Abstract: A computer implemented method for agent scheduling using a Mixed Integer Programming (MILP) model, and a Mixed Integer Programming and Search Heuristic (MILP&SH) algorithm. The MILP model of the invention comprises of an objective function, decision variables, and constraints expressing tour group and shift template rules and required staffing levels as equations and inequalities. The MILP&SH algorithm comprises of an MILP solution algorithm and a search heuristic. The MILP solution algorithm applied to the MILP model to generate a plurality of solutions. If a solution generated by the MILP solution algorithm is an integer feasible solution, the search heuristic generates agent schedules. If a solution is not an integer feasible solution, the steps of the search heuristic are executed to find an integer feasible solution. The search heuristic compares the objective function value for an integer feasible solution found with the objective value for the best solution.

Abstract: A system for scheduling servers is provided. The system receives a scheduling period, staffing requirements in planning intervals during the scheduling period, skill groups with one or more servers in each skill group, location, tour group, shift templates and associated scheduling rules for each server to be scheduled. The system generates a Mixed Integer Linear Programming model using this information. The system continues searching feasible solutions to the Mixed Linear Integer Programming model until one or more stopping criteria are satisfied by a terminal solution. The system generates detailed server schedules using the terminal solution to the Mixed Integer Linear Programming model.

Abstract: An invention for scheduling employees in a skills-based contact routing environment in which each employee has one or more skills and belongs to a skill group. The invention includes an initialization step and an iterative step. In the initialization step, the invention generates initial staffing levels and initial agent schedules for each skill group. In the iterative step, a skill group is selected and unscheduled while keeping agents in other skill groups on their current schedules, agent requirements for the skill group selected are updated using a staffing model that applies contact routing rules and includes the number of agents scheduled for work in other skill groups, and agents are rescheduled in the selected skill group. In each iteration each skill group is unscheduled, agent requirements are updated, and agents are rescheduled once. Iterations continue until one or more stopping criteria are satisfied.

Abstract: The present invention provides a method for staffing and scheduling agents for revenue and service channels in a skills-based routing environment. Initially, the invention receives forecasts of number of contacts, average handling times, agent shrinkage and sales ratios for a planning period from a forecasting system. Next the initial required staffing levels for different skill groups are determined and agents are scheduled. In the iterative step, a skill group is selected and unscheduled while keeping agents in other skill groups on their current schedules, staffing levels for the skill group selected is updated using a staffing model that applies contact routing rules and includes the number of agents scheduled for work in other skill groups, and reschedules agents in the skill group selected. The invention continues iterations for each skill group until one or more stopping criteria are satisfied.

Abstract: The present invention provides a method and system for integrated supply chain and contact center planning. First, the system and method of the invention obtains forecasts of contacts expected, average handling times, agent shrinkage and sales ratios with and without backorders over a planning period. The method and system of the invention next schedules agents to minimize lost sales, agent, facility, and communication costs for contact groups used by customers to order products, and to best meet productivity-based targets for contact groups used for services not for sale. Using the scheduled staffing levels, and forecasts of contact volumes, average handling times, agent shrinkage, sales ratios with and without backorders, supply chain product availability, and a staffing model, the method and system of the invention generates product sales forecasts for products sold.

Abstract: A computer implemented system and methods for forecasting the future values of a time series with multiple seasonal cycles are described. The forecasting method of the present invention include 1) two multi-seasonal exponential smoothing methods, 2) the seasonal sub-series method splitting a time series into multiple sub-series, and 3) the aggregate-to-period forecasting method.

Abstract: The present invention relates to a method for workforce scheduling in which workload and workload types vary during scheduling period. The method acquires agent and skill requirements for all periods and contact types; acquires the contact center information including agent skill groups, agent work groups, tour and shift scheduling rules, agent availability, objective criterion to be optimized and its parameters; develops a Mixed Integer Linear Programming (MILP) model for the scheduling environment; applied an optimization algorithm that uses the Branch and Bound algorithm with a Rounding Algorithm to improve performance; and locates a globally optimal or near optimal workforce schedule in total cost or paid time or agent satisfaction. Detailed schedules may be developed by assigning daily shifts to work patterns, and breaks scheduled to daily shifts.

Abstract: An Order Based Materials Management (OBMM) method using forecasts of actual customer orders to determine component stocking levels. There may be several types of orders and several types of customer demand forecasts for them. Each order type is specified by a variable Bill Of Materials called an “order profile”—a multivariate probability distribution with each random variable representing the uncertain number of units of a component or subassembly that may be needed to build a customer order of that type. The profile of an order type is represented by a multivariate probability distribution of all components/subassemblies that may be needed for an order of that type, including the correlation between the numbers of units of different components needed for an order in the analysis.