Optimization of Order Allocation Algorithms for Industrial Internet Platforms

Abstract

Industrial Internet platforms have revolutionized traditional manufacturing ecosystems by enabling dynamic resource allocation across distributed production networks. This paper presents a comprehensive study on order allocation algorithm optimization within such platforms, addressing the critical challenge of efficiently matching customer orders with geographically dispersed manufacturing capabilities. We propose a hybrid optimization framework that combines graph neural networks for capturing complex supplier relationship patterns with multi-objective reinforcement learning for balancing competing priorities including cost minimization, delivery time adherence, and capacity utilization. The algorithm incorporates real-time production status updates, equipment availability metrics, and logistics constraints to generate allocation decisions that adapt to fluctuating demand and supply chain disruptions. Validation through large-scale simulations and a pilot implementation with a heavy equipment manufacturing consortium demonstrated a 17.3% reduction in total logistics costs, a 22.1% improvement in on-time delivery rates, and a 31.6% increase in overall equipment effectiveness compared to conventional rule-based allocation systems. The study further identifies key implementation challenges including data standardization across heterogeneous manufacturing execution systems, computational complexity in large-scale networks, and resistance to algorithmic decision-making in traditional procurement workflows. This research establishes both theoretical foundations and practical implementation guidelines for next-generation order allocation systems in industrial Internet environments, contributing to the evolution of agile, resilient manufacturing supply chains.