Optimization of Worker Scheduling at Logistics Depots Using Genetic Algorithms and Simulated Annealing
This is an incremental improvement for logistics depot managers seeking to reduce labor costs through more efficient scheduling.
This paper tackled the problem of optimizing worker scheduling at a logistics depot to minimize labor usage, achieving a minimum of 29,857 person-days using a combination of genetic algorithms and simulated annealing, with genetic algorithms performing better.
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.