Human Resource Planning

Optimization technology for human resource planning

The optimization technology from OptTek fills the gap that exists in other systems currently being used in career planning, and gives the decision maker support when faced with the need to assess alternative courses of action. This technology enables the policy decision maker to specify a variety of important relationships to control the determination of optimal policies. It then determines the strategic options that are investigated under its guidance, and which it successively passes to the simulation package for evaluation. The resulting search isolates scenarios that yield the highest quality outcomes for readiness, costs and risks, according to the criteria selected by the decision maker. Without the benefit of this search and analysis capability, the decision maker must contend with trying out different variations blindly -- with little hope of finding a competitive outcome from the vast number of possible decision combinations that arise in real world applications (even when the number of decision parameters is small).

OptTek integrates the optimization technology developed with a simulation platform to deliver a total solution to analyze a variety of planning policy options that meet manpower readiness levels. This total solution enables the user to examine alternative career plans for sailors and trade-offs between recruitment and training policies to arrive at desired skill levels at multiple locations based on achieving readiness requirements.

When fully integrated with the simulation, the total solution will deliver an analysis of how manpower and personnel policies impact readiness and costs. It will identify the best portfolio of policy decisions that allocate assignments based on skill requirements at locations to meet readiness requirements at minimum cost. The policy decision maker can modify assumptions and constraints “on the fly” (examine alternative results in a matter of minutes) and review the impact on performance on their monitors or “computer dashboards.”

The methods underlying the optimization technology are based on advances in the field of metaheuristics- the domain of optimization that incorporates artificial intelligence and analogs to physical, biological or evolutionary processes, that have led to the creation of a new approach that successfully integrates simulation and optimization. This innovation has been embedded in the OptQuest® Engine,. which provides the core optimization technology. The availability of this new system opens the door to handling policy decision-making problems in career planning that could not be adequately solved in the past.

This technology replaces the inaccuracy of trial-and-error, which has been the only way previously available to search for effective options using simulation, or other computer based models for evaluating proposed solutions, with a potent search engine that can pinpoint the best decisions that fall within the domain that the simulation or other evaluation model encompasses. Standard simulation packages give the decision- maker no help in identifying good alternatives to evaluate. More importantly, they offer no guidance or insight into the nature of alternatives that can yield the best decisions. To illustrate, the intelligent user of simulation and other business or industry evaluation models may want to know:

  • What are the best policies for recruitment and training?
  • What are the most cost effective deployment schedules?
  • What is the best workforce assignment allocation?
  • What is the best investment portfolio for the recruiting and training budgets?

The answers to such questions require a painstaking examination of multiple scenarios, where each scenario in turn requires the implementation of an appropriate simulation or evaluation model to determine the consequences for costs, profits and risks. The critical "missing component" is to disclose which decision scenarios are the ones that should be investigated -- and still more completely, to identify good scenarios automatically by a search process designed to find the best set of decisions. Traditional simulation and evaluation packages provide no means of fulfilling this critical function.