clinical psychology
The Beck Depression Inventory-II is a 21-item self-report instrument designed to assess the presence and severity of depressive symptoms in adolescents and adults. Originally published by Aaron T. Beck in 1961 and revised significantly in 1996, the BDI-II is one of the most widely used depression assessment tools in cl
Belief Rule Base (BRB), introduced by Yang et al. in 2006 under the RIMER framework, is an expert-system inference methodology that extends classical if-then rules by attaching belief degree distributions to rule consequents. It combines rule-based reasoning with the Evidential Reasoning (ER) approach, enabling the rep
The Bellman-Ford Algorithm, developed by Richard Bellman and Lester R. Ford in the 1950s, is a fundamental algorithm for computing shortest paths in weighted graphs that may contain negative edge weights. Unlike Dijkstra's algorithm, it correctly handles negative weights and can detect the presence of negative-weight c
Benders Decomposition, introduced by Jacques F. Benders in 1962, is a powerful algorithmic framework for solving large-scale mixed-integer programming (MIP) problems. It decomposes the problem into a master problem (controlling complicating variables) and subproblems (handling remaining variables), using cutting planes