We present an approach of a distributed planning method to solve dy- namically some .... thus giving it the planning and execution control for the rst action of the.
Distributed Negotiation-based Task Planning for a Flexible Manufacturing Environment Stefan Hahndel1 and Florian Fuchs1 and Paul Levi2 1
Department of computer science, Munich University of Technology, Orleansstr. 34, 81667 Munchen, Germany 2 Institute for Parallel and Distributed High-Performance Computers, University of Stuttgart, Breitwiesenstr. 20-22, 70565 Stuttgart, Germany
Abstract. In a exible manufacturing environment a group of autonomous agents such as autonomous mobile robots and intelligent manufacturing units is supposed to cooperate and avoid bad interaction. Thus they must have the capability to build a common multi-agent plan through detailed adequate agreements and take into consideration the group goals as well as those of the individual agents. We present an approach of a distributed planning method to solve dynamically some typical tasks of a production planning system at control level without using a xed central component. To archieve this, a exible manufacturing system has been modelled as a multi-agent system, while the planning method we present uses the structure of manufacturing plans to coordinate properly their activities through negotiation among the agents. The distributed planning method which was developed has been implemented and tested on a pool of workstations.
1 Introduction Usually manufacturing planning is done by a central production planning system (PPS) [15, 12, 7]. A typical production planning system in a exible manufacturing environment manages a large set of manufacturing plans in a representation which is independent of the exact structure of the manufacturing environment. These plans do not contain information about which individual machine to use, when several machines with overlapping capabilities are available. Also the operations for loading and unloading the machines and transport actions are missing in these plans. In general, engineers use so-called "net plans" for this purpose. When the manufacturing system receives an order to produce n parts of an individual product P until a given time t, the production planning system passes the manufacturing plan over to a central manufacturing control system. This system expands the plan with the manipulation and transportation actions needed and generates a schedule of these activities, based on its current knowledge of the state of the manufacturing environment. Such a system also controls the environment and gives the sub-orders to the individual agents just at the right time. Because of the high complexity of present-day manufacturing systems a central planning system is unable to react adequatly on unavoidable production failures (e.g. broken tools, machine
failures etc.). This strongly centralized and hierarchical built system represents a bottleneck, which makes it dicult to react with the desired exibility to several di ering failures at the same time under real-time conditions. Most planning subproblems, e.g. job shop scheduling, are NP complete. For this reason most of the time it is not possible to build an optimal plan under real-time conditions. Therefore, most central systems use good heuristics to build suboptimal solutions. But from the DAI's (Distributed Arti cial Intelligence) point of view that is also possible in a decentralized manner with the additional advantage of greater performance. Over the last few years the interest in decentralized control and process planning has increased in this area as well as in many other domains of computer science. Examples for the usage of DAI for manufacturing planning and control systems are the system YAMS from Parunak [9], the works of Ayel [1], the system of Raulefs [11] and the works of Dilger [2]. Another important domain that is related to our work is the domain of dynamic and distributed scheduling, e.g. [3], [10], [14]. Some of the main problems of building such systems concern the distribution of the actions to the agents, the cooperation of these agents, the planning of the synchronization of actions, the building of proper agent groups and decentralized methods for con ict resolution. These problems are mostly derived from the local point of view of the agents. We present an approach where no xed component of central decision or a xed hierarchical structure is needed. Further, planning and execution of actions is performed in a (temporal) overlapping manner. When we speak about planning in this context, we regard both (action) planning (generating local action plans) and scheduling of all actions. The distributed planning method described in this article has been implemented and tested on a pool of workstations.
2 The Model First, we describe a proper model for our production planning system. The model consists of an agent model and a model for manufacturing and action plans. Each autonomous component of the exible manufacturing environment (i.e. manufacturing cells, autonomous mobile robots) is modelled as an agent of our multi-agent system. Figure 1 shows the architecture of such an agent. The multi-agent system consists of a set of autonomous agents. Each of these agents has a set of capabilities, its own knowledge base and a combined local planning and communication module for proper coordination and negotiation with other agents. Knowledge base The knowledge base can be divided in a static and a dynamic
part. The static part contains knowledge about the capabilities of its own and the capabilities of other agents, how to perform an action, and knowledge about negotiation and local planning. The dynamic part contains di erent types of
interaction with other agents
negotiation process schedule production plans statistical data
decision component
control process
working process
communication manager interaction manager
simulated
supervision
Fig. 1. agent architecture (simpli ed) plans, that means the manufacturing plans and local action plans in which the agent is involved and a schedule of its own actions. Additionally, it contains statistical data about the reliability of the other agents and about the duration of past actions. Planning and Communication Module The local planning and communication
module has the following tasks: { Expanding manufacturing actions to a local action plan which contains all additionally needed auxiliary actions (i.e. add actions for loading/unloading or transportation tasks, depending on the actual state) { Distribution of all auxiliary actions which the agent cannot perform by itself to other agents through proper negotiation. This includes formulating requests for o ers, evaluating all o ers received and selecting the best, in case an agent requests actions from other agents. If the agent is the o ender, it tests if and when it can perform the requested action, in order to generate a proper o er. { Another task of the planning module is the local optimization of the agent's schedule. Plan Structures A manufacturing plan P is a kind of skeleton plan which con-
tains only manufacturing steps on the highest abstraction level together with the causal dependencies without the auxiliary actions needed. Formally, such a manufacturing plan can be seen as a semi-ordered set of actions (AP ;
