Kfir Arviv
Papers
3
Total Citations
96
H-Index
3
About
Kfir Arviv is a leading researcher in the intersection of robotics, operations research, and production scheduling, with a particular focus on multi-robot systems and flow-shop optimization. His work addresses the critical challenge of coordinating autonomous agents in manufacturing environments to maximize efficiency. Arviv’s most influential contribution is his pioneering study on collaborative reinforcement learning for a two-robot job transfer flow-shop scheduling problem (55 citations), where he defined and evaluated four distinct levels of robot collaboration—from independent operation to full joint learning with shared information. This framework demonstrated how varying degrees of information sharing and cooperative learning can dramatically improve system performance. His additional research on combined robot selection and scheduling under no-wait restrictions (22 citations) and optimal scheduling to minimize makespan in three-machine flow-shops with job-independent processing times (19 citations) further solidifies his expertise in developing computationally efficient solutions for complex scheduling problems. Arviv’s work is essential reading for researchers and students in industrial engineering, robotics, and AI-driven manufacturing, offering foundational insights into how collaborative learning can transform automated production systems.
Research Focus
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