What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Information technology
- Machine learning
The scientist’s investigation covers issues in Supply chain risk management, Risk analysis, Supply chain engineering, Industry 4.0 and Supply chain management.
His Risk analysis research is multidisciplinary, incorporating perspectives in Lead time, Ripple effect, Quantitative analysis and Operations research.
His Ripple effect research is multidisciplinary, incorporating elements of Robustness, Resilience, Analytics and Big data.
Boris Sokolov has researched Industry 4.0 in several fields, including Scheduling, Manufacturing engineering and Flow shop scheduling.
While the research belongs to areas of Flow shop scheduling, he spends his time largely on the problem of Production manager, intersecting his research to questions surrounding Optimal control and Industrial engineering.
His work in the fields of Supply chain management, such as Service management, intersects with other areas such as Operations management.
His most cited work include:
- 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)
- The Ripple effect in supply chains: trade-off 'efficiency-flexibility-resilience' in disruption management (229 citations)
What are the main themes of his work throughout his whole career to date?
Boris Sokolov focuses on Systems engineering, Control, Scheduling, Mathematical optimization and Optimal control.
His study in Systems engineering is interdisciplinary in nature, drawing from both Risk analysis and Distributed computing.
His Risk analysis study combines topics in areas such as Robustness and Big data.
His Control research includes elements of Control engineering, Structure and Control theory.
His biological study spans a wide range of topics, including Industrial engineering, Industry 4.0, Dynamic priority scheduling, Job shop scheduling and Operations research.
His research in the fields of Linear programming overlaps with other disciplines such as Schedule.
He most often published in these fields:
- Systems engineering (16.58%)
- Control (17.09%)
- Scheduling (13.57%)
What were the highlights of his more recent work (between 2017-2021)?
- Control (17.09%)
- Risk analysis (11.56%)
- Industry 4.0 (7.04%)
In recent papers he was focusing on the following fields of study:
Control, Risk analysis, Industry 4.0, State and Scheduling are his primary areas of study.
The concepts of his Control study are interwoven with issues in Stability, Control theory, Control engineering, Control theory and Aerospace engineering.
His Risk analysis research incorporates themes from Ripple effect and Big data.
His work blends Industry 4.0 and Supply chain management studies together.
The various areas that Boris Sokolov examines in his Scheduling study include Algorithm, Job shop scheduling and Optimal control.
Supply chain risk management is closely attributed to Supply chain engineering in his work.
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)
- Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications (90 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Information technology
- Machine learning
His primary scientific interests are in Industry 4.0, Risk analysis, Supply chain engineering, Ripple effect and Scheduling.
His study focuses on the intersection of Industry 4.0 and fields such as Analytics with connections in the field of Conceptual framework.
His research on Supply chain engineering often connects related topics like Supply chain risk management.
The Ripple effect study combines topics in areas such as Structural robustness, Robustness, Robustness and Risk aversion.
His Scheduling study incorporates themes from Algorithm, State, Optimal control and Computer experiment.
His work on Supply chain optimization as part of general Supply chain management study is frequently linked to Systems theory, bridging the gap between disciplines.
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