Artificial intelligence-driven scheduling optimizers can reduce employee downtime, increase productivity, and minimize schedule-related service interruptions.
FREMONT, CA: A schedule optimization problem is the most challenging organizational problem. It is difficult to standardize these solutions because there are many variations in the types of workforces and operations, as well as across sectors and businesses.
Current spreadsheet-based workforce scheduling models take significantly longer than AI-driven solutions to schedule and need help to cope with unexpected changes in operations well. It eliminates human bias and error, creates fair scheduling schedules, and reduces the managerial bandwidth required to oversee the scheduling process through a consistent and systematic approach.
Modules should be predesigned and able to be assembled to address specific scheduling problems. Modules simplify the optimization process by breaking it down into smaller, more manageable steps.
AI-driven workforce management solves organizational challenges.
Demand and supply balancing: An integer programming problem is used to optimize the model, where the input is sub-daily demand, and the output is the required shifts. Depending on the user's settings, the module determines the number of shifts necessary to meet demand and minimize total costs.
Job-to-work-center allocation: Identifying which jobs should be distributed between work centers is crucial before shifts can be optimized, such as in call centers where calls need to be routed to different centers or in field-force operations where jobs are divided between multiple technician centers.
Heuristic dispatching: Heuristic approaches to dispatching problems are particularly useful when assigning jobs is complex. Workers might have different competency levels, or some jobs might need prioritization. Managers can apply custom rules in a particularly flexible manner with heuristic optimization. The iterative nature of this approach gives the user control over the optimal response. It is easier to manage things when there is a flexible run time and computational requirement.
The traveling-salesman problem. This module is useful to mobile workers in field service operations. The module uses priority and skill type to assign jobs to workers, so the shortest overall travel time is possible.
Crew allocation: It might be necessary to allocate a crew of workers with specific skills to a job that requires more than one worker and skill type. Skills, availability, and distance from the job's location determine which crew is needed for which job and geographically dispersed crew members make that more difficult.
