Abstract In this paper, a new autonomous control strategy, which incorporates both (1) expected waiting times based on local information about buffer levels at hand and (2) the experience of predecessor parts’ decisions, i.e. a pheromone-based decision making process will be presented. This new autonomous control strategy will be applied to a general production logistic scenario with set-up times. Furthermore, its effects on the performance of the system will be analysed. 1 INTRODUCTION In order to cope with increasing dynamics and complexity, production planning and control systems have seen the introduction of autonomous control strategies. In accordance to the definition of autonomous control [1], the term can be understood as decentralised coordination of intelligent logistic objects and the routing through a logistic system by the intelligent objects themselves. For scenarios with varying processing times and without set-up times, different autonomous control strategies have been developed, i.e. the queue length estimator [2, 3], which is based on local information about buffer levels and thus expected waiting time and a pheromone-based approach [4, 5]. It was shown that these approaches can lead to shop floors, which can adapt themselves to changing work loads and unexpected disturbances [2, 3, 5]. In the following, the focus is on autonomous control of a shop floor scenario with set-up times. In a pheromone-based concept, set-up times are somewhat hard to handle because predecessors’ decisions have influence on successors, which is ordinary not communicated by the pheromone. Hence, a correction term is introduced. With this correction term it is possible to implement a new autonomous control strategy, which includes a weighted combination of the queue length estimator and the pheromone strategy. This new autonomous control Published in: Proc. of 40th CIRP International Seminar on Manufacturing Systems.Liverpool, United Kingdom, 2007, CD-ROM.
[1]
B. Scholz-Reiter,et al.
The Influence of Production Networks ’ Complexity on the Performance of Autonomous Control Methods
,
2006
.
[2]
Marco Dorigo,et al.
Swarm intelligence: from natural to artificial systems
,
1999
.
[3]
Bernd Scholz-Reiter,et al.
Modelling Dynamics of Autonomous Logistic Processes: Discrete-event versus Continuous Approaches
,
2005
.
[4]
Hendrik Van Brussel,et al.
Pheromone based emergent shop floor control system for flexible flow shops
,
1999,
Artif. Intell. Eng..
[5]
K. Windt,et al.
Understanding Autonomous Cooperation & Control in Logistics: The Impact on Management, Information, Communication and Material Flow
,
2007
.
[6]
Bernd Scholz-Reiter,et al.
Inventory Control in Shop Floors , Production Networks and Supply Chains Using System Dynamics
,
2007
.
[7]
Bernd Scholz-Reiter,et al.
Modelling and Analysis of Autonomous Shop Floor Control
,
2005
.
[8]
Thomas Jagalski,et al.
Autonomous control of production networks using a pheromone approach
,
2006
.