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Process planning and scheduling are important manufacturing planning activities which deal with resource utilisation and time span of manufacturing operations. The process plans and the schedules generated in the planning phase will be modified in the execution phase due to the unforeseen disturbances in the manufacturing systems. This paper deals with a multi-agent architecture of an integrated and dynamic system for process planning and scheduling for multiple jobs. A negotiation protocol is discussed, in this paper, to generate the process plans and the schedules of the manufacturing resources and the individual jobs, dynamically and incrementally, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks discussed in the previous paper [Tehrani et al. 2007. A search algorithm for generating alternative process plans in flexible manufacturing system. Journal of Advanced Mechanical Design, System, and Manufacturing, 1 (5), 706-716], and the suitable process plans and schedules are searched and generated to cope with both the dynamic status and the disturbances of the manufacturing systems. Coordination agents are proposed to generate a suitable assignment of the job agents to the machine tool agents at each step of the negotiation. Simulation software has been developed to carry out case studies, aimed at verifying the performance and it has been integrated with open robot interface network (ORIN) architecture for practical application of the multi-agent system in a real manufacturing system.

We propose a practical local and global path-planning algorithm for an autonomous vehicle or a car-like robot in an unknown semistructured (or unstructured) environment, where obstacles are detected online by the vehicle's sensors. The algorithm utilizes a probabilistic method based on particle filters to estimate the dynamic obstacles' locations, a support vector machine to provide the critical points and Bézier curves to smooth the generated path. The generated path safely travels through various static and moving obstacles and satisfies the vehicle's movement constraints. The algorithm is implemented and verified on simulation software. Simulation results demonstrate the effectiveness of the proposed method in complicated scenarios that posit the existence of multi moving objects.

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Future Manufacturing Systems 96 and intelligent ones, namely the agent-based and holonic manufacturing control system. In section 3, we present two case studied including real-time scheduling method for holonic manufacturing system and agent-based dynamic integrated process planning and scheduling in flexible manufacturing system. Finally, we briefly discuss about realizing the agent based manufacturing system by applying the ORiN (Open Robot Interface Network) architecture which recently has been developed for manufacturing automation. 2. Manufacturing control systems 2.1 Traditional approach to manufacturing control problem The manufacturing control is concerned with managing and controlling the physical activities in the factory aiming to execute the manufacturing plans, provided by the manufacturing planning activity, and to monitor the progress of the product as it is being processed, assembled, moved, and inspected in the factory. Algorithms at this level are used to decide what to produce, how much to produce, when production is to be finished, how and when to use the resources or make them available, when to release jobs into the factory, which jobs to release, job routing, and job/operation sequencing (Baker, 1998). Due to its complexity, especially the high number of interactions between the different components and the variety of functions executed, manufacturing control systems are traditionally implemented using centralized or hierarchical control approaches, comprising, the following main components: planning, scheduling, execution (i.e. dispatching, monitoring, diagnosis and error recovery) and machine/device control. Each one of these components operates in a specific temporal horizon, ranging from weeks at the strategic level to seconds at the shop floor. The traditional approach to manufacturing control systems based on centralized or hierarchical control structures, presents good characteristics in terms of productivity, essentially due to its intrinsic optimization capabilities. However, dynamic and adaptive response to change is, currently, the key to competitiveness, and the traditional approaches to manufacturing control typically fall into large monolithic and centralized software packages that are developed and adapted case by case, requiring a huge and expensive effort to implement, maintain or re-configure. In conclusion, they are not adequate because they do not support efficiently the current requirements imposed to manufacturing systems, namely in terms of flexibility, expansibility, agility and re-configurability. 2.2 Agent-based manufacturing control The multi-agent system paradigm derives from the distributed artificial intelligence (DAI) field, being characterized by decentralization and parallel execution of activities based on autonomous entities, called agents. The definition of agent concept is neither unique nor consensual (Russel & Norvig, 1995; Wooldridge & Jennings, 1995;Wooldridge, 2002). Despite some definitions and interpretations for agents, a suitable definition is: ...