Abstract: A method for improving a machine operation are provided. The method includes receiving a plurality of domain specific heuristics and a set of states and a set of actions, where an immediate cost and/or reward is associated with a pair of state and action. The method also includes generating at least one of: a graph of state transitions for the actions, and a transition probability matrix. The method also includes executing a Markov Decision Process (MDP) model for solving an MDP problem, and outputting an MDP optimal policy of an optimal mapping of a given state to an action. The method also includes selecting one of the plurality of domain specific heuristics and heuristic input parameters thereof. The method also includes controlling the machine for solving a predefined optimization problem in a plurality of execution iterations.

Abstract: A method comprising obtaining a scheduling problem comprising: a set of agents and a set of tasks to be performed by the set of agents, wherein solving the scheduling problem using an automated solver is not feasible using available predetermined resources. The method comprises automatically generating a plurality of alternative scheduling problems, wherein a solution to each such alternative scheduling problem defines a solution to the scheduling problem and determining a solution to the scheduling problem by applying the automated solver to solve, while using the available predetermined resources, an alternative problem of the plurality of alternative scheduling problems to determine a solution to the alternative problem and by mapping the solution to the alternative problem to the scheduling problem, whereby determining the solution.

Abstract: A computer program product comprising a non-transitory computer readable storage medium retaining program instructions configured to cause a processor to perform actions, which program instructions implement: a framework for creating a model of an NP-hard problem, the model comprising at least one entity selected from the group comprising: an objective, a variable, an equation and a constraint, wherein the framework provides methods for automatically transforming the model, comprising: one or more methods for manipulating or changing a status of the entity of the model, the methods comprising a method for imposing or ignoring the constraint; and one or more methods related to operations to be applied to the entity of the model.

Abstract: A computer-implemented method, computerized apparatus and computer program product for selecting time windows to vehicle routing problems. A set of criteria for estimating desirability of scheduling an appointment to a time interval, and a set of time intervals at which appointments can be scheduled are obtained. A new appointment for scheduling to a time interval is received. For each time interval of the set, a balanced score according to the set of criteria is calculated. A time interval for scheduling the new appointment is selected based on the balanced score.

Abstract: A method, apparatus and product for real-time update of a Mobile Workforce Scheduling Problem (MWSP), which comprises: agents and tasks to be performed by the agents, wherein a schedule which solves the mobile workforce scheduling problem exists and is being implemented by the agents. The method comprising: monitoring real time information update events provided to a Business Rule Management System (BRMS), activating, by the BRMS, business rules for schedule change detection; in response to a determination that re-planning is desired, automatically determining, using business rules, a portion of the MWSP to be re-planned; providing the portion of the MWSP to a MWSP solver; receiving from the MWSP solver, a new schedule for the portion of the MWSP; and updating the schedule based on the new schedule, whereby a first portion of the schedule is updated and a second portion of the schedule remains unchanged.

Abstract: A computer-implemented method, computerized apparatus and computer program product for decomposing multisite heterogeneous workforce scheduling problems. An instance of a multisite heterogeneous workforce scheduling problem comprising a set of work items and a set of technicians is obtained. A measure of likelihood that a pair of work items belong to the same sub-problem in a decomposition of the problem instance into a plurality of sub-problems, such that a union of solutions to the plurality of sub-problems is a solution to the problem, is calculated. The measure calculation comprises calculating one or more components indicating a relation between the pair of work items and technicians potentially scheduled to execute either of them. A solution to the problem is generated by solving the plurality of sub-problems in the decomposition obtained based on a partitioning of the set of work items induced by the measure and aggregating solutions to the plurality of sub-problems.

Abstract: A computer-implemented method, apparatus and computer program product, the method performed by a processor operatively connected to a memory, the method comprising: obtaining a schedule for handling service requests, the schedule planned offline in accordance with expected service requests for a service, the schedule comprising a number of service trips per time slot and per geographic location; receiving a service request, comprising a location in which the service is to be provided; automatically suggesting, during handling the service request, a time slot for providing the service from time slots available in the schedule for providing the service in the location; and upon acceptance of the time slot for providing the service, updating the schedule with the service being provided at the time slot.

Abstract: A computer program product comprising a non-transitory computer readable storage medium retaining program instructions configured to cause a processor to perform actions, which program instructions implement: a framework for creating a model of an NP-hard problem, the model comprising at least one entity selected from the group comprising: an objective, a variable, an equation and a constraint, wherein the framework provides methods for automatically transforming the model, comprising: one or more methods for manipulating or changing a status of the entity of the model, the methods comprising a method for imposing or ignoring the constraint; and one or more methods related to operations to be applied to the entity of the model.

Abstract: A method comprising obtaining a scheduling problem comprising: a set of agents and a set of tasks to be performed by the set of agents, wherein solving the scheduling problem using an automated solver is not feasible using available predetermined resources. The method comprises automatically generating a plurality of alternative scheduling problems, wherein a solution to each such alternative scheduling problem defines a solution to the scheduling problem and determining a solution to the scheduling problem by applying the automated solver to solve, while using the available predetermined resources, an alternative problem of the plurality of alternative scheduling problems to determine a solution to the alternative problem and by mapping the solution to the alternative problem to the scheduling problem, whereby determining the solution.

Abstract: A method for processing data using snapshots is provided. The method comprises generating a primary snapshot of data stored in a first storage medium, wherein the primary snapshot is a copy of the data stored in the first storage medium at a point in time; generating one or more secondary snapshots of the primary snapshot, wherein the secondary snapshots are copies of the data stored on the first storage medium at the point in time; and extracting the data stored in the first storage medium at the point in time by accessing the data included in the secondary snapshots, wherein multiple processing units concurrently transform the extracted data and load the transformed data onto a second storage medium.

Abstract: A method for processing data using snapshots is provided. The method comprises generating a primary snapshot of data stored in a first storage medium, wherein the primary snapshot is a copy of the data stored in the first storage medium at a point in time; generating one or more secondary snapshots of the primary snapshot, wherein the secondary snapshots are copies of the data stored on the first storage medium at the point in time; and extracting the data stored in the first storage medium at the point in time by accessing the data included in the secondary snapshots, wherein multiple processing units concurrently transform the extracted data and load the transformed data onto a second storage medium.

Abstract: A method for processing data using snapshots is provided. The method comprises generating a primary snapshot of data stored in a first storage medium, wherein the primary snapshot is a copy of the data stored in the first storage medium at a point in time; generating one or more secondary snapshots of the primary snapshot, wherein the secondary snapshots are copies of the data stored on the first storage medium at the point in time; and extracting the data stored in the first storage medium at the point in time by accessing the data included in the secondary snapshots, wherein multiple processing units concurrently transform the extracted data and load the transformed data onto a second storage medium.

Abstract: Methods and systems are provided for solving an optimization problem using a model expressed in a mixed integer programming (MIP) language. The problem is constrained within a space of valid solutions by a plurality of MIP variables. A skeleton set of the variables are designated as eligible for fixed value assignments. An initial solution for the problem is obtained, which forms the basis for refinement. New versions of the problem are prepared and solved iteratively by fixing a proportion of the skeleton set to their best known values, as found in a previous problem-solving iteration.