Sarmad Riazi

About

Sarmad Riazi is a researcher specializing in energy optimization, robotics, and automation systems, with a particular focus on reducing energy consumption and peak power demands in industrial environments. His work has made significant contributions to the field of sustainable manufacturing, demonstrating that intelligent trajectory planning and coordination algorithms can yield dramatic efficiency gains in real-world robotic systems. Riazi's most influential contributions stem from his work on robot trajectory optimization, where his algorithms have achieved reductions of up to 30% in energy consumption and an impressive 60% in peak power for industrial robots — findings validated on physical hardware as part of the EU-funded AREUS project. His research extends beyond single robots: his 2020 study on large-scale Automated Guided Vehicle (AGV) systems, with 58 citations, addresses multi-objective optimization balancing makespan, lateness, tardiness, and energy use simultaneously. Earlier work demonstrated up to 45% energy savings in multi-robot systems through the Sequence Planner tool. With a cumulative citation count exceeding 260 across his top publications, Riazi has established himself as a meaningful voice in energy-efficient automation. His contributions to hybrid systems modeling and convex optimization techniques further underscore his broad technical versatility within industrial cyber-physical systems research.

Research Focus

Key Achievements

Top Papers

1. 1
   Energy and Peak Power Optimization of Time-Bounded Robot Trajectories

   60 citations · 2017
2. 2
   Energy Optimization of Large-Scale AGV Systems

   58 citations · 2020
3. 3
   Productivity/energy optimisation of trajectories and coordination for cyclic multi-robot systems

   56 citations · 2017
4. 4
   Energy optimization of multi-robot systems

   43 citations · 2015
5. 5
   Energy and peak-power optimization of existing time-optimal robot trajectories

   20 citations · 2016
6. 6
   Modeling and Optimization of Hybrid Systems for the Tweeting Factory

   12 citations · 2015
7. 7
   Energy and peak-power optimization of time-bounded robot trajectories

   6 citations · 2017
8. 8
   Computationally efficient energy optimization of multiple robots

   5 citations · 2017
9. 9
   From trapezoid to polynomial: Next-generation energy-efficient robot trajectories

   4 citations · 2017
10. 10
    Modeling and Optimization of Hybrid Systems**This work was carried out within the Wingquist Laboratory VINN Excellence Centre within the Area of Advance - Production at Chalmers, and supported by VINNOVA and the Swedish Science Foundation. The support is gratefully acknowledged.

    2 citations · 2015

Key Collaborators

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