TY - GEN
T1 - Process Dynamics-Aware Flexible Manufacturing for Industry 4.0
AU - Balszun, Michael
AU - Hobbs, Clara
AU - Fraccaroli, Enrico
AU - Roy, Debayan
AU - Fummi, Franco
AU - Chakraborty, Samarjit
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper studies the following basic flexible manufacturing problem: Given N machines that can perform the same job on a production item (e.g., drilling or tapping) but with different capabilities (e.g., energy requirements and speeds), what is an optimal schedule for the job on these machines? While this is a well-studied problem, the main innovation this paper introduces is the explicit modeling of the underlying process dynamics - i.e., the physical interaction of the item and the machine - using differential equations. The resulting scheduling problem is in a hybrid systems setting that involves determining the transition times between states, where the system evolution in each state is defined by differential equations. To the best of our knowledge, such a cyber-physical systems (CPS) oriented approach to machine scheduling has not been studied before, although it lies at the core of flexible manufacturing in Industry 4.0. We believe that this new formulation might lead to a renewed interest in machine scheduling problems, but now in a hybrid/CPS-oriented setting.
AB - This paper studies the following basic flexible manufacturing problem: Given N machines that can perform the same job on a production item (e.g., drilling or tapping) but with different capabilities (e.g., energy requirements and speeds), what is an optimal schedule for the job on these machines? While this is a well-studied problem, the main innovation this paper introduces is the explicit modeling of the underlying process dynamics - i.e., the physical interaction of the item and the machine - using differential equations. The resulting scheduling problem is in a hybrid systems setting that involves determining the transition times between states, where the system evolution in each state is defined by differential equations. To the best of our knowledge, such a cyber-physical systems (CPS) oriented approach to machine scheduling has not been studied before, although it lies at the core of flexible manufacturing in Industry 4.0. We believe that this new formulation might lead to a renewed interest in machine scheduling problems, but now in a hybrid/CPS-oriented setting.
UR - https://www.scopus.com/pages/publications/85141347062
U2 - 10.1109/CASE49997.2022.9926495
DO - 10.1109/CASE49997.2022.9926495
M3 - Conference contribution
AN - SCOPUS:85141347062
T3 - IEEE International Conference on Automation Science and Engineering
SP - 2375
EP - 2382
BT - 2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PB - IEEE Computer Society
T2 - 18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Y2 - 20 August 2022 through 24 August 2022
ER -