The author deals with the production planning and control (PPC) problem encountered in industrial plants composed of chains of flexible manufacturing shops (FMS). Due to the variability of the FMS operation modes in processing a variable mix of parts, an efficient planning/control procedure has to be implemented to accommodate exogenous events (new orders) and to counteract the effects of endogenous events (failures). An outline is given of a hybrid control architecture, knowledge-based and analytical, able to integrate these planning/control tasks.>

The evolution of Computer Integrated Manufacturing calls for the organizatlon of complex production processes, obtained by the composition of automated job shops. The larye-scale event-driven nature of such systems requires a revision of the techniques for designing production planning and control architectures. Two manufacturing environments are here considered : batch and repetitive. In the former case, productlon batches are closely related to customer orders so that th eplanning problem consists of scheduling and sequencing the lots through the sequence of shops in order to meet due dates. In the latter case, stationary or slowly varying order quantities must be produced. Then production rates and lot sizes are the main control variables. The paper shows a unified approach to the production planning problem for batch and repetitive manufacturing. Starting from a common model and control architecture, specialized algorithms are derived for the two cases. A hierarchical and decentralized architecture is stressed.

In this paper the existing approaches to design of expert scheduling systems are briefly described. Features of flexible manufacturing environment are explored. To take advantage of the progress in AI and optimization theory two new architectures: a single-model tandem architecture (Kusiak [5]) and multimodel tandem architecture of expert scheduling systems are discussed. Based on these architectures two expert systems for scheduling flexible manufacturing systems are designed.

The problem of controlling production flows in a failure-prone Flexible Manufacturing System (FMS) is approached, when such a FMS is a component working section of a complex manufacturing process. A hierarchical control architecture is formulated, on the basis of the following concepts : a) the possibility of decentralizing the FMS Production Planning and Control (PPC) problem, by its decomposition into a number of PPC sub-problems, each one referred to a machining center included in the FMS itself ; b) the possibility of classifying possibly occurring events, according to their influence either on individual machining centers, or on the whole FMS. To overcome the intrinsic problem complexity, a new Hybrid (partly knowledge-based, partly analytical) Control design methodology is adapted.

The Manufacturing Systems cover a technical field where an intelligent combination of human and artificial capabilities can best solve major problems. Men know how to model the structure of those Systems and are able Lo formulate conveniently the problem of controlling them aiming to specified performance characteristics. But the information and data gathered can be handled and processed only by artificial means, primarily computers. The essential issue in this context consists of properly organizing the human and artificial Tasks, developing and implementing them in mutual accordance so as to make the best use of both potentials. The present paper tries to present in logical sequence the major lines and leading criteria of this approach.

Practical experience of production planning and control problems calls for a multi-decisionmaker control structure, to efficiently drive each work section of the manufacturing system. Present paper suggests to design a hierarchical control structure by integrating mathematical tools derived from Control Theory by use of relational tools derived from Artificial Intelligence. Conditions to allow an effective implementation of the Expert Control System resulting by said matching will be stated.

Abstract In many practical situations, connection structures have to be laid out in an environment with a strong inner structure. This article presents a new general mathematical formulation of cor responding design problems in which the layout possibilities are represented by a network. Because in practice such networks are often very large and sparse, there is great interest in the utilization of decomposition techniques for the optimization of connection structures. New decomposition methods for the determination of optimal paths without interference (independent decom position), of k node‐disjoint paths with minimal total costs and of optimal Steiner trees are presented. The new methods are compared with other techniques under different aspects of practical importance.