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.

翻译：本文采用遗传算法与模拟退火算法相结合的方法，研究物流仓库的工人调度优化问题。针对高效调度正式工与临时工以在优化仓库效率的同时最小化劳动力使用这一核心目标，研究首先构建了0-1整数线性规划模型，决策变量为每日各时段正式工与临时工的调度安排。目标函数旨在最小化人·天数，约束条件涵盖每小时劳动需求的满足、每位工人每日仅安排一个时段、正式工连续工作天数上限，以及非负整数约束。随后采用遗传算法与模拟退火算法对模型进行求解。结果表明，针对该问题，遗传算法在解质量方面优于模拟退火算法。优化解得出的最小人·天数为29,857。