Flexible manufacturing systems: An overview and bibliography
Production and Inventory Management
The auction algorithm for the transportation problem
Annals of Operations Research
Linear network optimization: algorithms and codes
Linear network optimization: algorithms and codes
Cell formation in group technology: review, evaluation and directions for future research
Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms
Journal of Intelligent and Robotic Systems
An agent-based service-oriented integration architecture for collaborative intelligent manufacturing
Robotics and Computer-Integrated Manufacturing
Automatica (Journal of IFAC)
A Decentralized Scheduling Policy for a Dynamically Reconfigurable Production System
HoloMAS '09 Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
Human-robot collaboration in cellular manufacturing: design and development
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A distributed algorithm for the multi-robot task allocation problem
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Parallel algorithms for the assignment and minimum-cost flow problems
Operations Research Letters
| Hi-index | 0.00 |
Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.