Distributed economic model predictive control for cooperative supply chain management using customer forecast information. (March 2021)

• Record Type: Journal Article
• Authors: Köhler, Philipp N.; Müller, Matthias A.; Pannek, Jürgen; Allgöwer, Frank
• Is Part Of: IFAC journal of systems and control. Volume 15(2021)
• Publication Date: 2021-03
• Subjects: Decentralized and distributed control -- Complex logistic systems -- Modeling and decision making in complex systems -- Multiagent systems -- Distributed economic model predictive control; Automatic control -- Periodicals; Relay control systems -- Periodicals; Embedded computer systems -- Periodicals; Feedback control systems -- Periodicals; Artificial intelligence -- Periodicals; Artificial intelligence; Automatic control; Embedded computer systems; Feedback control systems; Relay control systems; Electronic journals; Periodicals; 629.89
• DOI: 10.1016/j.ifacsc.2020.100125
• Languages: English
• ISSNs: 2468-6018

Abstract

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.