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

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

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

Linking the Witness Simulation Software to an Expert System to Represent a Decision–Making Process

Expert systems, and artificial intelligence more generally, can provide a useful means for representing decision-making processes. By linking expert systems software to simulation software an effective means of including these decision-making processes in a simulation model can be achieved. This paper demonstrates how a commercial-off-the-shelf simulation package (Witness) can be linked to an expert systems package (XpertRule) through a Visual Basic interface. The methodology adopted could be used for models, and possibly software, other than those presented here

Increasing Accuracy of Simulation Modeling via a Dynamic Modeling Approach

Simulating processes is a valuable tool which provides in-depth knowledge about overall performance of a system and caters valuable insight on improving processes. Current simulation models are developed and run based on the existing business and operations conditions at the time during which the simulation model is developed. Therefore a simulation run over one year will be based on operational and business conditions defined at the beginning of the run. The results of the simulation therefore are unrealistic, as the actual process will be going through dynamic changes during that given year. In essence the simulation model does not have the intelligence to modify itself based on the events occurring within the model. The paper presents a dynamic simulation modeling methodology which will reduce the variation between the simulation model results and actual system performance. The methodology will be based on developing a list of critical events in the simulation model that requires a decision. An expert system is created that allows a decision to be made for the critical event and then changes the simulation parameters. A dynamic simulation model is presented that updates itself based on the dynamics of the actual system to reflect correctly the impact of organization restructuring to overall organizational performance

Motivation-based direction of planning attention in agents with goal autonomy

The action of an agent with goal autonomy will be driven by goals generated with reference to its own beliefs and desires. This ability is essential for agents that are required to act in their own interests in a domain that is not entirely predictable. At any time, the situation may warrant the generation of new goals. However, it is not always the case that changes in the domain that lead to the generation of a goal are detected immediately before the goal should be pursued. Action may not be appropriate for some time. Furthermore, an agent may be influenced by goals that tend to recur periodically, or at particular times of the day or week for example. Such goals serve to motivate an agent towards interacting with other agents or processes with certain types of predictable behaviour patterns. This thesis provides a model of a goal autonomous agent that may generate goals in response to unexpected changes in its domain or cyclically through automatic processes. An important effect of goal autonomy is that the agent exhibiting this capability will have a varying, potentially unlimited, but certainly unpredictable number of goals. Goals that hold planning attention consume resources, and real agents are resource bounded. Hence, there is a limit to the number of goals that can hold planning attention before bookkeeping and search operations become the primary mode of activity; i.e. before cognitive overload. In this thesis, an heuristic mechanism is proposed for the directing and limiting of planning attention in agents with goal autonomy. These "alarm processing" mechanisms serve to focus the attention of an agent on a limited number of the most salient goals, and thereby avoid unnecessary reasoning and prevent cognitive overload. In this way, a resource-bounded agent can employ modern planning and reasoning methods effectively

Analysing supply chain operation dynamics through logic-based modelling and simulation

Supply Chain Management (SCM) is becoming increasingly important in the modern business world. In order to effectively manage and integrate a supply chain (SC), a deep understanding of overall SC operation dynamics is needed. This involves understanding how the decisions, actions and interactions between SC members affect each other, and how these relate to SC performance and SC disruptions. Achieving such an understanding is not an easy task, given the complex and dynamic nature of supply chains. Existing simulation approaches do not provide an explanation of simulation results, while related work on SC disruption analysis studies SC disruptions separately from SC operation and performance. This thesis presents a logic-based approach for modelling, simulating and explaining SC operation that fills these gaps. SC members are modelled as logicbased intelligent agents consisting of a reasoning layer, represented through business rules, a process layer, represented through business processes and a communication layer, represented through communicative actions. The SC operation model is declaratively formalised, and a rule-based specification is provided for the execution semantics of the formal model, thus driving the simulation of SC operation. The choice of a logic-based approach enables the automated generation of explanations about simulated behaviours. SC disruptions are included in the SC operation model, and a causal model is defined, capturing relationships between different types of SC disruptions and low SC performance. This way, explanations can be generated on causal relationships between occurred SC disruptions and low SC performance. This approach was analytically and empirically evaluated with the participation of SCM and business experts. The results indicate the following: Firstly, the approach is useful, as it allows for higher efficiency, correctness and certainty about explanations of SC operation compared to the case of no automated explanation support. Secondly, it improves the understanding of the domain for non-SCM experts with respect to their correctness and efficiency; the correctness improvement is significantly higher compared to the case of no prior explanation system use, without loss of efficiency. Thirdly, the logic-based approach allows for maintainability and reusability with respect to the specification of SC operation input models, the developed simulation system and the developed explanation system