Integrating Genetic Algorithms with Neural Networks for Optimizing Worker Scheduling in Logistics Depots

Abstract

Optimizing worker scheduling in logistics depots poses a complex combinatorial optimization challenge due to diverse tasks, varying worker skills, and fluctuating demand patterns. Traditional scheduling methods often struggle to efficiently allocate human resources amidst these dynamic conditions. In this study, we propose a novel approach that integrates Genetic Algorithms (GAs) with Neural Networks (NNs) to tackle the intricacies of worker scheduling in logistics depots. The proposed framework harnesses the strengths of GAs in exploring large solution spaces and NNs in learning complex patterns from data. Initially, a population of candidate schedules is generated using a GA, representing potential assignments of workers to tasks over time. Through iterative evolution, the GA refines these schedules by applying genetic operators such as crossover and mutation, guided by the fitness function that evaluates schedule quality based on criteria such as task completion time and worker preferences. To enhance the GA's performance and adaptability, we employ NNs to dynamically adjust parameters and encoding schemes based on historical data and real-time inputs. The NN learns from past scheduling instances and continuously updates the GA's search strategy, enabling it to adapt to evolving operational conditions and preferences. Our study highlights the effectiveness of combining Genetic Algorithms with Neural Networks for optimizing worker scheduling in logistics depots. This hybrid approach offers a flexible and scalable solution to address the complex scheduling challenges faced by modern logistics operations, paving the way for enhanced efficiency and productivity in the industry.