What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Microeconomics
His scientific interests lie mostly in Metaheuristic, Supply chain network, Algorithm, Closed loop and Mathematical optimization.
His Metaheuristic research is multidisciplinary, relying on both Optimization problem and Operations research.
His Optimization problem research includes elements of Theoretical computer science and Combinatorial optimization.
His Supply chain network research encompasses a variety of disciplines, including Competitive advantage, Sustainability, Industrial engineering and Total cost.
Mostafa Hajiaghaei-Keshteli studies Algorithm, focusing on Tree in particular.
His study involves Genetic algorithm and Transportation theory, a branch of Mathematical optimization.
His most cited work include:
- Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks (98 citations)
- The Social Engineering Optimizer (SEO) (75 citations)
- A bi-objective green home health care routing problem (60 citations)
What are the main themes of his work throughout his whole career to date?
Mostafa Hajiaghaei-Keshteli mostly deals with Metaheuristic, Operations research, Mathematical optimization, Supply chain network and Algorithm.
The study incorporates disciplines such as Optimization problem, Heuristics and Heuristic in addition to Metaheuristic.
His work on Location-allocation as part of general Operations research research is often related to Order, Vehicle routing problem and Reverse logistics, thus linking different fields of science.
His work in the fields of Transportation theory, Genetic algorithm and Simulated annealing overlaps with other areas such as Taguchi methods and Set.
His Genetic algorithm research focuses on Scheduling and how it relates to Real-time computing.
He usually deals with Algorithm and limits it to topics linked to Lagrangian relaxation and Duration.
He most often published in these fields:
- Metaheuristic (37.14%)
- Operations research (35.71%)
- Mathematical optimization (35.71%)
What were the highlights of his more recent work (between 2019-2021)?
- Supply chain network (32.86%)
- Operations research (35.71%)
- Metaheuristic (37.14%)
In recent papers he was focusing on the following fields of study:
Mostafa Hajiaghaei-Keshteli mainly investigates Supply chain network, Operations research, Metaheuristic, Mathematical optimization and Total cost.
Many of his studies on Supply chain network apply to Vendor-managed inventory as well.
His Operations research study incorporates themes from Linear programming and Process.
His Metaheuristic research is multidisciplinary, incorporating elements of Genetic algorithm and Heuristics.
His work deals with themes such as Hybrid algorithm and Solver, which intersect with Genetic algorithm.
His Mathematical optimization research includes elements of Closed loop, Financial risk, Set, Strengths and weaknesses and Integer.
Between 2019 and 2021, his most popular works were:
- An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem (44 citations)
- Red deer algorithm (RDA): a new nature-inspired meta-heuristic (41 citations)
- A set of efficient heuristics for a home healthcare problem (31 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Algorithm
- Microeconomics
His primary scientific interests are in Supply chain network, Metaheuristic, Operations research, Mathematical optimization and Lagrangian relaxation.
His Supply chain network research includes a combination of various areas of study, such as Vendor and Holding cost.
His research is interdisciplinary, bridging the disciplines of Heuristics and Metaheuristic.
He frequently studies issues relating to Gross profit and Operations research.
His Mathematical optimization research is mostly focused on the topic Linear programming.
Mostafa Hajiaghaei-Keshteli works mostly in the field of Lagrangian relaxation, limiting it down to concerns involving Duration and, occasionally, Algorithm.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
