What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical optimization
- Normal distribution
His primary scientific interests are in Mathematical optimization, Genetic algorithm, Inventory control, Fuzzy logic and Simulated annealing.
His study in the fields of Particle swarm optimization under the domain of Mathematical optimization overlaps with other disciplines such as Vendor.
The various areas that Seyed Taghi Akhavan Niaki examines in his Genetic algorithm study include Harmony search, Pareto principle, Inventory cost, Sorting and Constraint.
The study incorporates disciplines such as Budget constraint, Type, Independent and identically distributed random variables and Sensitivity in addition to Inventory control.
His work on Fuzzy number and Defuzzification as part of general Fuzzy logic research is frequently linked to Economic order quantity, thereby connecting diverse disciplines of science.
His biological study spans a wide range of topics, including Supply chain network, Optimization problem and Time horizon.
His most cited work include:
- A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model (108 citations)
- Fault Diagnosis in Multivariate Control Charts Using Artificial Neural Networks (107 citations)
- Multi-response simulation optimization using genetic algorithm within desirability function framework (102 citations)
What are the main themes of his work throughout his whole career to date?
Seyed Taghi Akhavan Niaki mainly investigates Mathematical optimization, Genetic algorithm, Control chart, Statistics and Algorithm.
His study in Mathematical optimization focuses on Particle swarm optimization in particular.
His Genetic algorithm research includes elements of Simulated annealing, Pareto principle, Sorting, Fuzzy logic and Optimization problem.
His Control chart research integrates issues from Estimator, Statistical process control, Chart and Autoregressive model.
His Estimator study combines topics in areas such as Point estimation and Monte Carlo method.
His Statistics research is multidisciplinary, incorporating perspectives in Quality and Shewhart individuals control chart.
He most often published in these fields:
- Mathematical optimization (46.62%)
- Genetic algorithm (23.79%)
- Control chart (19.94%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (46.62%)
- Genetic algorithm (23.79%)
- Quality (9.65%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Mathematical optimization, Genetic algorithm, Quality, Statistics and Reliability.
Seyed Taghi Akhavan Niaki has researched Mathematical optimization in several fields, including Taguchi methods and Total cost.
His studies in Genetic algorithm integrate themes in fields like Particle swarm optimization, Sorting, Lagrangian relaxation, Benchmark and Optimization problem.
His research in Quality intersects with topics in Environmental economics and Process management.
Seyed Taghi Akhavan Niaki has included themes like Control chart and Statistical process monitoring in his Statistics study.
The concepts of his Control chart study are interwoven with issues in Chart and Support vector machine.
Between 2017 and 2021, his most popular works were:
- Modeling and solving a sustainable closed loop supply chain problem with pricing decisions and discounts on returned products (48 citations)
- An optimal integrated lot sizing policy of inventory in a bi-objective multi-level supply chain with stochastic constraints and imperfect products (40 citations)
- A closed-loop supply chain considering carbon reduction, quality improvement effort, and return policy under two remanufacturing scenarios (27 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical optimization
- Artificial intelligence
Seyed Taghi Akhavan Niaki mainly focuses on Mathematical optimization, Genetic algorithm, Benchmark, Quality and Taguchi methods.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Total cost and Nonlinear programming.
His Genetic algorithm research incorporates themes from Sorting, Algorithm, Particle swarm optimization and Artificial intelligence.
His Benchmark research is multidisciplinary, incorporating perspectives in Optimization problem and Crossover.
The Quality study combines topics in areas such as Control engineering, Statistic and U-statistic.
The various areas that Seyed Taghi Akhavan Niaki examines in his Taguchi methods study include Budget constraint, Service level, Random variable, Location-allocation and Learning effect.
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