Abstract: The present invention provides a method for allocating appointment time windows. The steps of this method include creating a statistical estimate of a daily schedule comprising a series of estimated service orders. An actual service order is then received. This actual service order is inserted into the daily schedule by using a set of scheduling instructions for determining the least cost to employ the available service resources. At this point, this actual service order does not have a system imposed time window. The set of scheduling instructions is used to determine a time window surrounding this insertion point. If the customer accepts this time window, then the closest estimated service order is replaced by this actual service order, and the daily schedule is recomputed based upon the revised set of service orders to yield a revised daily schedule.

Abstract: The invention is directed to a method by which optimal paths are found between one or more start destinations and one or more end destinations. First destination and travel data is converted into a node and edge data format, wherein the nodes represent start points and the edges have a weight related to a travel weight. These nodes and edges are subdivided into subsets. The paths between the start nodes and each of the end nodes are determined using the node and edge representations stored in the subsets. A selected union of subsets is determined that contains the start end destinations. The optimal paths are determined by using the travel values associated with the edges connecting the nodes. The union of subsets, which may comprise less than the full amount of subsets, is loaded for the path determination. Or, when the path determination perceives that a relevant boundary has been reached in a path determination, that next subset in the union of subsets is loaded.

Abstract: The invention is directed to a method by which optimal paths are found between one or more start destinations and one or more end destinations. First destination and travel data is converted into a node and edge data format, wherein the nodes represent start points and the edges have a weight related to a travel weight. These nodes and edges are subdivided into subsets. The paths between the start nodes and each of the end nodes are determined using the node and edge representations stored in the subsets. A selected union of subsets is determined that contains the start end destinations. The optimal paths are determined by using the travel values associated with the edges connecting the nodes. The union of subsets, which may comprise less than the full amount of subsets, is loaded for the path determination. Or, when the path determination perceives that a relevant boundary has been reached in a path determination, that next subset in the union of subsets is loaded.

Abstract: The present invention provides a method for allocating appointment time windows. The steps of this method include creating a statistical estimate of a daily schedule comprising a series of estimated service orders. An actual service order is then received. This actual service order is inserted into the daily schedule by using a set of scheduling instructions for determining the least cost to employ the available service resources. At this point, this actual service order does not have a system imposed time window. The set of scheduling instructions is used to determine a time window surrounding this insertion point. If the customer accepts this time window, then the closest estimated service order is replaced by this actual service order, and the daily schedule is recomputed based upon the revised set of service orders to yield a revised daily schedule.

Abstract: The present invention provides a method for allocating appointment time windows. The steps of this method include creating a statistical estimate of a daily schedule comprising a series of estimated service orders. An actual service order is then received. This actual service order is inserted into the daily schedule by using a set of scheduling instructions for determining the least cost to employ the available service resources. At this point, this actual service order does not have a system imposed time window. The set of scheduling instructions is used to determine a time window surrounding this insertion point. If the customer accepts this time window, then the closest estimated service order is replaced by this actual service order, and the daily schedule is recomputed based upon the revised set of service orders to yield a revised daily schedule.

Abstract: The invention is directed to a method by which optimal paths are found between one or more start destinations and one or more end destinations. First destination and travel data is converted into a node and edge data format, wherein the nodes represent start points and the the edges have a weight related to a travel weight. These nodes and edges are subdivided into subsets. The paths between the start nodes and each of the end nodes are determined using the node and edge representations stored in the subsets. A selected union of subsets is determined that contains the start end end destinations. The optimal paths are determined by using the travel values associated with the edges connecting the nodes. The union of subsets, which may comprise less than the full amount of subsets, is loaded for the path determination. Or, when the path determination perceives that a relevant boundary has been reached in a path determination, that next subset in the union of subsets is loaded.

Abstract: The present invention provides a method for processing multiple work assignments to a mobile workforce having a plurality of mobile workforce members. The steps of this method include receiving a first work assignment. This work assignment is then examined to determine the type of work assignment, a service assignment or a pooled assignment. If the work assignment is a pooled work assignment, the work assignment is placed within a pooled work assignment queue. Similarly, if the work assignment is a service work assignment, that assignment is placed within a service work assignment queue. The process is iterative and additional work assignments may be received and sorted into the proper queue. A schedule may then be created for each individual work force member according to the assignments within the service queue. These scheduled are examined and periods of availability or slack times are identified. These periods of availability or slack time are then filled with work assignments from the pooled queue.

Abstract: A method for delivering an optimal routing solution that considers the complete operational cost model in the optimization process is provided. The method utilizes heterogeneous service providers to service heterogeneous service points, where the service points are geographically dispersed. The method includes providing a plurality of service point profiles that define parameters associated with a plurality of service points; providing a plurality of service provider profiles, that define parameters associated with a plurality of service providers; providing a service point visit plan for the plurality of service points; and scoring a plurality of routing solutions to service the plurality of service points, utilizing the service point profiles and the service provider profiles. In addition, scoring controls are provided for assigning a cost to, or penalty, either linearly or non-linearly, selected parameters being either met or not met within any possible routing solution.

Abstract: A scheduling engine for optimally scheduling the allocation of a set of service providers to a defined set of service points includes a service point mechanism for collecting and processing a plurality of service point data elements, and a service provider mechanism for collecting and processing a plurality of service provider data elements. A generic multi-layer scheduling mechanism generates allocation schedules for allocating the set of service providers to the set of service points irrespective of nonlinearity or dependence amongst said plurality of service point data elements and said plurality of service providers data elements. The generic multi-layer scheduling mechanism employs a heuristic algorithm for use of an objective function relating to said plurality of service point data elements and said plurality of provider data elements and said control parameters that describe what is important to the customer and generating therefrom an optimal allocation schedule.

Abstract: The invention is directed to a method by which optimal paths are found between one or more start destinations and one or more end destinations. First destination and travel data is converted into a node and edge data format, wherein the nodes represent start points and the the edges have a weight related to a travel weight. These nodes and edges are subdivided into subsets. The paths between the start nodes and each of the end nodes are determined using the node and edge representations stored in the subsets. A selected union of subsets is determined that contains the start end end destinations. The optimal paths are determined by using the travel values associated with the edges connecting the nodes. The union of subsets, which may comprise less than the full amount of subsets, is loaded for the path determination. Or, when the path determination perceives that a relevant boundary has been reached in a path determination, that next subset in the union of subsets is loaded.

Abstract: The present invention provides a method for allocating appointment time windows. The steps of this method include creating a statistical estimate of a daily schedule comprising a series of estimated service orders. An actual service order is then received. This actual service order is inserted into the daily schedule by using a set of scheduling instructions for determining the least cost to employ the available service resources. At this point, this actual service order does not have a system imposed time window. The set of scheduling instructions is used to determine a time window surrounding this insertion point. If the customer accepts this time window, then the closest estimated service order is replaced by this actual service order, and the daily schedule is recomputed based upon the revised set of service orders to yield a revised daily schedule.