Production Logistics Management Research Articles

Optimizing production logistics through advanced machine learning techniques: A study on resource allocation for small-batch and multi-variety challenges

In light of increasing consumer purchasing power and the growing diversification of demands, the emergence of small-batch and multi-variety requirements has posed substantial challenges to production manufacturing, particularly in on-site production logistics management. This study aims to address the issues of effective material flow and resource allocation in production logistics management by investigating a method for the rapid and accurate allocation of supply logistics resources. Leveraging the extensive logistics resource allocation data available in the Plan For Every Part system, this research utilizes the Term Frequency-Inverse Document Frequency algorithm to filter out less discriminative data. Additionally, a recommender system based on machine learning, specifically the Recsys (Recommender System) employing similarity algorithms, is employed to construct a logistics resource allocation recommendation model. The effectiveness of this approach is validated through a case study that involves material resource allocation recommendations in a specific remote control production workshop. The validation results indicate that, compared to traditional logistics resource allocation processes, the proposed model optimizes resource allocation recommendations in production logistics, resulting in a significant improvement in material delivery punctuality and overall operational efficiency in enterprises.

Production Logistics Management of Industrial Enterprises Based on Wavelet Neural Network

With an efficient production logistics system, intelligent manufacturers can reduce the investment in production, improve the stability and self-repair ability of production logistics, and strike a perfect balance between production scheduling and production logistics. This paper probes deep into the production logistics management (PLM) of industrial enterprises, and proposes a PLM model for such enterprises based on wavelet neural network (WNN). Firstly, the PLM system architecture of industrial enterprises was established, and the scheduling and task allocation principles were proposed for the collaboration of various subjects in the system. Based on curved time window, a multi-objective path planning and optimization model was established, under influencing factors like the dynamics of station demand and the maximum driving range of handling equipment. Simulation results show that the proposed model is effective in optimizing the path for industrial production logistics. The research results provide theoretical supports to the real-time optimization of PLM and rationalization of production scheduling in industrial enterprises.