This paper presents a formal approach to resolve an important question concerning changes in the control of computerized manufacturing systems when a human operator is involved as a task-performing agent. It requires building a model of human functional specifications used in executing tasks and integrating it into a control scheme for the model. More importantly, analysis of control complexity needs to be conducted to build an effective control mechanism. In this paper, a human material handler is considered, and an assessment of part flow complexity affected by human tasks in a highly automated manufacturing system is presented. For this purpose, a formal model of human task-performing processes is proposed in terms of a part and location(s) of a task. A classification for human material handling tasks is presented based on the proposed model. Furthermore, human errors and the impact of human errors on part flow are considered. Part flow complexity of a manufacturing system from the control perspective is then investigated in terms of the human tasks and errors. A shop floor control example where a human operator performs material handling tasks is provided to illustrate the proposed model.

To achieve an effective integration framework for manufacturing systems, a formal model of a system is highly desired. In spite of significant work on automated manufacturing systems, human operators still play a critical role in virtually every system, especially for material-handling processes. To build a model for control and analysis of a system where a human operator is integrated, a formal functional specification of a human material handler (MH) is presented in a hierarchical framework. Two types of human operational errors associated with material-handling tasks are also classified and discussed. A shop floor control example is provided to illustrate the proposed modeling framework.

The past two decades have seen significant improvements in optimization modeling and software solvers for large-scale optimization problems, especially discrete problems. We feel that a critical feature of many of these systems is being overlooked. That is, the process control engineer adjusts process parameters while only considering the local efficiency or not considering process efficiency at all. Production control engineers, while optimizing the global system performance, consider process parameters as given and fixed, i.e., unchangeable. Combining the optimization of the process parameters with a global system view can significantly improve the overall system performance. In practice, "hot jobs" are treated in this ad hoc manner, making sure that all resources are available and operate at peak efficiency (minimum production time) for these critical products. This phenomenon occurs not only in manufacturing but also in many other industries. This modeling part of the optimization problem can be even more important than "optimal versus heuristic"-based solution decisions made. In this paper, we present an aggregative high-fidelity modeling approach and illustrate the formulation of parameter variability in three different domains: manufacturing, air travel, and food processing.

Planning that incorporates critical resource and part constraints due to manufacturing processing as well as material handling tolerance and kinematic requirements has not been reported in the existing literature. By integrating these manufacturing resource and part constraints using object-oriented design, our operations planner creates immediately executable manufacturing plans for flexible manufacturing systems. This planner automatically generates plans for coordinated material processing and handling activities. The effects and preconditions of actions by resources on a single part are modeled to facilitate operations planning. The operations planner uses a problem-reducing search algorithm to automatically generate alternative sequences of operations needed to complete the finished part. These alternative sequences were converted into an AND/OR operations plan graph, and results showed that for multiple robots and multiple machines, representative of resources in the PSU Computer Integrated Manufacturing laboratory, the generated operations plans were reasonable compared to human generated instruction sets. The alternative sequences in this AND/OR graph could be selected at production time to optimally satisfy specific criteria.

Flexibility has become an important characteristic of today's manufacturing industry. Unfortunately, today's standard of using a fixed (linear) process plan is not flexible enough to adapt to different manufacturing system conditions aiming at increasing the system's efficiency. One means to overcome such a limitation is to prepare pre-planned alternatives in the process plan. If this can be easily done and implemented, the most appropriate plan can be selected in real time from all possible alternatives according to the conditions of the shop floor. The objective of the paper is to demonstrate this concept in detail and to discuss the influence of process plan alternatives in a dynamic shop floor environment at equipment level. In order to verify whether the presence of alternatives in process plans increases the efficiency of the manufacturing system, a simulation will be carried out using some example parts currently being manufactured in Penn State's Factory for Advanced Manufacturing Education (FAME).

Flexible manufacturing systems are capable of producing a broad variety of products and changing their characteristics quickly and frequently. This flexibility provides for more efficient use of resources, but makes control of these systems more difficult. Control problems previously unstudied now require practical resolution, like system deadlock. A system deadlock is a situation that arises due to resource sharing in manufacturing systems, when the flow of parts is permanently inhibited and/or operations on parts cannot be performed. This problem has been ignored by most scheduling and control studies, which usually assume infinite machine queue capacity and unlimited tooling resources. FMS's, however, have little or no queue capacity and limited tooling resources. In this paper, graph-theoretic deadlock detection and resolution procedures are presented which are suitable for real-time control of manufacturing systems. These procedures determine whether part movement in the system causes system deadlock or not. To this end, a system status graph representing part routings is virtually updated for every part movement before parts move physically to the next destination. Two types of system deadlocks, part flow deadlock and impending part flow deadlock, are detected using the updated system status graph. If a deadlock detection and recovery method is used to recover from a deadlock using a storage buffer, only part flow deadlocks need to be detected. On the other hand, if no buffer is available, both types of existing as well as impending system deadlocks need to be detected to avoid a deadlock situation.>

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.>

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.>