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About Heuristic Methods for Schedules

The ABS and the Optimizer use heuristic methods to schedule appointments. These heuristic methods are included in the parameter set configuration. For more information, see Creating Parameter Sets for Schedules.

Heuristic Methods for the Appointment Booking System

To schedule appointments, the ABS can use the following heuristic methods:

• Earliest First. The earliest available time slots are selected for the set. The ABS examines each person in the schedule to find the first open slots after the date in Earliest Start field. This method can provide the fastest response for an activity. Companies that book activities for the same day prefer this method because it maximizes the amount of work service engineers perform.
• Resource Loading. Time slots are selected so that the schedules for employees are completed 1 employee at a time, to the extent possible. If multiple qualified employees are available, then the time slots for the employee with the most complete (filled-up) schedule are selected for the set. Companies primarily use this heuristic method for scheduling partners or on-call resources when they want to use as few resources as possible to meet the service needs.

  TIP:   You can configure the number of time slots returned in response to the request by using the ABS - Default Number of Slots parameter.

• Resource Leveling. Time slots are selected so that the schedules for qualified employees are evenly loaded, to the extent possible. Companies use this heuristic method when they employ salaried field service engineers who must work when scheduled.

  NOTE:  Selecting this heuristic method does not mean that the time slots for all employees in the schedule appear. Multiple slots from the same employee might appear if that employee is more lightly loaded.

Heuristic Methods for the Optimizer

The Optimizer provides a choice of methods to obtain solutions for a service region schedule. Each of these methods involves a different strategy to improve the schedule. All methods use operations to move appointments and activities in a schedule and then verify improvements in the overall cost of the schedule. For example, the Optimizer might try to swap a pair of activities between two field service engineers. This swap can change the cost of the schedule by changing the cost of travel, the amount and cost of overtime, and the rate that the field engineer bills. Any reduction in cost is an improvement in the schedule. For more information, see Defining Cost Functions for the Optimizer.

The Optimizer can use 2 basic optimization methods alone. The Optimizer can use 3 additional methods in combination with the basic methods. The following basic methods consistently search for and accept only improvements (lower costs) in the schedule:

• Greedy search. This method starts with an existing schedule, finds the first move that improves the schedule, accepts the move, and then uses this solution to find the next improvement. A Greedy search repeats this process until there are no more opportunities for improvement or until a time limit is reached. This method is relatively fast, but the resulting solution is not as good as other methods.
• Steepest search. This method starts with an existing schedule, tries all moves, accepts the move that provides the greatest improvement in the schedule, and then uses this solution to find the next improvement. A Steepest search repeats this process until there are no more opportunities for improvement or until a time limit is reached. This method takes longer, but generally produces lower-cost schedules.

Additional methods, combined with the Greedy search or the Steepest search, allow moves that temporarily increase the cost of a schedule to arrive at significant, overall improvements in the schedule. In all cases, the Greedy search or the Steepest search quickly finds an improved schedule, and then one of the following methods searches for improvements:

• Tabu search. This method accepts the next best solution even if it is not an improvement over the previous schedule. This method keeps a tabu list of finite length that contains the results of previous moves. The Optimizer cannot repeat a move until the move is no longer on the list.
• Fast Guided Local search. This method adjusts the cost of a solution to reflect the number of times the Optimizer tried a move so that the Optimizer can try a wider range of changes.
• Fast Guided Tabu search. This method combines the Tabu search and the Fast Guided Local search. This method often finds good solutions faster than the Tabu or Fast Guided Local search.