Holonic control for manufacturing systems: functional design of a manufacturing robot cell
Integrated Computer-Aided Engineering - Special issue: intelligent manufacturing systems
The AARIA agent architecture: an example of requirements-driven agent-based system design
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Holonic manufacturing scheduling: architecture, cooperation mechanism and implementation
Computers in Industry - Special issue on manufacturing systems
Distributed Manufacturing Scheduling Using Intelligent Agents
IEEE Intelligent Systems
ADACOR: a holonic architecture for agile and adaptive manufacturing control
Computers in Industry
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Experimental validation of ADACOR holonic control system
HoloMAS'05 Proceedings of the Second international conference on Holonic and Multi-Agent Systems for Manufacturing
Distributed control of production systems
Engineering Applications of Artificial Intelligence
Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach
Engineering Applications of Artificial Intelligence
Dynamic composition of holonic processes to satisfy timing constraints with minimal costs
Engineering Applications of Artificial Intelligence
Semi-heterarchical control of FMS: From theory to application
Engineering Applications of Artificial Intelligence
Handling disruptions in manufacturing systems: An immune perspective
Engineering Applications of Artificial Intelligence
Journal of Intelligent Manufacturing
A holonic approach to flexible flow shop scheduling under stochastic processing times
Computers and Operations Research
Hi-index | 0.00 |
Manufacturing scheduling is a complex combinatorial problem, particularly in distributed and dynamic environments. This paper presents a holonic approach to manufacturing scheduling, where the scheduling functions are distributed by several entities, combining their calculation power and local optimization capability. In this scheduling and control approach, the objective is to achieve fast and dynamic re-scheduling using a scheduling mechanism that evolves dynamically to combine centralized and distributed strategies, improving its responsiveness to emergence, instead of the complex and optimized scheduling algorithms found in traditional approaches.