Abstract: A system and method for assigning and scheduling resource requests to resource providers use a modified "best-first" search technique that combines optimization, artificial intelligence, and constraint-processing to arrive at near-optimal assignment and scheduling solutions. In response to changes in a dynamic resource environment, potential changes to an existing assignment set are evaluated in a search for a better solution. New calls are assigned and scheduled as they are received, and the assignment set is readjusted as the field service environment changes, resulting in global optimization. Each search operation is in response to either an incremental change to the assignment set such as adding a new resource request, removing a pending resource request, reassigning a pending resource request, or to a request for further evaluation. Thus, the search technique assumes that the existing assignment set is already optimized, and limits the task only to evaluating the effects of the incremental change.

Abstract: A system that integrates active and simulated decisionmaking processes generates decisions in response to events representing changes in a domain model, and updates the domain model according to the decisions. The system includes a real-time mode for generating recommendations in response to real-time events, and a simulation mode for generating recommendations in response to simulated events. The simulation mode is capable of running on either randomly generated domain events or real-time domain events captured during the real-time mode. In addition, the simulation mode does not require development of a separate domain model for simulation. Rather, the simulation mode may use the contents of a domain model established during the real-time mode. Integration of an active decisionmaking tool with a simulation tool thereby eliminates the cost of constructing a separate simulation model, and avoids invalidation of the contents of the simulation model over time.

Abstract: A system and method for assigning and scheduling resource requests to resource providers use a modified "best-first" search technique that combines optimization, artificial intelligence, and constraint-processing to arrive at near-optimal assignment and scheduling solutions. In response to changes in a dynamic resource environment, potential changes to an existing assignment set are evaluated in a search for a better solution. New calls are assigned and scheduled as they are received, and the assignment set is readjusted as the field service environment changes, resulting in global optimization. Each search operation is in response to either an incremental change to the assignment set such as adding a new resource request, removing a pending resource request, reassigning a pending resource request, or to a request for further evaluation. Thus, the search technique assumes that the existing assignment set is already optimized, and limits the task only to evaluating the effects of the incremental change.

Abstract: A system and method for scheduling resource requests for a resource provider generate a first schedule, based on expected durations of each resource request, and a second schedule, based on longer, pessimistic durations of each resource request. A user interface simultaneously displays the first and second schedules to a system user. The first schedule provides the system user with a guide to good overall management of the resource performance. The second schedule provides the system user with a guide for making time commitments to customers with a greater degree of confidence. The system and method employ a variety of techniques including statistic probability calculations to determine expected and pessimistic durations for each resource request, and incorporate features for updating the first and second schedules in response to dynamic changes in the resource environment.

Abstract: A resource assignment system provides mixed-initiative updates of a user interface display in response to asynchronous events independently triggering updates. The system features an update module capable of selecting one of a plurality of diverse update modes based on system user input and additional criteria relating to each update. The update modes enable the system user to develop an update behavior appropriate for her individual circumstances. Selection of an update behavior can prevent disruption of the system user's work, producing an appearance of interface stability without sacrificing near real-time response to asynchronous events.

Abstract: A system and method for assigning and scheduling resource requests to resource providers use a modified "best-first" search technique that combines optimization, artificial intelligence, and constraint-processing to arrive at near-optimal assignment and scheduling solutions. In response to changes in a dynamic resource environment, potential changes to an existing assignment set are evaluated in a search for a better solution. New calls are assigned and scheduled as they are received, and the assignment set is readjusted as the field service environment changes, resulting in global optimization. Each search operation is in response to either an incremental change to the assignment set such as adding a new resource request, removing a pending resource request, reassigning a pending resource request, or to a request for further evaluation. Thus, the search technique assumes that the existing assignment set is already optimized, and limits the task only to evaluating the effects of the incremental change.