A Mathematical Minimax Optimization Framework for AI-Driven Strategic Decision-Making in Human Resource Management, Marketing, and Finance under Uncertainty
Keywords:
Minimax Optimization, Robust Decision-Making, Strategic Resource Allocation, Artificial Intelligence, Mathematical Programming, Uncertainty Analysis.Abstract
Understanding of allocating strategy resources under uncertainty is an enormous challenge for companies trying to manage their human resource management (HRM), marketing and financial resources. Deterministic methods can often not meet the challenge of uncertain operation conditions and conflicting departmental requirements. This work proposes a mathematical minimax optimization framework for resource allocation in a strategic context with uncertainty. The proposed model uses a normalized budget of 100, three decision variables of departments, and five levels of uncertainty (0% - 20%). Strong minimax optimization is used to minimize the maximum loss to any department; given a set budget. From the analytical results optimal distribution of 40.00% for HRM, 33.33% for marketing and 26.67% for finance. The worst case organizational loss is 0.900, or 16.67% and 25.00% less than equal and fixed allocation strategies respectively. These results illustrate that minimax optimization can mathematically handle the problem of making sound strategic decisions in an uncertain world.
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