A system, method, and apparatus for implementing workflows across multiple differing systems and devices is provided herein. During operation, a workflow is automatically generated based upon a high probability of overloading resources with a current workflow. In particular, a workstation (or server) detects a high probability that a resource will be overloaded if a particular trigger is implemented. The workstation (or server) then determines an alternate trigger to reduce the chances that a resource will be overloaded. The alternate trigger and action can then be implemented or suggested as a newly-created workflow.

Exemplary embodiments provide a transportation staffing management system. An amount of transportations miles are forecasted for delivering inventory from a distribution enter to a store based on a sales forecast for a store. Non-driving time for drivers engaged in delivering inventory to a store is tracked based on data collected in real-time from mobile computing devices associated with delivery vehicles. An amount of time needed to deliver the inventory based on the forecasted amount of transportation miles and non-driving is calculated. An optimal transportation workload is generated for the distribution center based on the amount of time, data from a first database storing data from a central office, and data from a second database storing data from a distribution center.

An online shopping concierge system identifies a set of delivery orders and a set of delivery agents associated with a location. The system allocates the orders among the agents, each agent being allocated at least one order. The system obtains agent progress data describing travel progress of the agents to the location, and order preparation progress data describing progress of preparing the orders for delivery. The system periodically updates the allocation of the orders among the agents based on the agent progress data and the order preparation progress data. This involves re-allocating at least one order to a different delivery agent. When a first agent arrives at the location, the system assigns to the first agent the orders allocated to the first agent. The system then removes the first agent from the set of available delivery agents, and removes the assigned delivery orders from the set of delivery orders.

Technologies are described for replicating selected changes to planned working time from a first system, such as an HR system, to a second system, such as a payroll system. For example, upon receiving an indication of a change to a planned working time for one or more workers on one or more days, the first system can evaluate the change to determine whether it should be replicated to the second system. The determination can be based on whether the change is a temporary change, as well as based on information read from a data replication configuration object, a data replication period object, and/or an in-sync work schedule object. Upon determining that the change should be replicated to the second system, a data replication proxy object can be created based on the change and used to transmit data regarding the change for replication at the second system.

In some examples, a service provider may determine from a shift schedule that a first employee is scheduled to work a particular shift at a business within a threshold period of time. The service provider may determine, based on a location of a first mobile device of the first employee, that the first employee is unlikely to arrive at the business in time for the start of the particular shift. In response, the service provider may find a replacement employee to fill the shift of the first employee. For instance, the service provider may send the communication to a second mobile device associated with a second employee that is determined to be within a threshold distance of the business. Alternatively, if the service provider determines that the business is overstaffed on a particular day, the service provider may offer a selected employee an incentive to depart work early.

Systems and methods for enabling controls at a user computing device for delegating tasks include providing information to an intermediary regarding the likelihood that a user associated with the user computing device will delegate a particular task. The intermediary may then selectively activate a delegation control at the user computing device that, when activated, results in delegation of the task for completion by a task facilitation service.

Methods, apparatuses, and systems for scheduling tasks to field professionals include: storing, in a database, a plurality of records reflecting characteristics associated with completing a set of technical services, wherein information in each record is derived from historical experience of completing each of the technical services; receiving a request for a new technical service associated with a location; and assigning a field professional to perform the new service having determined from information in the database a likelihood that the field professional will complete the new technical service in a single on-site visit at the location.

Systems and methods of the present disclosure facilitate scheduling and managing a project. In some embodiments, the system includes a quoting module, a product data module, and a project planning module executing on at least one processor of a server. The product data module may be configured to store at least one product. The product stored in the project module may have a product class indicating that the product is a labor product, parts product, or agreements product. The system may be configured to copy products from the product data module to the quoting module. The system may be configured to also copy products from the product data module to the project planning module, and generate tasks for the project planning module based on the products.

Project modeling is conducted using variable defect arrival rate or variable defect rate density parameters. These defect rates may be updated on an iteration by iteration basis and may be used to provide remediation and further project modeling, remediation, and prediction.

The present disclosure relates to an apparatus and method of altruistic scheduling based on reinforcement learning. An altruistic scheduling apparatus according to an embodiment of the present disclosure includes: an external scheduling agent for determining a basic resource share for each process based on information of a resource management system; an internal scheduling agent for determining a basic resource allocation schedule for each process based on information including the basic resource share and a resource leftover based on the basic resource allocation schedule; and a leftover scheduling agent for determining a leftover resource allocation schedule based on information including the resource leftover. According to an embodiment of the present disclosure, it may be expected that reinforcement learning will not only mitigate the diminution of fairness of an altruistic scheduler but also further improve other performance indicators such as completion time and efficiency.