This paper proposes a negotiation methodology based on multi-agent system for heterarchical and complex manufacturing control systems. This approach has been selected to implement new paradigms based on “co-opetition=co-operation+competition” in order to improve the “production on demand” and reaction capabilities of distributed production systems related to the net-economy. Agents may represent products and resources of the system. The local scheduling and control functions in dynamic environments is addressed by a new negotiation protocol between agents based on the “request session” principle for cooperation and on the game theory approach for competition.
The application of multi-agent systems based on the concept of distributed artificial intelligence is considered as being one of the most promising control architectures for next-generation of complex production systems, specifically in a dynamic environment (failed resources, disturbances, etc.). In particular, very attractive solutions and efficient issues are expected in the domain of local planning, and execution control, to improve the conventional supply chain management; here, the usual production system management consists, in a set of separate and heterogeneous application software packages, such as Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Supervisory Control And Data Acquisition (SCADA), etc. These tools are not able to cover satisfactorily the constraints required by the new challenges of the economy such as networked enterprises, production on demand or mass customization, with the high reactivity of the E-business. With regard to this situation, new paradigms have to be introduced (e.g. auctions, dynamic products and processes reconfiguration) which can be implemented only through an adapted and consistent technology. In this paper we will indifferently address the term of manufacturing system (MS) or production system (PS), which is an extension of the MS concept. In multi-agent manufacturing systems, agents may represent single resources (a work cell, a machine, a tool), workers involved in a process, etc. as well as products, customers or providers. Altogether, they perform individual tasks, in interaction between them, in order to fulfil production functions (such as procurement, local planning, task assignment, scheduling execution control, or even distribution). Architectures commonly studied in a multi-agent approach mainly comprise hierarchical and heterarchical structures. In a hierarchical architecture, there are multiple levels of master/slave agent type relationships. Here, in a heterarchical architecture, agents communicate on a “peer-to-peer” mode, without any predefined master/slave relationship. This core notion of the heterarchical control architecture is the follow-on of full local autonomy and co-opetition approaches “co-opetition=co-operation+competition” (Brandenburger and Nalebuff, 1996) used to perform global decision-making. Only heterarchical control architecture will be considered here.
This paper is organized as follows: in Section 2, we are reviewing some algorithms involved in distributed manufacturing control system; they are a theoretical basis to design and develop our monitoring and control system. In Section 3, an example of the architecture intended to be used is presented; it is in connection with the so-called PABADIS European IST project (IST-#60016: “Plant Automation BAsed on DIstributed Systems”). Section 4 will be devoted to the description system modeling approaches and associated mechanisms. Section 5 details negotiation protocols between agents, which are proposed to solve the problem of local scheduling and resource allocation. In Section 6, implementation and some experimental results are discussed. Section 7 is a summary of the paper. Finally a glossary of the abbreviations used in the paper is proposed.
The management and control system designed and described in this paper is based on multi-agents concepts for the local scheduling and monitoring of complex production systems. Our approach uses the single step production reservation method with the request session approach; it is associated with protocols for negotiation between agents.
In our work, we have compared and evaluated different types of negotiation protocols for dynamic resource allocation. The “co-opetition” protocol was considered; compared to different strategies, it achieved the best results in terms of tardiness and waiting time, in a simulation of situations with highly turbulent demands. It conducted to the emergence of stable and adaptable reactive solutions, in an environment with dynamic demand and decentralized decision. Such an approach is very promising; it enables the implementation of new and innovative paradigms to improve conventional manufacturing technologies.
In the future, we intend to use some other criteria such as cost, priority, etc. This will enable a smarter PA decision-making in the “request session” and to develop the “neighborhood” notion for the competition between agents in the session. The “co-opetition” model used in this work can be extended to other field of applications such as air traffic management and E-business as well.
