Demand Prediction and Allocation Optimization of Manufacturing Resources
Jingjing Meng
Abstract
Big data analysis, Internet technology, and cloud computing are being integrated to the allocation of production and manufacturing (P-M) resources, promoting the transformation, upgrading, and innovative development of traditional job-shop P-M resource allocation. However, the existing studies have not fully considered the demand dynamicity of production materials. To solve the problem, this paper attempts to predict the demand and optimize the allocation of job-shop P-M resources. Firstly, a demand prediction model was established for job-shop P-M resources, which can simultaneously capture the static and dynamic spatial dependence of P-M resource volume. Based on the demand prediction, the authors detailed an allocation optimization strategy for job-shop P-M resources, and defined the objective function and constraints. The proposed model was proved effective through experiments.