What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical optimization
- Programming language
His main research concerns Supply chain, Risk analysis, Mathematical optimization, Supply chain engineering and Operations research.
His research in Supply chain intersects with topics in Industry 4.0, Scheduling, Ripple effect and Resilience.
His work carried out in the field of Risk analysis brings together such families of science as Enterprise risk management, Marketing, Service level and Systems engineering.
His Mathematical optimization research is multidisciplinary, relying on both Probability distribution, Workstation and Random variable.
His Supply chain engineering course of study focuses on Manufacturing engineering and Dynamic pricing and Outsourcing.
His work deals with themes such as Supply and demand, Microeconomics, Profit, Nash equilibrium and Mathematical model, which intersect with Operations research.
His most cited work include:
- A taxonomy of line balancing problems and their solutionapproaches (400 citations)
- The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (284 citations)
- A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0 (270 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Mathematical optimization, Supply chain, Machining, Operations research and Lead time.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Workstation, Set and Transfer line.
His Supply chain research is multidisciplinary, incorporating perspectives in Risk analysis, Ripple effect and Resilience.
His Machining research includes themes of Transfer, Manufacturing engineering and Engineering drawing.
Operations research and Production are commonly linked in his work.
The study incorporates disciplines such as Supply planning, Component and Inventory control in addition to Lead time.
He most often published in these fields:
- Mathematical optimization (37.01%)
- Supply chain (14.25%)
- Machining (12.15%)
What were the highlights of his more recent work (between 2017-2021)?
- Supply chain (14.25%)
- Mathematical optimization (37.01%)
- Risk analysis (5.31%)
In recent papers he was focusing on the following fields of study:
Alexandre Dolgui mainly investigates Supply chain, Mathematical optimization, Risk analysis, Resilience and Production.
His Supply chain research includes elements of Industry 4.0 and Ripple effect.
His studies deal with areas such as Lead time and Component as well as Mathematical optimization.
His Risk analysis study integrates concerns from other disciplines, such as Disruption risk and Risk analysis.
His studies in Resilience integrate themes in fields like Supply chain resilience and Control reconfiguration.
His Production study incorporates themes from Multi-objective optimization, Manufacturing engineering and Line.
Between 2017 and 2021, his most popular works were:
- The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (284 citations)
- Ripple effect in the supply chain: an analysis and recent literature (214 citations)
- Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak (167 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Programming language
- Artificial intelligence
His primary scientific interests are in Supply chain, Risk analysis, Industry 4.0, Supply chain risk management and Ripple effect.
His work in the fields of Supply chain engineering, Supply chain resilience and Supply chain management overlaps with other areas such as Ripple.
His study in Risk analysis is interdisciplinary in nature, drawing from both Disruption risk and Risk aversion.
Alexandre Dolgui interconnects Optimal control, Scheduling, Operations management, Control and Analytics in the investigation of issues within Industry 4.0.
His Downstream study also includes
- Bullwhip effect together with Control theory,
- Bayesian probability that connect with fields like Mathematical optimization.
His work on Stochastic programming as part of general Mathematical optimization study is frequently linked to Hazardous waste, bridging the gap between disciplines.
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