This paper discusses the application of the PROSA reference architecture and its ant-colony-inspired delegate MAS to the coordination of open-air engineering processes such as open-pit mining, road construction and agriculture farming. The execution of such an open-air engineering process by cooperating mobile equipment needs to be carefully planned for its cost effective and timely completion. The dynamics in the open and distributed operating environment of these processes make the planning complex. This paper presents the design of an on-line planning system. The focus of the presented approach is on improving the process visibility to enable better-informed decision making. The on-line planning system based on PROSA is model-driven.; it uses executable models to reflect the operations and to generate short-term forecasts.

Manufacturing control systems that are built along the Holonic Manufacturing Execution Systems (HMES) approach, are able to generate short-term forecasts (resource loads and order routings). However, the reliability of these forecasts has to be balanced against the reactivity of the system in coping with the changes and disturbances that happen in the manufacturing environment. These phenomena are captured in so-called system nervousness issues. This paper discusses the system nervousness issues and how to handle them.

This paper reports on a development process for determining a suitable algorithm for application within agent based shop floor control systems. In particular, the algorithm discussed here is focussed on the achievement of high and consistent levels of production throughput in a manufacturing flow shop environment where there is a significant degree of flexibility associated with route selection. It is intended that in addition to performing well under nominal operating conditions, that such algorithms support a control system that is robust in the face of typical but unexpected process disturbances, such as congestion. It is emphasised that the algorithms are primarily intended for shop floor control and execution rather than scheduling for which numerous existing algorithms already exist.

The paper discusses an approach to multiagent coordination and control inspired by the behaviors of social insect societies. It enables an agent-based control architecture where the desired control behavior emerges from the single activities of individual agents without exposing them to the complexity and dynamics of the overall system. The research presented is demonstrated in the manufacturing domain. However, the approach is applicable to other types of "ironware" systems.

In a holonic shop floor control (SFC) architecture, a reactive scheduler co-operates with an autonomous online SFC system to combine high performance with reactivity against disturbances. To take its decisions, the online SFC system uses the existing schedule as advice. This "schedule execution" (SE) problem is not trivial, because the decisions taken by the online SFC system are local decisions in a combinatorial optimisation problem. By representing the schedule as a graph, this paper presents an algorithm for SE, based on perturbation analysis. It tackles the nonlinearities in the production system by a combination of linearisation and feedback control.

The design of a knowledge-based scheduling system for a flexible assembly cell is discussed. The system integrates disruptions and can be connected to a real-time cell controller. It is composed of a static database, a dynamic database, and expert modules and is driven by an asynchronous clock. The knowledge is hierarchically organized in the distinct expert modules, each standing for a decision level. The scheduler is characterized by a scheduling-in-advance methodology. This capability is to be used on- or offline.>