Details
Original language | English |
---|---|
Article number | 100125 |
Journal | IFAC Journal of Systems and Control |
Volume | 15 |
Early online date | 26 Nov 2020 |
Publication status | Published - Mar 2021 |
Abstract
Efficient operation of supply chain systems by means of distributed model predictive control is studied in this work. The main focus is on the exploitation and sharing of predictive information on delivery and demand throughout the supply chain. Based on the availability of customer demand predictions, which are assumed to be reliable to some extent, two distributed model predictive control algorithms for supply chain operation are proposed, analyzed and investigated in numerical simulations. The mechanisms employed for information exchange throughout the supply chain differ in both approaches. The first approach establishes and implements the exchange of semi-accurate predictions, which explicitly requires predicted trajectories to only vary slightly from one time step to the next. In the second approach, information exchange is rather indirect by means of terminal constraints in the local MPC formulations, explicitly relying on the stock and flow nature of the overall system. The two approaches considerably differ in terms of system setup, requirements and corresponding results, and hence provide a flexible framework for leveraging predictive information in supply chain system management. As such, they form a basis for further investigations towards the ultimate goal of quantifying the value of predictive information in supply chain systems.
Keywords
- Complex logistic systems, Decentralized and distributed control, Distributed economic model predictive control, Modeling and decision making in complex systems, Multiagent systems
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computer Networks and Communications
- Decision Sciences(all)
- Management Science and Operations Research
- Mathematics(all)
- Modelling and Simulation
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In: IFAC Journal of Systems and Control, Vol. 15, 100125, 03.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Distributed economic model predictive control for cooperative supply chain management using customer forecast information
AU - Köhler, Philipp N.
AU - Müller, Matthias A.
AU - Pannek, Jürgen
AU - Allgöwer, Frank
N1 - Funding Information: Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant AL 316/11-2 and under Germany’s Excellence Strategy - EXC 2075 - 390740016 .
PY - 2021/3
Y1 - 2021/3
N2 - Efficient operation of supply chain systems by means of distributed model predictive control is studied in this work. The main focus is on the exploitation and sharing of predictive information on delivery and demand throughout the supply chain. Based on the availability of customer demand predictions, which are assumed to be reliable to some extent, two distributed model predictive control algorithms for supply chain operation are proposed, analyzed and investigated in numerical simulations. The mechanisms employed for information exchange throughout the supply chain differ in both approaches. The first approach establishes and implements the exchange of semi-accurate predictions, which explicitly requires predicted trajectories to only vary slightly from one time step to the next. In the second approach, information exchange is rather indirect by means of terminal constraints in the local MPC formulations, explicitly relying on the stock and flow nature of the overall system. The two approaches considerably differ in terms of system setup, requirements and corresponding results, and hence provide a flexible framework for leveraging predictive information in supply chain system management. As such, they form a basis for further investigations towards the ultimate goal of quantifying the value of predictive information in supply chain systems.
AB - Efficient operation of supply chain systems by means of distributed model predictive control is studied in this work. The main focus is on the exploitation and sharing of predictive information on delivery and demand throughout the supply chain. Based on the availability of customer demand predictions, which are assumed to be reliable to some extent, two distributed model predictive control algorithms for supply chain operation are proposed, analyzed and investigated in numerical simulations. The mechanisms employed for information exchange throughout the supply chain differ in both approaches. The first approach establishes and implements the exchange of semi-accurate predictions, which explicitly requires predicted trajectories to only vary slightly from one time step to the next. In the second approach, information exchange is rather indirect by means of terminal constraints in the local MPC formulations, explicitly relying on the stock and flow nature of the overall system. The two approaches considerably differ in terms of system setup, requirements and corresponding results, and hence provide a flexible framework for leveraging predictive information in supply chain system management. As such, they form a basis for further investigations towards the ultimate goal of quantifying the value of predictive information in supply chain systems.
KW - Complex logistic systems
KW - Decentralized and distributed control
KW - Distributed economic model predictive control
KW - Modeling and decision making in complex systems
KW - Multiagent systems
UR - http://www.scopus.com/inward/record.url?scp=85115722554&partnerID=8YFLogxK
U2 - 10.1016/j.ifacsc.2020.100125
DO - 10.1016/j.ifacsc.2020.100125
M3 - Article
AN - SCOPUS:85115722554
VL - 15
JO - IFAC Journal of Systems and Control
JF - IFAC Journal of Systems and Control
M1 - 100125
ER -