Labor allocation in warehouse distribution centers is a persistent operational challenge: new orders arrive continuously throughout a shift, worker availability fluctuates, and even small mismatches between labor supply and demand result in delayed fulfillment, overtime costs, and missed customer commitments. This capstone presents a mixed integer linear programming (MILP) model designed to align same-day task demand with available labor supply across the six functional departments of a Wesco distribution center. Given the tasks currently on the warehouse floor and the workers available, the model determines the optimal number of workers to allocate to each department by optimizing six hierarchical objectives: completing urgent, same-day tasks before deferrable ones, ordering remaining work by priority code, customer due date, and time waiting on the warehouse floor, and minimizing total labor cost only after task urgency is fully resolved. The model was validated at two scales: a small-scale set confirmed that all constraints and objective logic behaved as designed, and a full-scale set of 41,124 tasks solved 452,484 decision variables across 327,334 constraints in under two seconds. At full scale, the model successfully allocated available workers across all six departments, completing 26,702 same-day tasks with only 12.4 total overtime hours required. Beyond same-day allocation, the model identifies forward departmental staffing needs as the known backlog drains over the planning horizon, giving managers a baseline for the days ahead. In all cases, the model recommends how many workers each department needs rather than prescribing individual worker assignments, preserving manager discretion over who moves and where. More broadly, this model givesWesco’s warehouse managers a consistent, data-driven tool to allocate labor by department rather than by intuition alone.
