Optimization of Worker Scheduling at Logistics Depots Using Genetic Algorithms and Simulated Annealing

The efficient scheduling of permanent and temporary workers is crucial for Improving the efficiency of sortation center management optimizing the efficiency of the logistics depot while has a direct impact on the fulfillment efficiency and minimizing labor usage. The study begins by establishing operational costs of the entire logistics network. Staff a 0-1 integer linear programming model, with decision management in sortation centers is a key challenge. Staffing needs to be adjusted according to the forecasted shipment variables determining the scheduling of permanent and volume to ensure a sufficient workforce to handle the flow of temporary workers for each time slot on a given day. The goods during peak hours while avoiding the wastage of excess objective function aims to minimize person-days, while manpower during low-demand times. Staff scheduling based constraints ensure fulfillment of hourly labor on effective solution algorithms becomes one of the key requirements, limit workers to one time slot per day, cap strategies to improve the efficiency of the sorting center. By consecutive working days for permanent workers, and reasonably allocating regular and temporary workers, the maintain non-negativity and integer constraints. The sorting speed and accuracy can be improved, thus reducing the model is then solved using genetic algorithms and overall logistics cost and improving customer satisfaction.