Flexible manufacturing systems: An overview and bibliography
Production and Inventory Management
A Microeconomic Approach to Optimal Resource Allocation in Distributed Computer Systems
IEEE Transactions on Computers
Market-based control: a paradigm for distributed resource allocation
Market-based control: a paradigm for distributed resource allocation
Cell formation in group technology: review, evaluation and directions for future research
Computers and Industrial Engineering - Cellular manufacturing systems: design, analysis and implementation
Efficient mechanisms for the supply of services in multi-agent environments
Decision Support Systems - Special issue on information and computational economics
Strategic negotiation in multiagent environments
Strategic negotiation in multiagent environments
Automatica (Journal of IFAC)
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
A distributed multi-agent production planning and scheduling framework for mobile robots
Computers and Industrial Engineering
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In this paper, the static layout of a traditional multi-machine factory producing a set of distinct goods is integrated with a set of mobile production units - robots. The robots dynamically change their work position to increment the product rate of the different typologies of products in respect to the fluctuations of the demands and production costs during a given time horizon. Assuming that the planning time horizon is subdivided into a finite number of time periods, this particularly flexible layout requires the definition and the solution of a complex scheduling problem, involving for each period of the planning time horizon, the determination of the position of the robots, i.e., the assignment to the respective tasks in order to minimize production costs given the product demand rates during the planning time horizon. We propose a decentralized multi-agent system (MAS) scheduling model with as many agents as there are the tasks in the system, plus a resource (robot) owner which assigns the robots to the tasks in each time period on the basis of the requests coming from the competing task agents. The MAS model is coupled with an iterative auction based negotiation protocol to coordinate the agents' decisions. The resource prices are updated using a strategy inspired by the subgradient technique used in the Lagrangian relaxation approach. To measure the effectiveness of the results, the same are evaluated in respect to that of the benchmark centralized model.