Two basic measures, model complexity and model construction efficiency, are usually used to evaluate the implementability of a methodology for modeling the control of a discrete event manufacturing system (DEMS) on the shop floor. Many well-recognized methods are used to represent and analyze the dynamics of DEMs, but not many relevant applications have been found in developing control software for the shop floor due to their shortcomings in satisfying these two measures. The paper explores a methodology for modeling the control of a DEMS, which leads to ease of control software development, rather than a new representational/analytical tool, by significantly reducing the model complexity (in terms of the number of required control states) and improving the model construction efficiency. First, an extended finite machine, called a deterministic finite capacity machine (DFCM) with parallel computing capability is developed. Based on DFCMs, the complexity growth function of a DEMS control model is linear in the number of synthesized control components. Then, an automaton structure of a DFCM control model, called structured adaptive supervisory control (SASC), is developed. By referring to supervisory control theory, an SASC model is created with three function layers: acceptance, adaptive supervision, and execution. The well-defined structure ensures that the control model can be constructed systematically.

A two pass genetic algorithm is used to solve the job shop scheduling problem with alternate routing. The first pass picks the alternatives using a genetic algorithm, the second pass provides the order and start time of jobs on the selected alternatives by solving a nonlinear program. A nonlinear constraint reduces the dimensional complexity of the best known formulation for a job shop problem, and is used in the second pass of the algorithm. Preliminary results of this algorithm are encouraging and the algorithm has been able to solve small test problems to optimality.

Scheduling automated, unmanned manufacturing systems is significantly different from traditional job shops where human presence is implicitly assumed. Deadlocking has been identified as one of the critical problem in the control of automated manufacturing systems. Past approaches have been myopic in the sense that deadlock avoidance and recovery have been treated as run time problems of the system. In this paper, formulations for deadlock-free schedules are proposed and the importance of considering both material handling and buffer space availability while scheduling such an automated workstation are discussed. Extensions to the basic model and solution methods are also presented.

Most control implementations of flexible manufacturing cells have been developed specific to a particular facility, and no generic format or tools exist for the systematic planning and creation of control. This paper presents the first phase of research in automatic generation of control software. It focuses on the development of theoretical foundations and generic issues necessary to understand and implement control. Specific formal models are developed for the physical activities, system actions, and individual machines comprising the manufacturing cell. In a subsequent paper, the formal models presented here are used to provide the basis for creating context free control grammars which are used to automatically generate software for controlling flexible manufacturing cells.>

Scheduling automated, unmanned manufacturing systems is significantly different from traditional job shops where human presence is implicitly assumed. Deadlocking has been identified as one of the critical problem in the control of automated manufacturing systems. In this paper, formulations for deadlock-free schedules are proposed and the importance of considering both material handling and buffer space availability while scheduling such an automated workstation are discussed.>

Generally an error recovery module performs three activities: error detection, identification, and diagnosis. This paper presents an approach for dealing with several critical issues that arise in performing these three activities: error classification, error knowledge representation, and generation of recovery procedures. Execution errors occurring in a manufacturing system are emphasized in this paper. A formal model called MPSG (Message-based Part State Graph), which represents the execution activities of the shop floor controller, is applied to develop this approach. The objective is to simplify, and generalize the error types and the parameters required by the error knowledge representation. The proposed method shows that this simplification and generalization provides a more convenient interaction with the subsequent diagnostic activity of generating recovery procedures. Moreover, it contributes to the integration of the error recovery module during the process of controller construction.>

One specific problem of control, namely, system deadlock that can arise in an unmanned flexible manufacturing system (FMS) is addressed, and some related work in the deadlocking of computer systems is described. A formal model for manufacturing system deadlock detection is presented. Necessary and sufficient conditions for manufacturing system deadlock based on actual manufacturing-system characteristics are defined along with a set of bounds for searching for deadlocks. An implementation approach is also presented.>