  This paper addresses the optimization of scheduling for workers at a
logistics depot using a combination of genetic algorithm and simulated
annealing algorithm. The efficient scheduling of permanent and temporary
workers is crucial for optimizing the efficiency of the logistics depot while
minimizing labor usage. The study begins by establishing a 0-1 integer linear
programming model, with decision variables determining the scheduling of
permanent and temporary workers for each time slot on a given day. The
objective function aims to minimize person-days, while constraints ensure
fulfillment of hourly labor requirements, limit workers to one time slot per
day, cap consecutive working days for permanent workers, and maintain
non-negativity and integer constraints. The model is then solved using genetic
algorithms and simulated annealing. Results indicate that, for this problem,
genetic algorithms outperform simulated annealing in terms of solution quality.
The optimal solution reveals a minimum of 29857 person-days.
