The integration of condition monitoring with queueing systems to support decision making is not well explored. This paper addresses the impact of condition monitoring of the server on the system-level performance experienced by entities in a queueing system. The system consists of a queue with a single-server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady-state analysis to obtain performance measures (average queue length, average degradation, and so on). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality, and holding. The queue performance measures derived from steady-state analysis are benchmarked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Results indicate that the total cost function is convex and, thus, subject to an optimal repair policy. The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early stage policy repair decisions.

OBJECTIVE: The AMPATH program is a leading initiative in rural Kenya providing healthcare services to combat HIV. Malnutrition and food insecurity are common among AMPATH patients and the Nutritional Information System (NIS) was designed, with cross-functional collaboration between engineering and medical communities, as a comprehensive electronic system to record and assist in effective food distribution in a region with poor infrastructure. DESIGN: The NIS was designed modularly to support the urgent need of a system for the growing food distribution program. The system manages the ordering, storage, packing, shipping, and distribution of fresh produce from AMPATH farms and dry food supplements from the World Food Programme (WFP) and U.S. Agency for International Development (USAID) based on nutritionists' prescriptions for food supplements. Additionally, the system also records details of food distributed to support future studies. MEASUREMENTS: Patients fed weekly, patient visits per month. RESULTS: With inception of the NIS, the AMPATH food distribution program was able to support 30,000 persons fed weekly, up from 2,000 persons. Patient visits per month also saw a marked increase. CONCLUSION: The NIS' modular design and frequent, effective interactions between developers and users has positively affected the design, implementation, support, and modifications of the NIS. It demonstrates the success of collaboration between engineering and medical communities, and more importantly the feasibility for technology readily available in a modern country to contribute to healthcare delivery in developing countries like Kenya and other parts of sub-Saharan Africa.

Manufacturing systems are often subject to resource failure. In past work, we developed ldquorobustrdquo supervisory controllers for the single-unit resource allocation model. These guarantee that if any subset of resources fail, parts in the system requiring failed resources do not block the production of parts not requiring failed resources. To establish supervisor correctness, we assumed that each part type required at most one unreliable resource in its route. We now relax this assumption using a central buffer and present supervisors that guarantee robust operation without assumptions on route structure.

The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components. Initial predicted failure times are estimated through trained neural networks using real-time sensory signals. These estimates are used to derive a prior failure time distribution for the component that is being monitored. Subsequent failure time estimates are then utilized to update the prior distributions using a Bayesian approach. The proposed methodology is tested using real world vibration-based degradation signals from rolling contact thrust bearings. The proposed methodology performed favorably when compared to other reliability-based and statistical-based benchmarks.

Deadlock-free resource allocation has been an active area of research in flexible manufacturing. Most researchers have assumed that allocated resources do not fail, and thus, little research has addressed the discrete-event supervision of manufacturing systems that are subject to resource failure. In our previous work, we developed supervisory controllers to ensure robust deadlock-free operation for systems with unreliable resources. These controllers guarantee that parts requiring failed resources do not block the production of parts that are not requiring failed resources. This previous work assumes that parts requiring failed resources can be advanced into failure-dependent (FD) buffer space (buffer space exclusively dedicated to parts requiring unreliable resources). Supervisors admit only states for which a sequence of such part advancements is feasible. The research presented in this paper relaxes this assumption because, in some systems, providing FD buffer space might be too expensive or it might be desirable to load the system more heavily with FD parts. In this paper, we concentrate on distributing parts requiring failed resources throughout the buffer space of shared resources so that these distributed parts do not block the production of part types that are not requiring failed resources. The approach presented here requires no state enumeration and is polynomial in stable measures of system size. We also present results from simulation experiments that compare system performance under these new policies with system performance under our previously published supervisors. These results show that our new policies allow better performance if the required part mixes favor FD part types. The systems of interest are single-unit resource allocation systems.

This paper develops an integrated neural-network-based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics

Supervisory control for deadlock-free resource allocation has been an active area of manufacturing systems research. To date, most work assumes that allocated resources do not fail. Little research has addressed allocating resources that may fail. In our previous work, we assumed a single unreliable resource and developed supervisory controllers to ensure robust deadlock-free operation in the event of resource failure. In this paper, we assume that several unreliable resources may fail simultaneously. In this case, a controller must guarantee that a set of resource failures does not propagate through blocking to stall other portions of the system. That is, the controller must ensure that every part type not requiring any of the failed resources should continue to produce smoothly without disruption. To do this, the controller must constrain the system to states that serve as feasible initial states for: 1) a reduced system when resource failures occur and 2) an upgraded system when failed resources are repaired. We develop the properties that such a controller must possess and then develop supervisory controllers that satisfy these properties. Note to Practitioners-For the past decade or so, researchers have begun to actively address the issue of ensuring smooth and continuous operation for flexibly automated manufacturing systems. This research effort has been motivated by the many failed attempts to implement flexible automation throughout the 1980s. During this time, much has been learned about modeling the control functions of a flexible, automated system. In fact, ladder logic control code can now be generated automatically from mathematical models, such as Petri nets, which compactly capture the required operating system logic. Because the code is based on a formal model with well-established properties, it is guaranteed to ensure proper operation without significant startup troubleshooting. One area that has not been investigated is controlling these systems when machines or tools "fail". The question is not how to fix what has failed, but rather how to control the system so that if something does fail, the system can continue producing items that do not require the failed elements. This is essential work since automated manufacturing systems consist of thousands of components, any of which are subject to failure. If failures in the system are not handled gracefully, it becomes difficult to keep the automated system running, in which case, system production does not meet expectations. In our previous work, we investigated ensuring smooth operation for systems with a single unreliable resource. We developed supervisory controllers to guarantee this requirement for these systems. In this paper, we extend the previous results to a more general class of systems where there are multiple unreliable resources. We establish a set of desired properties that the supervisory controller must possess in order to guarantee robust operation for these systems, and then develop a number of controllers that satisfy these properties.

Manufacturing practitioners and researchers recognize the need to develop effective supervisory controllers for automated manufacturing systems. One active area of research has been developing deadlock avoidance methods for these systems. Almost all work to date has assumed that allocated resources do not fail. In this paper, we consider deadlock and blocking problems in systems with unreliable resources. Our objective is to develop supervisory control policies that allocate resources so the failure of any given resource does not propagate through blocking to effectively stall other portions of the system. To accomplish this, the supervisory controller must enforce liveness for the original system while avoiding states that do not serve as feasible initial states for the reduced system when an unreliable resource fails. The policy must then enforce liveness for the reduced system while avoiding states that do not serve as feasible initial states for the original system so transition to normal operation is smooth when the failed resource is restored.

Manufacturing practitioners and researchers have recognized the need to develop effective supervisory controllers for automated manufacturing systems. One active area of research has been developing deadlock avoidance methods for these systems. Almost all work to date has assumed that allocated resources do not fail. In this paper, we consider deadlock and blocking problems in systems with unreliable resources. Our objective is to develop supervisory control policies that allocate resources so that the failure of any given resource does not propagate through blocking to effectively stall other portions of the system. In other words, when a resource fails, we want the system to automatically continue producing all part types that do not require the failed resource. To accomplish this, the supervisory controller must ensure safety for the original system while avoiding states that do not serve as feasible initial states for the reduced system when an unreliable resource fails. The policy must then ensure safety for the reduced system while avoiding states that do not serve as feasible initial states for the original system so that transition to normal operation is smooth when the failed resource is restored. This paper illustrates this class of problems through several examples, identifies properties that controllers must satisfy to deal effectively with these problems, and develops two polynomial control policies that satisfy these properties.

Automated tool sharing systems provide a technological response to the high cost of tools in flexible manufacturing systems. These systems allow different machines to use the same tools by automatically transferring them from machine to machine as tooling needs evolve. With these systems, tool allocation is a real-time issue that requires sophisticated control techniques to make the right allocation decisions. An essential property that tool sharing policies must guarantee is deadlock-free operation. Although manufacturing researchers have investigated the performance aspects of tool sharing through simulation, no work has yet addressed deadlock handling strategies for these real-time systems. In this paper, we characterize the structural and computational properties of the tooling deadlock problem. We develop polynomial algorithms that detect and avoid deadlock, and we investigate the safety implications of special structures appearing in tool sequences.

Most deadlock avoidance work in manufacturing deals with systems where each part type requires a fixed sequence of operations. In contrast, the paper deals with those systems that support sequencing flexibility, that is, the operations required by the part are partially ordered. In this setting, the order in which operations are performed is not predetermined but becomes a real-time decision. Specifically, the paper presents a detailed resource allocation model and proves the NP-completeness of optimal deadlock avoidance for a highly flexible subclass of these systems. It also identifies several cases and conditions for which optimal deadlock avoidance is of polynomial complexity.

Deadlock avoidance policies guarantee deadlock-free resource allocation to concurrent competing processes in flexible manufacturing systems. Most deadlock avoidance work in manufacturing deals with systems where each part requires a fired sequence of operations. In contrast, this paper deals with those systems that support sequencing flexibility, that is, the operations required by the part are partially ordered. In this setting, the order in which operations are performed is not predetermined, but becomes a real-time decision. Specifically, the paper characterizes the deadlock detection and avoidance problems for these systems, presents a sub-optimal deadlock avoidance approach, and addresses two relevant special structures on the partial orders for which optimal deadlock avoidance is of polynomial complexity.

The objective of this work is to characterize the deadlock avoidance problem for systems with flexible routing capabilities. Specifically, the paper addresses deadlock avoidance for single capacity systems (each machine has a single unit of buffer capacity), and mixed capacity systems (some machines have multiple units of buffer capacity). For each of these, we characterize deadlock and prove the correctness of several methods of suboptimal deadlock avoidance. We also address two interesting special cases. The first assumes that every stage of every part type can be performed on at least one multiple capacity machine, whereas the second provides a finite central buffer that can be revisited after every processing stage. For the first case, we present two suboptimal deadlock avoidance approaches, while for the second case, we show optimal deadlock avoidance to be computationally tractable.

Deadlock avoidance policies guarantee deadlock free buffer space allocation to concurrent competing parts in flexible manufacturing systems. Typically, these policies require enumeration of routes that parts follow. In systems with flexible routing, where parts can choose from a set of machines at each processing step, route enumeration can be of exponential complexity. Setup and execution of deadlock avoidance constraints are rendered computationally intractable. The objective of this work is to develop a control model that allows for several processing alternatives at each step while maintaining polynomial bounds on deadlock-free operation.

Deadlock avoidance is essential in the operation of flexible manufacturing systems. Many of these systems have redundant processing capabilities that support flexible part routing. This work characterizes deadlock avoidance for systems with flexible routing capabilities. Specifically, we address deadlock avoidance in systems where every stage of every part type possesses some processing alternatives, and one or more machines are equipped with multiple units of buffer capacity. Several necessary conditions for deadlock and deadlock avoidance are identified.

Configuration flexibility and deadlock-free operation are two essential properties of control systems for highly automated flexible manufacturing systems. Configuration flexibility, the ability to quickly modify manufacturing system components and their logical relationships, requires automatic generation of control executables from high level system specifications. These control executables must guarantee deadlock-free operation. The resource order policy is a configurable controller that provides the deadlock-free guarantee for buffer space allocation. It uses a total ordering of system machines and routing information to generate a set of configuration specific linear constraints. These constraints encode the system state along with a buffer capacity function and define a deadlock-free region of operation. Constraint generation and execution are of polynomial complexity.

There have been many R&D efforts in recent years to develop computer environments that directly enhance group problem-solving productivity. An example of such efforts is the computer-based Virtual Teaming Technology (VTT) currently being developed at the Knowledge-based Engineering Systems Research Laboratory (KESRL). The core software environment for KESRL's VTT is called SWIFT (System Workbench for Integrating and Facilitating Teams) and is based on the knowledge processing technology (KPT) previously developed at KESRL to support various engineering design tasks. SWIFT is a flexible environment which enables the rapid development of collaborative applications, linking workers in distributed environments, of many domains. This paper presents the overall system architecture of SWIFT, and introduces the current and future R&D efforts at KESRL in realizing SWIFT for significant industrial applications.