Knowledge based improvement:simulation and artificial intelligence for identifying and improving human decision-making in an operations systems

The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant

Behavior Monitoring in Self-healing Service-oriented Systems

Web services and service-oriented architecture (SOA) have become the de facto standard for designing distributed and loosely coupled applications. Many service-based applications demand for a mix of interactions between humans and Software-Based Services (SBS). An example is a process model comprising SBS and services provided by human actors. Such applications are difficult to manage due to changing interaction patterns, behavior, and faults resulting from varying conditions in the environment. To address these complexities, we introduce a self-healing approach enabling recovery mechanisms to avoid degraded or stalled systems. The presented work extends the notion of self-healing by consideringamixtureof human and service interactions observing their behavior patterns. We present the design and architecture of the VieCure framework supporting fundamental principles for autonomic self-healing strategies. We validate our self-healing approach through simulations