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'''Workforce modeling''' is the process of aligning the demand for skilled labor with the availability and preferences of workers. It utilizes mathematical models to perform tasks such as sensitivity analysis, scheduling, and workload forecasting.
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==Definition==
The term can be differentiated from traditional staff [[Schedule (workplace)|scheduling]].<ref>{{Cite journal |last=Ernst |first=A. T |last2=Jiang |first2=H |last3=Krishnamoorthy |first3=M |last4=Sier |first4=D |date=2004-02-16 |title=Staff scheduling and rostering: A review of applications, methods and models |url=https://www.sciencedirect.com/science/article/pii/S037722170300095X |journal=European Journal of Operational Research |series=Timetabling and Rostering |volume=153 |issue=1 |pages=3–27 |doi=10.1016/S0377-2217(03)00095-X |issn=0377-2217|url-access=subscription }}</ref> Research indicates that traditional static planning models result in 60% of operating hours being either understaffed, or overstaffed, while modern workforce modeling implementations have achieved substantial cost reductions.<ref name=":1">{{Cite web |title=AI workforce planning for travel and logistics {{!}} McKinsey |url=https://www.mckinsey.com/industries/travel/our-insights/ai-can-transform-workforce-planning-for-travel-and-logistics-companies |access-date=2025-06-24 |website=www.mckinsey.com}}</ref> Staff scheduling is rooted in [[time management]].<ref>{{Cite journal |last=Pinedo |first=Michael L. |date=2022 |title=Scheduling |url=https://link.springer.com/book/10.1007/978-3-031-05921-6 |journal=SpringerLink |language=en |doi=10.1007/978-3-031-05921-6|url-access=subscription }}</ref> Besides demand orientation, workforce modeling also incorporates the forecast of the workload and the required staff, the integration of workers into the scheduling process through interactivity, and analysis of the entire process.<ref>{{Cite journal |last=Algethami |first=Haneen |last2=Martínez-Gavara |first2=Anna |last3=Landa-Silva |first3=Dario |date=2019-10-01 |title=Adaptive multiple crossover genetic algorithm to solve workforce scheduling and routing problem |url=https://doi.org/10.1007/s10732-018-9385-x |journal=Journal of Heuristics |language=en |volume=25 |issue=4 |pages=753–792 |doi=10.1007/s10732-018-9385-x |issn=1572-9397}}</ref> The evolution from traditional scheduling to workforce modeling demonstrated quantitative benefits and reflects broader technological advancement in organizational management.<ref name=":1" />
==Complexity of the model==
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