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LINEAR-SUM-NORMALIZATION (Linear Sum Normalization — column-sum division (probability / stochastic normalisation)) is a normalization multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z., Peldschus, F., Kaklauskas, A. in 1994. It turns a decision matrix of alternatives scored on mult
LINMAP (LINear programming technique for Multidimensional Analysis of Preference) is a ranking multi-criteria decision-making (MCDM) method introduced by Srinivasan, V., Shocker, A. D. in 1973. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Little's Law is a fundamental theorem in queueing theory that relates the long-run average number of items in a stable system (L) to the long-run average arrival rate (λ) and the long-run average time an item spends in the system (W), expressed as L = λW. Introduced and rigorously proved by John D. C. Little in 1961, t
LMAW (Logarithm Methodology of Additive Weights) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Žižović, M., Biswas, S., Božanić, D. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
LOCAL-OWA (neighbourhood-adaptive Ordered Weighted Averaging) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J.; Liu, X. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
LOCAL-WLC (Local WLC — neighbourhood-adaptive weighted linear combination) is a ranking multi-criteria decision-making (MCDM) method introduced by Malczewski, J. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
LODECI (LOgarithmic DEcomposition of Criteria Importance) is a weight objective multi-criteria decision-making (MCDM) method introduced by Pala, O. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
LOGARITHMIC-NORMALIZATION (Logarithmic Normalization — log-ratio column normalisation for multiplicative aggregation contexts) is a normalization multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Turskis, Z. in 2008. It turns a decision matrix of alternatives scored on multiple criteria into
LOPCOW (LOgarithmic Percentage Change-driven Objective Weighting) is a weight objective multi-criteria decision-making (MCDM) method introduced by Ecer, F., Pamučar, D. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
LOPM (LoPM — Limits on Property Method) is a ranking multi-criteria decision-making (MCDM) method introduced by Farag, M. M. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
LPF-EDAS (LPF-CRITIC-EDAS — Linguistic Pythagorean Fuzzy EDAS with CRITIC weighting (Akram-Ramzan-Deveci 2023)) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Ramzan, N., Deveci, M. in 1995 crisp; 2023 variant applicator. It turns a decision matrix of alternatives scored on multiple
The Level of Service Inventory-Revised (LSI-R) is a 54-item assessment instrument developed by Andrews and Bonta (1995) to measure offender risk level and criminogenic needs (dynamic risk factors related to criminal behavior) in criminal justice populations. It is grounded in the Risk-Need-Responsivity (RNR) model of o
The M/M/1 queue is the foundational single-server queueing model in which customers arrive according to a Poisson process with rate λ, are served one at a time by a single server with exponentially distributed service times at rate μ, and wait in an infinite-capacity first-come-first-served queue. Formalized within the
The M/M/c queue is a multi-server stochastic model in which customers arrive according to a Poisson process at rate λ, are served by c identical servers each with exponentially distributed service times at rate μ, and wait in a single common queue when all servers are busy. Systematized within classical queueing theory
MABAC (Multi-Attributive Border Approximation area Comparison) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Ćirović, G. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MACBETH (Measuring Attractiveness by a Categorical-Based Evaluation Technique) is a weight subjective multi-criteria decision-making (MCDM) method introduced by Bana e Costa, C. A., Vansnick, J.-C. in 1994. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
The Maclaurin Symmetric Mean (MSM) operator is an aggregation method that combines multiple criteria or attribute values using symmetric mean functions. Unlike simple averaging, MSM captures interactions between criteria and enables flexible sensitivity to criterion magnitudes through a parameter λ. It is particularly
MACONT (Mixed Aggregation by Comprehensive Normalization Technique) is a ranking multi-criteria decision-making (MCDM) method introduced by Wen, Z. Liao, H. Zavadskas, E. K. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MAHALANOBIS-DISTANCE (Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations) is a distance multi-criteria decision-making (MCDM) method introduced by Mahalanobis, P. C. in 1936. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducib
Maintenance Optimization is a quantitative framework for determining the timing, type, and frequency of maintenance actions—preventive, predictive, or corrective—that minimize total cost or expected downtime over a system's operational life. Systematic formulations were consolidated by Hongzhou Wang (2002), whose surve
MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Pamučar, D., Vasin, Lj., Lukovac, V. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
The Malmquist Productivity Index (MPI) is a non-parametric measure of total factor productivity (TFP) change over time. Formally grounded in distance functions by Caves, Christensen, and Diewert (1982) and operationalized using Data Envelopment Analysis by Färe, Grosskopf, Norris, and Zhang (1994), MPI decomposes produ
The Malmquist-Luenberger (ML) Productivity Index combines concepts from the Malmquist index and Luenberger's directional distance functions to measure total factor productivity (TFP) change over time. It decomposes productivity growth into technical efficiency change and technological progress, enabling comprehensive p
MARA (Magnitude of the Area for the Ranking of Alternatives) is a ranking multi-criteria decision-making (MCDM) method introduced by Gligorić, M., Gligorić, Z., Lutovac, S., Negovanović, M., Lugijevskij, R. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible r
MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) is a ranking multi-criteria decision-making (MCDM) method introduced by Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible res
The Maslach Burnout Inventory (MBI) is the most widely used instrument for measuring occupational burnout—a syndrome of emotional exhaustion, depersonalization, and reduced personal accomplishment in response to chronic workplace stress. Developed by Christina Maslach and Susan Jackson in the early 1980s, the MBI has b
Material Requirements Planning (MRP) is a computerized system developed by Joseph Orlicky in the 1970s that calculates material requirements based on master production schedules and bill-of-materials data. MRP determines what materials to buy, how much to order, and when to order them to meet production demand while mi
Matheuristics is a class of hybrid optimization methods that tightly couple exact mathematical programming components—such as mixed-integer programming (MIP) solvers—with metaheuristic search procedures. Formally introduced and named by Maniezzo, Stützle, and Voß in 2009, the framework leverages the global-search capab
MAUT (Multi-Attribute Utility Theory) is a ranking multi-criteria decision-making (MCDM) method introduced by Keeney, R. L., Raiffa, H. in 1976. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MEDIAN-RANKING (Median ranking — per-alternative median 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.
A Memetic Algorithm (MA) is a population-based metaheuristic that combines the global exploration of an evolutionary algorithm with the local exploitation of individual learning procedures. Introduced by Pablo Moscato in 1989 at Caltech, MAs draw on Richard Dawkins' concept of the meme — a unit of cultural transmission
MEREC (MEthod based on the Removal Effects of Criteria) is a weight objective multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Antucheviciene, J., Turskis, Z. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured
MEREC-G (Method Based on Removal Effects of Criteria - Generalized) is an objective weight derivation method that assigns weights based on the impact of removing each criterion from the decision analysis. The core idea is that important criteria, when removed, cause large changes in the final ranking. Generalized varia
The Multidimensional Fatigue Inventory is a 20-item self-report instrument that comprehensively measures five distinct dimensions of fatigue: general fatigue, physical fatigue, reduced activity, reduced motivation, and mental fatigue. Developed by Smets and colleagues in 1995, the MFI-20 is grounded in a theoretical mo
MHF-TOPSIS (m-Polar Hesitant Fuzzy extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A., Alcantud, J.C.R. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MIN-MAX-NORMALIZATION (Min-Max Normalization — linear rescaling of each criterion column to [0, 1]) is a normalization 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.
Mixed-Integer Programming (MIP) is a mathematical optimization framework in which some decision variables must take integer values while others may be continuous. It generalizes linear programming and is widely used in operations research, logistics, scheduling, resource allocation, and engineering design, where indivi
The Minnesota Multiphasic Personality Inventory (MMPI) is a 567-item standardized self-report inventory designed to assess personality traits, psychopathology, and behavioral tendencies in adults. Originally published in 1943 and revised as the MMPI-2 in 1989 and the MMPI-2-RF in 2008, the MMPI remains the most widely
MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MOORA (Multi-Objective Optimisation by Ratio Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Brauers, W. K. M., Zavadskas, E. K. in 2006. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MOOSRA (Multi-Objective Optimization on the basis of Simple Ratio Analysis) is a ranking multi-criteria decision-making (MCDM) method introduced by Sarkar, A. Panja, S. C. Das, D. Sarkar, B. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MPF-DOMBI-WA (m-Polar Fuzzy Dombi Weighted Averaging / Geometric MCDM (Akram, Yaqoob, Ali & Chammam 2020 / Akram & Adeel 2023 Ch. 8) — single-DM m-PF MCDM ranking via Dombi t-conorm/t-norm-based aggregation operators (mFDWA primary, mFDWG companion) followed by m-PF score-based descending ordering) is a aggregationoper
MPF-ELECTRE-I (m-Polar Fuzzy extension of ELECTRE-I (Akram, Waseem & Liu 2019)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Waseem, N., Liu, P. in 2019. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
MPF-ELECTRE-II (m-Polar Fuzzy extension of ELECTRE-II for multi-criteria group decision making (Akram & Adeel 2023)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproduc
MPF-ELECTRE-III (m-Polar Fuzzy extension of ELECTRE-III with pseudo-criterion thresholds, Shannon-entropy objective weights and Li–Wang net credibility ranking (Akram & Adeel 2023)) is a outranking multi-criteria decision-making (MCDM) method introduced by Akram, M., Adeel, A. in 2023. It turns a decision matrix of alt
MPF-ELECTRE-IV (m-Polar Fuzzy extension of ELECTRE-IV with weight-free pseudo-criterion outranking, five dominance classes (quasi/canonical/pseudo/sub/veto), Vallée–Zielniewicz credibility levels and Belton–Stewart ascending+descending distillation (Akram & Adeel 2023)) is a outranking multi-criteria decision-making (M
MPF-HF-TOPSIS (m-Polar Hesitant Fuzzy TOPSIS (Akram, Adeel & Alcantud 2019, Symmetry 11(6):795) — multi-criteria group decision-making by extending TOPSIS to the m-polar hesitant fuzzy (mHF) set framework; pole-wise mHPIS/mHNIS extraction, mHF Euclidean distance and closeness coefficient ranking) is a ranking multi-cri
MPF-PROMETHEE (m-Polar Fuzzy extension of PROMETHEE I/II with AHP-derived crisp weights, six Brans–Vincke generalized criteria preference functions, and positive/negative/net outranking flows (Akram, Shumaiza & Alcantud 2020 / Akram & Adeel 2023 Ch. 7)) is a outranking multi-criteria decision-making (MCDM) method intro
MPF-TOPSIS-LING (m-Polar Fuzzy Linguistic TOPSIS for MCGDM (Adeel, Akram & Koam 2019, Symmetry 11(6):735) — multi-criteria group decision-making via m-polar fuzzy linguistic variables (mFLV), expert-aggregated m-PF linguistic decision matrix, aggregated linguistic-term-set weights, m-PF linguistic positive/negative ide
MPSI (Modified PSI method for objective weighting (Modified Preference Selection Index weighting)) is a weight objective multi-criteria decision-making (MCDM) method introduced by Maniya, K., Bhatt, M. G. in 2010. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible res
Multi-Objective Ant Colony Optimization (MOACO) is a swarm-intelligence metaheuristic that extends the classic Ant Colony Optimization framework to simultaneously optimize two or more conflicting objectives. Artificial ants construct candidate solutions guided by pheromone trails and heuristic information, progressivel
Multi-Objective Dynamic Programming (MODP) extends Bellman's classical dynamic programming to settings where a decision-maker must optimize several competing objectives simultaneously across a sequence of stages. Rather than a single optimal policy, it produces a Pareto-optimal set of policies — each representing a dis
A Multi-Objective Genetic Algorithm (MOGA) is an evolutionary computation method that evolves a population of candidate solutions toward a Pareto-optimal front, simultaneously optimizing two or more conflicting objective functions. It avoids collapsing trade-offs into a single score, instead producing a set of non-domi
Multi-Objective Goal Programming (MOGP) is a mathematical programming technique that simultaneously pursues several aspirational targets by minimizing weighted deviations from each goal. Rooted in Charnes and Cooper's original goal programming framework (1961), MOGP extends it to handle multiple competing objectives, m
Multi-Objective Linear Programming (MOLP) extends classical linear programming to handle several conflicting linear objective functions simultaneously over a feasible region defined by linear constraints. Instead of a single optimal solution, MOLP produces a Pareto-efficient frontier from which a decision-maker selects
Multi-Objective Mixed-Integer Programming (MO-MIP) is an optimization framework that simultaneously optimizes two or more conflicting objective functions subject to linear or nonlinear constraints, where some decision variables are restricted to integer values and others are continuous. It is widely applied in engineer
Multi-Objective Particle Swarm Optimization (MOPSO) is a swarm-intelligence metaheuristic that extends the original Particle Swarm Optimization (PSO) to handle multiple conflicting objective functions simultaneously. It maintains an external Pareto archive and uses dominance-based selection to guide a population of can
Multi-objective queueing simulation combines discrete-event queueing models with multi-objective optimization to simultaneously evaluate and optimize conflicting performance metrics — such as average wait time, server utilization, throughput, and service cost — across a simulated queuing system. It produces a Pareto fr
Multi-Objective Simulated Annealing (MOSA) extends the classical simulated annealing metaheuristic to problems with two or more conflicting objective functions. Instead of converging to a single optimum, MOSA explores the solution space stochastically and maintains an archive of non-dominated (Pareto-optimal) solutions
Multi-objective Tabu Search (MOTS) is a metaheuristic algorithm that extends the classic Tabu Search framework to simultaneously optimize two or more conflicting objective functions. Instead of a single optimum, it seeks to approximate the Pareto front — the set of solutions where no objective can be improved without w