Latency-minimized Computation Offloading in 3C Manufacturing Workshops

With the rapid advancement and integration of Internet of Things technology into manufacturing, industrial workshops in computer, communication, and consumer electronics (3C) manufacturing are increasingly confronted with complex computational tasks during production. However, the limited hardware resources and computational capabilities of local devices often hinder efficient task execution. Computational offloading offers a viable solution by allowing complex computational tasks to be processed on either edge or cloud servers, enhancing the efficiency of computational task handling in production environments. A critical challenge lies in optimizing task offloading among local devices, edge servers, and cloud servers to maximize production efficiency while ensuring reasonable task scheduling. To address this challenge, this work proposes a flexible computational offloading strategy based on an edge-cloud architecture in a smartphone manufacturing workshop. First, a framework for edge-cloud workshop manufacturing is constructed, integrating various smartphone production devices. Based on the edge-cloud framework, a constrained optimization problem for computation offloading is formulated, using latency as the objective in industrial production settings. A time consumption model is employed to optimize computational time, and a novel scheduling strategy named Ivy-Genetic Evolution Algorithm (IGEA) is designed to solve the scheduling problem. The IGEA integrates genetic operators into the Ivy Algorithm to introduce a randomness strategy. Experimental results demonstrate that IGEA significantly outperforms state-of-the-art approaches in optimizing production efficiency.